Technologies like Apache Kafka and Apache Flink are at the heart of this transformation. They allow logistics companies to capture, process, and act on streaming data as it’s generated—from vehicle sensors and telematics systems to inventory platforms and customer applications. This enables new use cases in predictive maintenance, live fleet tracking, customer service automation, and much more.

A growing number of companies across the supply chain are embracing this model. Whether it’s real-time shipment tracking, automated compliance reporting, or AI-driven optimization, the ability to stream, process, and route data instantly is proving vital.

One standout example is Penske Logistics—a transportation leader using Confluent’s data streaming platform (DSP) to transform how it operates and delivers value to customers.

Join the data streaming community and stay informed about new blog posts by subscribing to my newsletter and follow me on LinkedIn or X (former Twitter) to stay in touch. And make sure to download my free book about data streaming use cases.

Why Real-Time Data Matters in Logistics and Transportation

Transportation and logistics operate on tight margins and stricter timelines than almost any other sector. Delays ripple through supply chains, disrupting manufacturing schedules, customer deliveries, and retail inventories. Traditional data integration methods—batch ETL, manual syncing, and siloed systems—simply can’t meet the demands of today’s global logistics networks.

Data streaming enables organizations in the logistics and transportation industry to ingest and process information in real-time while the data is valuable and critical. Vehicle diagnostics, route updates, inventory changes, and customer interactions can all be captured and acted upon in real time. This leads to faster decisions, more responsive services, and smarter operations.

Real-time data also lays the foundation for advanced use cases in automation and AI, where outcomes depend on immediate context and up-to-date information. And for logistics providers, it unlocks a powerful competitive edge.

How Data Streaming with Apache Kafka and Flink Transforms the Supply Chain

Apache Kafka serves as the backbone for real-time messaging—connecting thousands of data producers and consumers across enterprise systems. Apache Flink adds stateful stream processing to the mix, enabling continuous pattern recognition, enrichment, and complex business logic in real time.

In the logistics industry, this event-driven architecture supports use cases such as:

• Continuous monitoring of vehicle health and sensor data
• Proactive maintenance scheduling
• Real-time fleet tracking and route optimization
• Integration of telematics, ERP, WMS, and customer systems
• Instant alerts for service delays or disruptions
• Predictive analytics for capacity and demand forecasting

This isn’t just theory. Leading logistics organizations are deploying these capabilities at scale.

Data Streaming Success Stories Across the Logistics and Transportation Industry

Many transportation and logistics firms are already using Kafka-based architectures to modernize their operations. A few examples:

• LKW Walter relies on data streaming to optimize its full truck load (FTL) freight exchanges and enable digital freight matching.
• Uber Freight leverages real-time telematics, pricing models, and dynamic load assignment across its digital logistics platform.
• Instacart uses event-driven systems to coordinate live order delivery, matching customer demand with available delivery slots.
• Maersk incorporates streaming data from containers and ports to enhance shipping visibility and supply chain planning.

These examples show the diversity of value that real-time data brings—across first mile, middle mile, and last mile operations.

An increasing number of companies are using data streaming as the event-driven control tower for their supply chains. It’s not only about real-time insights—it’s also about ensuring consistent data across real-time messaging, HTTP APIs, and batch systems. Learn more in this article: A Real-Time Supply Chain Control Tower powered by Kafka.

Penske Logistics: A Leader in Transportation, Fleet Services, and Supply Chain Innovation

Penske Transportation Solutions is one of North America’s most recognizable logistics brands. It provides commercial truck leasing, rental, and fleet maintenance services, operating a fleet of over 400,000 vehicles. Its logistics arm offers freight management, supply chain optimization, and warehousing for enterprise customers.

But Penske is more than a fleet and logistics company. It’s a data-driven operation where technology plays a central role in service delivery. From vehicle telematics to customer support, Penske is leveraging data streaming and AI to meet growing demands for reliability, transparency, and speed.

Penske’s Data Streaming Success Story

Penske explored its data streaming journey at the Confluent Data in Motion Tour. Sarvant Singh, Vice President of Data and Emerging Solutions at Penske, explains the company’s motivation clearly: “We’re an information-intense business. A lot of information is getting exchanged between our customers, associates, and partners. In our business, vehicle uptime and supply chain visibility are critical.”

This focus on uptime is what drove Penske to adopt a real-time data streaming platform, powered by Confluent. Today, Penske ingests and processes around 190 million IoT messages every day from its vehicles.

Each truck contains hundreds of sensors (and thousands of sub-sensors) that monitor everything from engine performance to braking systems. With this volume of data, traditional architectures fell short. Penske turned to Confluent Cloud to leverage Apache Kafka at scale as a fully-managed, elastic SaaS to eliminate the operational burden and unlocking true real-time capabilities.

By streaming sensor data through Confluent and into a proactive diagnostics engine, Penske can now predict when a vehicle may fail—before the problem arises. Maintenance can be scheduled in advance, roadside breakdowns avoided, and customer deliveries kept on track.

This approach has already prevented over 90,000 potential roadside incidents. The business impact is enormous, saving time, money, and reputation.

Other real-time use cases include:

• Diagnosing issues instantly to dispatch roadside assistance faster
• Triggering preventive maintenance alerts to avoid unscheduled downtime
• Automating compliance for IFTA reporting using telematics data
• Streamlining repair workflows through integration with electronic DVIRs (Driver Vehicle Inspection Reports)

Why Confluent for Apache Kafka?

Managing Kafka in-house was never the goal for Penske. After initially working with a different provider, they transitioned to Confluent Cloud to avoid the complexity and cost of maintaining open-source Kafka themselves.

“We’re not going to put mission-critical applications on an open source tech,” Singh noted. “Enterprise-grade applications require enterprise level support—and Confluent’s business value has been clear.”

Key reasons for choosing Confluent include:

• The ability to scale rapidly without manual rebalancing
• Enterprise tooling, including stream governance and connectors
• Seamless integration with AI and analytics engines
• Reduced time to market and improved uptime

Data Streaming and AI in Action at Penske

Penske’s investment in AI began in 2015, long before it became a mainstream trend. Early use cases included Erica, a virtual assistant that helps customers manage vehicle reservations. Today, AI is being used to reduce repair times, predict failures, and improve customer service experiences.

By combining real-time data with machine learning, Penske can offer more reliable services and automate decisions that previously required human intervention. AI-enabled diagnostics, proactive maintenance, and conversational assistants are already delivering measurable benefits.

The company is also exploring the role of generative AI. Singh highlighted the potential of technologies like ChatGPT for enterprise applications—but also stressed the importance of controls: “Configuration for risk tolerance is going to be the key. Traceability, explainability, and anomaly detection must be built in.”

Fleet Intelligence in Action: Measurable Business Value Through Data Streaming

For a company operating hundreds of thousands of vehicles, the stakes are high. Penske’s real-time architecture has improved uptime, accelerated response times, and empowered technicians and drivers with better tools.

The business outcomes are clear:

• Fewer breakdowns and delays
• Faster resolution of vehicle issues
• Streamlined operations and reporting
• Better customer and driver experience
• Scalable infrastructure for new services, including electric vehicle fleets

With 165,000 vehicles already connected to Confluent and more being added as EV adoption grows, Penske is just getting started.

The Road Ahead: Agentic AI and the Next Evolution of Event-Driven Architecture Powered By Apache Kafka

The future of logistics will be defined by intelligent, real-time systems that coordinate not just vehicles, but entire networks. As Penske scales its edge computing and expands its use of remote sensing and autonomous technologies, the role of data streaming will only increase.

Agentic AI—systems that act autonomously based on real-time context—will require seamless integration of telematics, edge analytics, and cloud intelligence. This demands a resilient, flexible event-driven foundation. I explored the general idea in a dedicated article: How Apache Kafka and Flink Power Event-Driven Agentic AI in Real Time.

Penske’s journey shows that real-time data streaming is not only possible—it’s practical, scalable, and deeply transformative. The combination of a data streaming platform, sensor analytics, and AI allows the company to turn every vehicle into a smart, connected node in a global supply chain.

For logistics providers seeking to modernize, the path is clear. It starts with streaming data—and the possibilities grow from there. Join the data streaming community and stay informed about new blog posts by subscribing to my newsletter and follow me on LinkedIn or X (former Twitter) to stay in touch. And make sure to download my free book about data streaming use cases.

The post How Penske Logistics Transforms Fleet Intelligence with Data Streaming and AI appeared first on Kai Waehner.

Join the data streaming community and stay informed about new blog posts by subscribing to my newsletter and follow me on LinkedIn or X (former Twitter) to stay in touch. And download my free book about data streaming use cases and business value, including customer stories across all industries.

From Event Hype to Event Infrastructure

Over the last decade, Apache Kafka has evolved from a scalable log for engineers at LinkedIn to the de facto event streaming platform adopted across every industry. Banks, automakers, telcos, logistics firms, and retailers alike rely on Kafka as the nervous system for critical data.

Today, over 150,000 organizations globally use Apache Kafka to enable real-time operations, modernize legacy systems, and support digital innovation. Kafka 4.0 moves even deeper into this role as a business-critical backbone. If you want to learn more about use case and industry success stories, download my free ebook and subscribe to my newsletter.

Version 4.0 of Apache Kafka signals readiness for CIOs, CTOs, and enterprise architects who demand:

• Uninterrupted uptime and failover for global operations
• Data-driven automation and decision-making at scale
• Flexible deployment across on-premises, cloud, and edge environments
• A future-proof foundation for modernization and innovation

Apache Kafka 4.0 doesn’t just scale throughput—it scales business outcomes:

This post does not cover the technical improvements and new features of the 4.0 release, like ZooKeeper removal, Queues for Kafka, and so on. Those are well-documented elsewhere. Instead, it highlights the strategic business value Kafka 4.0 delivers to modern enterprises.

Kafka 4.0: A Platform Built for Growth

Today’s IT leaders are not just looking at throughput and latency. They are investing in platforms that align with long-term architectural goals and unlock value across the organization.

Apache Kafka 4.0 offers four core advantages for business growth:

1. Open De Facto Standard for Data Streaming

Apache Kafka is the open, vendor-neutral protocol that has become the de facto standard for data streaming across industries. Its wide adoption and strong community ecosystem make it both a reliable choice and a flexible one.

Organizations can choose between open-source Kafka distributions, managed services like Confluent Cloud, or even build their own custom engines using Kafka’s open protocol. This openness enables strategic independence and long-term adaptability—critical factors for any enterprise architect planning a future-proof data infrastructure.

2. Operational Efficiency at Enterprise Scale

Reliability, resilience, and ease of operation are key to any business infrastructure. Kafka 4.0 reduces operational complexity and increases uptime through a simplified architecture. Key components of the platform have been re-engineered to streamline deployment and reduce points of failure, minimizing the effort required to keep systems running smoothly.

Kafka is now easier to manage, scale, and secure—whether deployed in the cloud, on-premises, or at the edge in environments like factories or retail locations. It reduces the need for lengthy maintenance windows, accelerates troubleshooting, and makes system upgrades far less disruptive. As a result, teams can operate with greater efficiency, allowing leaner teams to support larger, more complex workloads with greater confidence and stability.

Storage management has also evolved in the past releases by decoupling compute and storage. This optimization allows organizations to retain large volumes of event data cost-effectively without compromising performance. This extends Kafka’s role from a real-time pipeline to a durable system of record that supports both immediate and long-term data needs.

With fewer manual interventions, less custom integration, and more built-in intelligence, Kafka 4.0 allows engineering teams to focus on delivering new services and capabilities—rather than maintaining infrastructure. This operational maturity translates directly into faster time-to-value and lower total cost of ownership at enterprise scale.

3. Innovation Enablement Through Real-Time Data

Real-time data unlocks entirely new business models: predictive maintenance in manufacturing, personalized digital experiences in retail, and fraud detection in financial services. Kafka 4.0 empowers teams to build applications around streams of events, driving automation and responsiveness across the value chain.

This shift is not just technical—it’s organizational. Kafka decouples producers and consumers of data, enabling individual teams to innovate independently without being held back by rigid system dependencies or central coordination. Whether building with Java, Python, Go, or integrating with SaaS platforms and cloud-native services, teams can choose the tools and technologies that best fit their goals.

This architectural flexibility accelerates development cycles and reduces cross-team friction. As a result, new features and services reach the market faster, experimentation is easier, and the overall organization becomes more agile in responding to customer needs and competitive pressures. Kafka 4.0 turns real-time architecture into a strategic asset for business acceleration.

4. Cloud-Native Flexibility

Kafka 4.0 reinforces Kafka’s role as the backbone of hybrid and multi-cloud strategies. In a data streaming landscape that spans public cloud, private infrastructure, and on-premise environments, Kafka provides the consistency, portability, and control that modern organizations require.

Whether deployed in AWS, Azure, GCP, or edge locations like factories or retail stores, Kafka delivers uniform performance, API compatibility, and integration capabilities. This ensures operational continuity across regions, satisfies data sovereignty and regulatory needs, and reduces latency by keeping data processing close to where it’s generated.

Beyond Kafka brokers, it is the Kafka protocol itself that has become the standard for real-time data streaming—adopted by vendors, platforms, and developers alike. This protocol standardization gives organizations the freedom to integrate with a growing ecosystem of tools, services, and managed offerings that speak Kafka natively, regardless of the underlying engine.

For instance, innovative data streaming platforms built using the Kafka protocol, such as WarpStream, provide a Bring Your Own Cloud (BYOC) model to allow organizations to maintain full control over their data and infrastructure while still benefiting from managed services and platform automation. This flexibility is especially valuable in regulated industries and globally distributed enterprises, where cloud neutrality and deployment independence are strategic priorities.

Kafka 4.0 not only supports cloud-native operations—it strengthens the organization’s ability to evolve, modernize, and scale without vendor lock-in or architectural compromise.

Real-Time as a Business Imperative

Data is no longer static. It is dynamic, fast-moving, and continuous. Businesses that treat data as something to collect and analyze later will fall behind. Kafka enables a shift from data at rest to data in motion.

Kafka 4.0 supports this transformation across all industries. For instance:

• Automotive: Streaming data from factories, fleets, and connected vehicles
• Banking: Real-time fraud detection and transaction analytics
• Telecom: Customer engagement, network monitoring, and monetization
• Healthcare: Monitoring devices, alerts, and compliance tracking
• Retail: Dynamic pricing, inventory tracking, and personalized offers

These use cases cannot be solved by daily batch jobs. Kafka 4.0 enables systems—and decision-making—to operate at business speed. “The Top 20 Problems with Batch Processing (and How to Fix Them with Data Streaming)” explore this in more detail.

Additionally, Apache Kafka ensures data consistency across real-time streams, batch processes, and request-response APIs—because not all workloads are real-time, and that’s okay.

The Kafka Ecosystem and the Data Streaming Landscape

Running Apache Kafka at enterprise scale requires more than open-source software. Kafka has become the de facto standard for data streaming, but success with Kafka depends on using more than just the core project. Real-time applications demand capabilities like data integration, stream processing, governance, security, and 24/7 operational support.

Today, a rich and rapidly developing data streaming ecosystem has emerged. Organizations can choose from a growing number of platforms and cloud services built on or compatible with the Kafka protocol—ranging from self-managed infrastructure to Bring Your Own Cloud (BYOC) models and fully managed SaaS offerings. These solutions aim to simplify operations, accelerate time-to-market, and reduce risk while maintaining the flexibility and openness that Kafka is known for.

Confluent leads this category as the most complete data streaming platform, but it is part of a broader ecosystem that includes vendors like Amazon MSK, Cloudera, Azure Event Hubs, and emerging players in cloud-native and BYOC deployments. The data streaming landscape explores all the different vendors in this software category:

The market is moving toward complete data streaming platforms (DSP)—offering end-to-end capabilities from ingestion to stream processing and governance. Choosing the right solution means evaluating not only performance and compatibility but also how well the platform aligns with your business strategy, security requirements, and deployment preferences.

Kafka is at the center—but the future of data streaming belongs to platforms that turn Kafka 4.0’s architecture into real business value.

The Road Ahead with Apache Kafka 4.0 and Beyond

Apache Kafka 4.0 is a strategic enabler responsible for driving modernization, innovation, and resilience. It directly supports the key transformation goals:

• Modernization without disruption: Kafka integrates seamlessly with legacy systems and provides a bridge to cloud-native, event-driven architectures.
• Platform standardization: Kafka becomes a central nervous system across departments and business units, reducing fragmentation and enabling shared services.
• Faster ROI from digital initiatives: Kafka accelerates the launch and evolution of digital services, helping teams iterate and deliver measurable value quickly.

Kafka 4.0 reduces operational complexity, unlocks developer productivity, and allows organizations to respond in real time to both opportunities and risks. This release marks a significant milestone in the evolution of real-time business architecture.

Kafka is no longer an emerging technology—it is a reliable foundation for companies that treat data as a continuous, strategic asset. Data streaming is now as foundational as databases and APIs. With Kafka 4.0, organizations can build connected products, automate operations, and reinvent the customer experience easier than ever before.

And with innovations on the horizon—such as built-in queueing capabilities, brokerless writes directly to object storage, and expanded transactional guarantees supporting the two-phase commit protocol (2PC)—Kafka continues to push the boundaries of what’s possible in real-time, event-driven architecture.

The future of digital business is real-time. Apache Kafka 4.0 is ready.

Want to learn more about Kafka in the enterprise? Let’s connect and exchange ideas. Subscribe to the Data Streaming Newsletter. Explore the Kafka Use Case Book for real-world stories from industry leaders.

The post Apache Kafka 4.0: The Business Case for Scaling Data Streaming Enterprise-Wide appeared first on Kai Waehner.

Join the data streaming community and stay informed about new blog posts by subscribing to my newsletter and follow me on LinkedIn or X (former Twitter) to stay in touch. And download my free book about data streaming use cases, including various use cases from the retail industry.

What is Retail Media?

Retail media is transforming how brands advertise by leveraging first-party data from retailers to create highly targeted ads within their ecosystems. Instead of relying solely on third-party data from traditional digital advertising platforms, retail media allows companies to reach consumers at the point of purchase—whether online, in-store, or via mobile apps.

Retail media is one of the fastest-growing and most strategic revenue streams for retailers today. It has transformed from a niche digital advertising concept into a multi-billion-dollar industry, changing how retailers monetize their data and engage with brands. Below are the key reasons retail media is crucial for retailers.

Retailers like Amazon, Walmart, and Albertsons are leading the way in monetizing their digital real estate, offering brands access to sponsored product placements, banner ads, video ads, and personalized promotions based on shopping behavior. This shift has made retail media one of the fastest-growing sectors in digital advertising, expected to exceed $100 billion globally in the coming years.

The Digitalization of Retail Media

Retail media has grown from traditional in-store promotions to a fully digitized, data-driven advertising ecosystem. The rise of e-commerce, mobile apps, and connected devices has enabled retailers to:

• Collect granular consumer behavior data in real time
• Offer personalized promotions to drive higher conversion rates
• Provide advertisers with measurable ROI and closed-loop attribution
• Leverage AI and machine learning for dynamic ad targeting

By integrating digital advertising with real-time customer data and real-time inventory, retailers can provide contextually relevant promotions across multiple touchpoints. The key to success lies in seamlessly connecting online and offline shopping experiences—a challenge that data streaming with Apache Kafka and Flink helps solve.

Online, Brick-and-Mortar, and Hybrid Retail Media

Retail media strategies vary depending on whether a retailer operates online, in-store, or in a hybrid model:

• Online-Only Retail Media: Retail giants like Amazon and eBay leverage vast amounts of digital consumer data to offer programmatic ads, sponsored products, and personalized recommendations directly on their websites and apps.
• Brick-and-Mortar Retail Media: Traditional retailers like Target and Albertsons are integrating digital signage, in-store Wi-Fi promotions, and AI-powered shelf displays to engage customers while shopping in physical stores.
• Hybrid Retail Media: Retailers like Walmart and Kroger are bridging the gap between digital and physical shopping experiences with omnichannel marketing strategies, personalized mobile app promotions, and AI-powered customer insights that drive both online and in-store purchases.

Omnichannel vs. Unified Commerce in Retail Media

Retailers are moving beyond omnichannel marketing, where customer interactions happen across multiple channels, to unified commerce, where all customer data, inventory, and marketing campaigns are synchronized in real time.

• Omnichannel: Offers a seamless shopping experience across different platforms but often lacks real-time data integration.
• Unified Commerce: Uses real-time data streaming to unify customer behavior, inventory management, and personalized advertising for a more cohesive experience.

For example, a unified commerce strategy allows a retailer to:

• Track customer interactions in real time across e-commerce, mobile, and in-store purchases
• Deliver personalized product recommendations based on live shopping behavior
• Optimize ad placements and promotions based on real-time demand and inventory availability

This level of integration is only possible with real-time data streaming using technologies such as Apache Kafka and Apache Flink.

How Data Streaming with Apache Kafka and Flink Powers Retail Media

Retail media networks require real-time data processing at scale to manage millions of customer interactions across online and offline touchpoints. Kafka and Flink provide the foundation for a scalable, event-driven infrastructure that enables retailers to:

• Process customer behavior in real time: Tracking clicks, searches, and in-store activity instantly
• Deliver hyper-personalized ads and promotions: AI-driven dynamic ad targeting
• Optimize inventory and pricing: Aligning promotions with real-time stock levels
• Measure campaign performance instantly: Providing brands with real-time attribution and insights

With Apache Kafka as the backbone for data streaming and Apache Flink for real-time analytics, retailers can ingest, analyze, and act on consumer data within milliseconds.

Here are a few examples of input data sources, stream processing applications, and outputs for other systems:

Input Data Sources for Retail Media

1. Customer transaction data (e.g., point-of-sale purchases, online orders)
2. Website and app interactions (e.g., product views, searches, cart additions)
3. Loyalty program data (e.g., customer preferences, purchase frequency)
4. Third-party ad networks (e.g., campaign performance data, audience segments)
5. In-store sensor and IoT data (e.g., foot traffic, digital shelf interactions)

Stream Processing Applications for Retail Media

1. Real-time advertisement personalization engine (customizes promotions based on live behavior)
2. Dynamic pricing optimization (adjusts ad bids and discounts in real-time)
3. Customer segmentation & targeting (creates audience groups based on behavioral signals)
4. Fraud detection & clickstream analysis (identifies bot traffic and fraudulent ad clicks)
5. Omnichannel attribution modeling (correlates ads with online and offline purchases)

Output Systems for Retail Media

1. Retail media network platforms (e.g., sponsored product listings, display ads)
2. Programmatic ad exchanges (e.g., Google Ads, The Trade Desk, Amazon DSP)
3. CRM & marketing automation tools (e.g., Salesforce, Adobe Experience Cloud)
4. Business intelligence dashboards (e.g., Looker, Power BI, Tableau)
5. In-store digital signage & kiosks (personalized promotions for physical shoppers)

Key Retail Media Use Cases for Data Streaming with Kafka & Flink

Real-time data streaming with Kafka and Flink enables critical retail media use cases by processing vast amounts of data from customer interactions, inventory updates and advertising platforms. The ability to analyze and act on data instantly allows retailers to optimize ad placements, enhance personalization, and measure the effectiveness of marketing campaigns with unprecedented accuracy. Below are some of the most impactful retail media applications powered by event-driven architectures.

Personalized In-Store Promotions

Retailers can use real-time customer location data, combined with purchase history and preferences, to deliver highly personalized promotions through mobile apps or digital signage. By incorporating location-based services (LBS), the system detects when a shopper enters a specific section of a store and triggers a targeted discount or special offer. For example, a customer browsing the beverage aisle might receive a notification offering 10% off their favorite soda, increasing the likelihood of an impulse purchase.

Dynamic Ad Placement & Bidding

Kafka and Flink power real-time programmatic advertising, enabling retailers to dynamically adjust ad placements and bids based on customer activity and shopping trends. This allows advertisers to serve the most relevant ads at the optimal time, maximizing engagement and conversions. For instance, Walmart Connect continuously analyzes in-store and online behavior to adjust which ads appear on product pages or search results, ensuring brands reach the right shoppers at the right moment.

Inventory-Aware Ad Targeting

Real-time inventory tracking ensures that advertisers only bid on ads for products that are in stock and ready for fulfillment, reducing wasted ad spend and improving customer satisfaction. This integration between retail media networks and inventory systems prevents scenarios where customers click on an ad only to find the item unavailable. For example, if a popular TV model is running low in a specific store, the system can prioritize ads for a similar in-stock product, ensuring a seamless shopping experience.

Fraud Detection & Brand Safety

Retailers must protect their media platforms from click fraud, fake engagement, and suspicious transactions, which can distort performance metrics and drain marketing budgets.

Kafka and Flink enable real-time fraud detection by analyzing patterns in ad clicks, user behavior, and IP addresses to identify bots or fraudulent activity. For example, if an unusual spike in ad impressions originates from a single source, the system can immediately block the traffic, safeguarding advertisers’ investments.

Real-Time Attribution & Measurement

Retail media networks must provide advertisers with instant insights into ad performance by linking online interactions to in-store purchases.

Kafka enables event-driven attribution models, allowing brands to measure how digital ads drive real-world sales. For example, if a customer clicks on an ad for running shoes, visits a store, and buys them later, the platform tracks the conversion in real time, ensuring brands understand the full impact of their campaigns. Solutions like Segment (built on Kafka) provide robust customer data platforms (CDPs) that help retailers unify and analyze customer journeys.

Retail Media as an Advertising Channel for Third-Party Brands

Retailers are increasingly leveraging third-party data sources to bridge the gap between retail media networks and adjacent industries, such as quick-service restaurants (QSRs).

Kafka enables seamless data exchange between grocery stores, delivery apps, and restaurant chains, optimizing cross-industry advertising. For example, a burger chain could dynamically adjust digital menu promotions based on real-time data from a retail partner—if a grocery store’s sales data shows a surge in plant-based meat purchases, the restaurant could prioritize ads for its new vegan burger, ensuring more relevant and effective marketing.

Albertsons’ New Retail Media Strategy Leveraging Data Streaming

One of the most innovative retail media success stories comes from Albertsons. Albertsons is one of the largest supermarket chains in the United States, operating over 2,200 stores under various banners, including Safeway, Vons, and Jewel-Osco, and providing groceries, pharmacy services, and household essentials.

I explored Albertsons in another article about its revamped loyalty platform to retain customers for life. Data streaming is essential and a key strategic part of Albertsons’ enterprise architecture:

When I hosted a webinar with Albertsons around two years ago on their data streaming strategy, retail media was one of the bullet points. But I didn’t yet realize until now how crucial it would become for retailers:

• Retail Media Network Expansion: Albertsons has launched its own retail media network, leveraging first-party data to create highly targeted advertising campaigns.
• Real-Time Personalization: With real-time data streaming, Albertsons can provide personalized promotions based on customer purchase history, in-store behavior, and digital engagement.
• AI-Powered Insights: Albertsons uses AI and machine learning on top of streaming data pipelines to optimize ad placements, campaign effectiveness, and dynamic pricing strategies.
• Data Monetization: By offering data-driven advertising solutions, Albertsons is monetizing its shopper data while enhancing the customer experience with relevant, timely promotions.

Business Value of Real-Time Retail Media

Retailers that adopt data streaming with Kafka and Flink for retail media strategies to unlock massive business value:

• New Revenue Streams: Retail media monetization drives ad sales growth
• Higher Conversion Rates: Real-time targeting improves customer engagement
• Better Customer Insights: Streaming analytics enables deep behavioral insights
• Competitive Advantage: Retailers with real-time personalization outperform rivals
• Better Customer Experience: Retail media reduces friction and enhances the shopping journey through personalized promotions

The Future of Retail Media is Real-Time and Context-Specific Data Streaming

Retail media is no longer just about placing ads on retailer websites—it’s about delivering real-time, data-driven advertising experiences across every consumer touchpoint.

With Kafka and Flink powering real-time data streaming, retailers can:

• Unify online and offline shopping experiences
• Enhance personalization with AI-driven insights
• Maximize ad revenue with real-time campaign optimization

As retailers like Albertsons, Walmart, and Amazon continue to innovate, the future of retail media will be hyper-personalized, data-driven, and real-time.

How is your organization using real-time data for retail media? Stay ahead of the curve in retail innovation! Subscribe to my newsletter for insights into data streaming and connect with me on LinkedIn to continue the conversation. And download my free book about data streaming use cases and success stories in the retail industry.

The post Retail Media with Data Streaming: The Future of Personalized Advertising in Commerce appeared first on Kai Waehner.

MWC (Mobile World Congress) 2025 serves as the global stage where industry leaders, telecom operators, and technology pioneers converge to discuss the next wave of connectivity and digital transformation. As organizations gear up for a data-driven future, real-time data streaming emerges as the critical enabler of efficiency, agility, and value creation.

This blog explores each of McKinsey’s key themes from MWC 2025 and how data streaming helps businesses innovate and gain a competitive advantage in the hyper-connected world ahead.

Join the data streaming community and stay informed about new blog posts by subscribing to my newsletter and follow me on LinkedIn or X (former Twitter) to stay in touch. And make sure to download my free book about data streaming use cases.

1. IT Excellence: Driving Telecom Innovation and Cost Efficiency

Telecom operators are under immense pressure to monetize massive infrastructure investments while maintaining cost efficiency. McKinsey’s benchmarking study shows that leading telecom tech players spend less on IT while achieving superior cost efficiency and innovation. Successful operators integrate business and IT transformations holistically, optimizing cloud strategies, IT architectures, and AI-driven processes.

How Data Streaming Powers IT Excellence

• Real-Time IT Monitoring: Streaming data pipelines provide continuous observability into IT performance, reducing downtime and optimizing infrastructure costs.
• Automated Network Operations: Event-driven architectures allow operators to dynamically allocate resources, minimizing network congestion and improving service quality.
• Cloud-Native AI Models: By continuously feeding AI models with live data, telecom leaders ensure optimal network performance and predictive maintenance.

Business Impact: Faster time-to-market, lower IT costs, and improved network reliability.

A great example of this transformation is Dish Wireless, which built a fully cloud-native, software-driven 5G network powered by Apache Kafka. By leveraging real-time data streaming, Dish ensures low-latency, scalable, and event-driven operations, allowing it to optimize network performance, automate infrastructure management, and provide next-generation connectivity for enterprise applications.

Dish’s data-first approach demonstrates how streaming technologies are redefining telecom infrastructure and unlocking new business models.

Read more about how Apache Kafka powers Dish Wireless’ 5G infrastructure or watch the following webinar with Dish:

2. Tackling Telecom Emissions: A Sustainable Future

The telecom industry faces increasing regulatory pressure and consumer expectations to decarbonize operations. While many companies have reduced Scope 1 (direct emissions) and Scope 2 (energy consumption) emissions, the real challenge lies in Scope 3 emissions from supply chains. McKinsey’s research suggests that 60% of an integrated operator’s emissions can be reduced for less than $100 per ton of CO₂.

How Data Streaming Supports Sustainability Efforts

• Energy Optimization in Real Time: Streaming analytics continuously monitor energy usage across network infrastructure, automatically adjusting power consumption.
• Carbon Footprint Tracking: Data pipelines aggregate real-time emissions data, enabling operators to meet sustainability goals efficiently.
• Predictive Maintenance for Energy Efficiency: AI-driven insights help optimize network hardware lifespan, reducing waste and unnecessary energy consumption.

Business Impact: Reduced carbon footprint, cost savings on energy consumption, and regulatory compliance.

Beyond telecom, data streaming is transforming sustainability efforts across industries. For example, in manufacturing and real estate, companies like Ampeers Energy and PAUL Tech AG use Apache Kafka and Flink to optimize energy distribution, reduce emissions, and improve ESG ratings.

These real-time data platforms analyze IoT sensor data, weather forecasts, and energy consumption patterns to automate decision-making and lower energy waste. Similarly, EverySens leverages streaming data to decarbonize freight transport, eliminating hundreds of thousands of unnecessary truck rides each year. These use cases demonstrate how data-driven sustainability strategiescan be scaled across sectors to achieve meaningful environmental impact.

Read more about how data streaming with Kafka and Flink power ESG transformations.

3. Shaping the Future of 6G: Beyond Connectivity

6G is expected to revolutionize industries by enabling ultra-low latency, ubiquitous connectivity, and AI-driven network optimization. However, the transition from 5G to 6G requires overcoming legacy infrastructure challenges and developing multi-capability platforms that go beyond simple connectivity.

How Data Streaming Powers the 6G Revolution

• Network Sensing and Intelligent Routing: Streaming architectures process real-time network telemetry, enabling adaptive, self-optimizing networks.
• AI-Enhanced Edge Computing: Real-time analytics ensure minimal latency for mission-critical applications such as autonomous vehicles and smart cities.
• Cross-Sector Data Monetization: Operators can leverage streaming data to offer network-as-a-service (NaaS) solutions, opening new revenue streams.

Business Impact: New monetization opportunities, improved network efficiency, and enhanced customer experience.

As the 6G era approaches, real-time data streaming is already proving its value in 5G deployments, unlocking low-latency, high-bandwidth use cases.

A great example is Verizon’s Mobile Edge Computing (MEC) initiative, which uses data streaming and AI-powered analytics to support real-time applications like autonomous drone monitoring, vehicle-to-everything (V2X) communication, and predictive maintenance in industrial settings. By processing data at the network edge, telcos minimize latency and optimize bandwidth—capabilities that will be even more critical in 6G.

With cloud-native, event-driven architectures, data streaming enables telcos to evolve from traditional connectivity providers to technology leaders. As 6G advances, expect faster network automation, more sophisticated AI integration, and deeper partnerships between telecom operators and enterprise customers.

Read more about how data streaming is shaping the future of telco.

4. Generative AI: A Profitability Game-Changer for Telcos

McKinsey highlights generative AI’s potential to boost telco profitability by up to 10% in annual EBITDA through automation, hyper-personalization, and AI-driven customer engagement. Leading telcos are already leveraging AI to improve customer service, marketing, and network operations.

How Data Streaming Enhances Gen AI in Telecom

• Real-Time Customer Insights: AI-powered recommendation engines deliver personalized offers and dynamic pricing in milliseconds.
• Automated Call Center Operations: Real-time transcription and sentiment analysis improve chatbot accuracy and agent productivity.
• Proactive Network Management: AI models trained on continuous streaming data predict and prevent network failures before they occur.

Business Impact: Higher customer satisfaction, reduced operational costs, and increased revenue per user.

As telecom providers integrate Generative AI (GenAI) into their business models, real-time data streaming is a foundational technology that enables efficient AI inference and model retraining. One compelling example is the GenAI Demo with Kafka, Flink, LangChain, and OpenAI, which illustrates how streaming architectures power AI-driven sales and customer interactions.

This demo showcases how real-time CRM data from Salesforce is enriched with web and LinkedIn data via streaming ETL using Apache Flink. Then, AI models process this context using LangChain and OpenAI, generating personalized, context-specific sales recommendations—a workflow that can be extended to telecom call centers and customer engagement platforms.

Expedia’s success story further highlights how GenAI combined with data streaming improves customer interactions. Facing a massive surge in support requests during COVID-19, Expedia automated responses with AI-driven chatbots, significantly reducing agent workloads. By integrating Apache Kafka with AI models, 60% of travelers began self-servicing their inquiries, resulting in over 40% cost savings in customer support operations.

For telecom providers, similar AI-driven automation can optimize call centers, personalized customer offers, fraud detection, and even predictive maintenance for network infrastructure. Data streaming ensures that AI models continuously learn from fresh data, making GenAI solutions more accurate, responsive, and cost-effective.

5. AI-Driven Software Development: Faster, Smarter, Better

AI is fundamentally transforming software development, accelerating the product development lifecycle (PDLC) and improving product quality. AI-assisted coding, automated testing, and real-time feedback loops are enabling companies to deliver customer-centric solutions at unprecedented speed.

How Data Streaming Transforms AI-Driven Software Development

• Continuous Feedback and Iteration: Streaming analytics provide instant feedback from user behavior, enabling faster iterations and bug fixes.
• Automated Code Quality Checks: AI-driven continuous integration (CI/CD) pipelines validate new code in real-time, ensuring seamless software deployments.
• Live Performance Monitoring: Streaming data enables real-time anomaly detection, ensuring optimal application performance.

Business Impact: Faster time-to-market, higher software reliability, and reduced development costs.

For telecom providers, AI-driven software development is key to maintaining a reliable, scalable, and secure network infrastructure while rolling out new customer-facing services at speed. Data streaming accelerates software development by enabling real-time feedback loops, automated testing, and AI-powered observability—bringing the industry closer to a true “Shift Left” approach.

The Shift Left Architecture in software development means moving testing, security, and quality assurance earlier in the development lifecycle, reducing costly errors and vulnerabilities late in production. Data streaming enables this shift by continuously feeding AI-driven CI/CD pipelines with real-time insights, allowing developers to detect issues earlier, optimize network performance, and iterate faster on new services.

A relevant AI-powered automation example comes from the “GenAI for Development vs. Visual Coding” article, which discusses how automation is shifting from traditional code-based development to AI-assisted software engineering. Instead of manual coding, AI-driven workflows help telcos streamline DevOps, automate CI/CD pipelines, and enhance software quality in real time.

For telecom providers, this transformation means proactive issue detection, faster rollouts of network upgrades, and automated AI-driven security monitoring—all powered by real-time data streaming and a Shift Left mindset.

Data Streaming as the Ultimate Competitive Advantage for Telcos

Across all five of McKinsey’s key trends, real-time data streaming is the backbone of transformation. Whether optimizing IT efficiency, reducing emissions, unlocking 6G’s potential, enabling generative AI and Agentic AI, or accelerating software development, streaming technologies provide the agility and intelligence businesses need to win in 2025 and beyond.

The path forward isn’t just about adopting AI or cloud-native infrastructure—it’s about embracing real-time, event-driven architectures to drive innovation at scale.

As organizations take bold steps to lead the future, those who harness the power of data streaming will emerge as the industry’s true pioneers.

Stay ahead of the curve! Subscribe to my newsletter for insights into data streaming and connect with me on LinkedIn to continue the conversation. And make sure to download my free book about data streaming use cases.

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Understanding Model Drift: The Achilles’ Heel of Static Models

Real-time model inference with a data streaming platform using Apache Kafka and Flink is a powerful solution for delivering fast and accurate predictions, as detailed in my model inference blog post, but it’s not enough to sustain long-term model accuracy.

Machine learning models degrade in accuracy over time due to shifts in data or concepts—a phenomenon known as model drift.

This can take several forms:

1. Concept Drift: Changing relationships between input and output variables, such as shifting user behavior.
2. Data Drift: Variations in data distribution, e.g., demographic shifts.
3. Upstream Data Changes: Pipeline modifications, e.g., new logging formats or unavailable sources.

Unchecked, model drift leads to poor predictions and missed opportunities. Addressing it requires continuous updates, which online machine learning enables through data streaming platforms like Kafka and Flink.

TikTok: Revolutionizing Real-Time Personalization with Kafka and Flink

TikTok’s recommendation system, detailed in ByteDance’s whitepaper, leverages a cutting-edge, real-time machine learning architecture powered by data streaming technologies like Kafka and Flink to deliver personalized content at scale, seamlessly integrating user behavior data, dynamic feature processing, and online model updates for unparalleled user engagement and platform efficiency.

What is ByteDance and TikTok?

ByteDance, TikTok’s parent company, is a Chinese technology giant renowned for its innovative use of AI and real-time ML. TikTok, its most famous product, has redefined user engagement through hyper-personalized video recommendations. TikTok employs real-time online machine learning, ensuring recommendations are dynamic, accurate, and engaging.

Why TikTok Outshines Competitors

While other social video platforms also leverage advanced machine learning for recommendations, TikTok’s architecture distinguishes itself by prioritizing real-time adaptability and hyper-personalization, ensuring it can respond to user behavior faster and more effectively than its competitors.

• User Engagement: TikTok’s recommendation engine adapts in real-time, delivering hyper-relevant content that increases user retention.
• Scalability: Unlike many platforms relying on periodic retraining, TikTok continuously updates its models, handling massive data streams with ease.
• Speed: Real-time processing reduces latency in adapting to user behavior, a stark contrast to Facebook or YouTube’s delayed batch processes.

Technical Implementation using Apache Kafka, Flink and Machine Learning for Continuous Online Model Training

TikTok’s real-time recommendation system is built on a robust streaming data architecture:

Data Ingestion:

• User interactions like views, likes, and shares are streamed in real-time via Kafka.
• Kafka ensures reliable collection and distribution of high-velocity event data.

Feature Engineering:

• Flink processes raw data streams, performing real-time feature extraction and enrichment.
• Techniques like point-in-time lookups prevent training-inference skew, ensuring the same features are used in both phases.

Online Model Training:

• Lightweight models are continuously updated with fresh data.
• This approach mitigates model drift, ensuring predictions stay relevant and accurate.

Real-Time Inference:

• Updated models are deployed immediately to serve predictions.
• TikTok’s architecture ensures latency is minimal, with recommendations delivered almost instantly.

This dynamic infrastructure has made TikTok a leader in real-time AI, setting a benchmark for others.

Data Streaming with Kafka and Flink: The Backbone of Modern Machine Learning and AI

Apache Kafka and Flink are indispensable for organizations embracing online ML.

Data streaming addresses key challenges:

• Training-Inference Data Skew: By streaming real-time features into models, Flink ensures consistency in model training and inference data.
• Multi-Model Governance: Kafka and Flink enable the data integration with small models for enrichment and large models for complex decision-making, ensuring governance and modularity.
• Scalability and Efficiency: Data streaming pipelines handle massive volumes with low latency, enabling real-time decision-making.

Complementing Other Data Platforms: Streaming Meets Analytics

Data streaming complements platforms like Databricks, Snowflake, and Microsoft Fabric, creating a seamless ecosystem for AI/ML workflows:

• Databricks: While Databricks excels in large-scale batch processing and AI model training, Kafka adds real-time data ingestion and pre-processing capabilities.
• Snowflake: Zero-ETL integration with Kafka and Flink allows for real-time analytics alongside Snowflake’s strong data warehousing and AI features.
• Microsoft Fabric: Fabric’s AI-powered analytics gain agility from Kafka’s event-driven architecture, ensuring near-instant data availability.

Shift Left Architecture with Apache Iceberg as Open Table Format for Data Sharing

The Shift Left Architecture emphasizes moving from traditional batch processing and lakehouse-centric approaches to real-time data products, empowering businesses to act on data faster and with greater agility. Learn more about this transformative approach in my Shift Left Architecture blog post.

Meanwhile, Apache Iceberg, an open table format for lakehouses and streaming, ensures seamless data sharing across real-time and batch workflows by providing a unified view of data. Dive deeper into its capabilities in my Apache Iceberg blog post.

This complementary relationship enables businesses to leverage best-in-class tools without trade-offs, providing both real-time and batch capabilities. Learn more in my comparison blog series “Data Streaming with Kafka and Flink vs. Snowflake” and “Microsoft Fabric and Apache Kafka“.

Business Value: A Compelling Case for Real-Time AI/ML with Data Streaming using Kafka and Flink

The adoption of real-time ML with Kafka and Flink drives tangible business outcomes:

1. Enhanced User Engagement: Personalized recommendations lead to improved customer retention.
2. Faster Time to Market: Real-time data pipelines reduce the lead time for deploying ML solutions.
3. Improved ROI: Real-time adaptability ensures models deliver consistent business value.
4. Freedom of Choice: Kafka acts as the backbone, enabling seamless integration with diverse tools and platforms.

This translates to a flexible, scalable, and high-performing ML infrastructure capable of handling evolving business demands.

Real-Time AI/ML with Kafka and Flink: Transforming Data into Business Value

Online machine learning with Apache Kafka and Flink is the future of adaptive, real-time AI. TikTok’s success story is a testament to the power of dynamic AI/ML systems in driving engagement and staying competitive. By complementing platforms like Snowflake, Databricks, and Microsoft Fabric, data streaming enables a holistic, future-proof data strategy.

Organizations must embrace these technologies to unlock faster time to market, unparalleled user experiences, and sustained business growth.

Let’s connect on LinkedIn and discuss how to implement these ideas in your organization. Stay informed about new developments by subscribing to my newsletter. And make sure to download my free book about data streaming use cases.

The post Online Model Training and Model Drift in Machine Learning with Apache Kafka and Flink appeared first on Kai Waehner.

Join the data streaming community and stay informed about new blog posts by subscribing to my newsletter and follow me on LinkedIn or X (former Twitter) to stay in touch. And make sure to download my free book about data streaming use cases across all industries.

Data Streaming in the FinServ Industry

This article builds on FinTechMagazine.com’s “Top 10 Emerging Technologies in Finance“ by mapping each of these innovations to real-time data streaming concepts, possibilities, and real-world success stories.

By leveraging Apache Kafka and Apache Flink, financial institutions can process transactions instantly, detect fraud proactively, and enhance customer experiences with real-time insights. Each emerging technology—whether IoT payment networks, AI-powered banking, or embedded finance—relies on the ability to stream, analyze, and act on data in real time, making data streaming a foundational enabler of the future of finance.

10. IoT Payment Networks: Real-time Processing for Seamless Payments

IoT payment networks enable automated, contactless transactions using connected devices like smartwatches, cars, and home appliances. Whether it’s a fridge restocking groceries or a car paying for tolls, these interactions generate massive real-time data streams that must be processed instantly and securely.

How Data Streaming with Kafka and Flink Adds Value

• Fraud Detection in Milliseconds – Flink analyzes streaming transaction data to detect anomalies, flagging fraudulent activity before payments are approved.
• Reliable Connectivity – Kafka ensures payment events from IoT devices are securely transmitted and processed, preventing dropped or duplicate transactions.
• Dynamic Pricing & Offers – Flink processes sensor and market data to adjust prices dynamically (e.g., surge pricing for EV charging stations) and deliver real-time personalized discounts.
• Edge Processing for Low-Latency Payments – Kafka enables local transaction validation on IoT devices, reducing lag in autonomous vehicle payments and retail checkout systems.
• Compliance & Security – Streaming pipelines support real-time monitoring, encryption, and anomaly detection, ensuring IoT payments meet financial regulations like PSD2 and PCI DSS.

In financial services, don’t make the mistake of only looking inward for lessons—other industries have been solving similar challenges for years. Consumer IoT and Apache Kafka have long been used together in sectors like retail, where real-time data integration is critical for unified commerce, rewards programs, social selling, and many other use cases.

9. Voice-First Banking: Turning Conversations into Transactions

Voice-first banking enables customers to interact with financial services using smart speakers, virtual assistants, and mobile voice recognition. Whether checking an account balance, making a payment, or applying for a loan, these interactions require instant access to multiple backend systems—from core banking and CRM to fraud detection and credit scoring systems.

To make voice banking seamless, fast, and secure, banks must integrate real-time data streaming between AI-powered voice assistants and backend financial systems. This is where Apache Kafka and Apache Flink come in.

How Data Streaming with Kafka and Flink Adds Value

• Seamless Integration Across Banking Systems – Voice assistants need real-time access to core banking (account balances, transactions), CRM (customer history), risk systems (fraud checks), and AI analytics. Kafka acts as a high-speed messaging and integration layer (aka ESB/middleware), ensuring that voice requests are instantly routed to the right backend services (including legacy technologies, such as mainframe) and responses are processed in milliseconds.
• Instant Voice Query Processing – When a customer asks, “What’s my balance?”, Flink streams real-time transaction data from Kafka to retrieve the latest balance, rather than relying on outdated batch data.
• Secure Authentication & Fraud Detection – Streaming pipelines analyze voice patterns in real time to detect fraud and trigger multi-factor authentication (MFA) if needed.
• Personalized & Context-Aware Banking and Advertising – Flink continuously enriches customer profiles by analyzing past transactions, spending habits, and preferences—allowing the system to offer real-time financial insights (e.g., suggesting a savings plan based on spending trends).
• Asynchronous Processing for Long-Running Requests – For complex tasks like loan applications, Kafka handles asynchronous processing—initiating background workflows across multiple systems while keeping the customer engaged.

For instance, Northwestern Mutual presented at Kafka Summit how the bank leverages Apache Kafka as a database for real-time transaction processing.

8. Autonomous Finance Platforms: AI-Driven Financial Decision Making

Autonomous finance platforms use AI, machine learning, and multi-agent systems to optimize savings, investments, and budgeting for consumers. These platforms act as digital financial advisors to make real-time decisions based on market data, user spending habits, and risk models.

How Data Streaming with Kafka and Flink Adds Value

• Multi-Agent AI System Coordination – Autonomous finance platforms use multiple AI agents to handle different aspects of financial decision-making (e.g., portfolio optimization, credit assessment, fraud detection). Kafka streams data between these AI agents, ensuring they can collaborate in real time to refine investment and savings strategies.
• Streaming Market Data Integration – Kafka ingests live stock prices, interest rates, and macroeconomic data, making it instantly available for AI models to adjust financial strategies.
• Real-Time Customer Insights – Flink continuously analyzes customer transactions and spending behavior to enable AI-driven recommendations (e.g., automatically moving surplus funds into an interest-bearing account).
• Predictive Portfolio Management – By combining real-time stock market data with AI-driven risk models, Flink helps adjust portfolio allocations based on current trends, ensuring maximum returns while minimizing exposure.
• Automated Risk Mitigation – Autonomous finance systems must react instantly to market shifts. Flink’s real-time monitoring detects economic downturns or sudden market crashes, triggering immediate adjustments to investment portfolios or loan interest rates.
• Event-Driven Financial Automation – Kafka enables real-time triggers (e.g., an AI agent detecting high inflation can automatically adjust a savings strategy).

7. RegTech 3.0: Automating Compliance and Risk Monitoring

RegTech is modernizing compliance by replacing slow batch audits with continuous real-time monitoring, automated reporting, and proactive fraud detection.

Financial institutions need instant insights into transactions, risk exposure, and regulatory changes—Kafka and Flink make this possible by streaming, analyzing, and automating compliance at scale.

How Data Streaming with Kafka and Flink Adds Value

• Continuous Transaction Monitoring – Kafka streams every transaction in real time, enabling Flink to detect fraud, money laundering, or unusual patterns instantly—ensuring compliance with AML and KYC regulations.
• Automated Regulatory Reporting – Flink processes compliance events as they happen, ensuring regulatory bodies receive up-to-date reports without delays. Kafka integrates compliance data across banking systems for audit-ready records.
• Real-Time Fraud Prevention – Flink analyzes transaction behavior in milliseconds, detecting anomalies and triggering security actions like transaction blocking or multi-factor authentication.
• Event-Driven Compliance Alerts – Kafka ensures instant alerts when regulations change, allowing banks to adapt in real time instead of relying on manual updates.
• Proactive Risk Management – By analyzing live risk factors across transactions, users, and markets, Flink helps financial institutions identify and prevent compliance violations before they occur.

For example, KOR leverages data streaming to revolutionize compliance and regulatory reporting in the derivatives market by enabling on-demand historical reporting and real-time insights that were previously difficult to achieve with traditional batch processing. By using Kafka as a persistent state store, KOR ensures an immutable log of data that allows regulators to track changes over time, reconcile historical corrections, and meet compliance requirements more efficiently than legacy ETL-based big data systems. Read the entire KOR success story in my ebook.

6. Central Bank Digital Currencies (CBDC): The Future of Government-Backed Digital Money

Central Bank Digital Currencies (CBDC) are digital versions of national currencies, designed to enable faster, more secure, and highly scalable financial transactions.

Unlike cryptocurrencies, CBDCs are government-backed, meaning they require robust, real-time infrastructure capable of handling millions of transactions per second. They also need instant settlement, fraud detection, and cross-border interoperability—all of which depend on real-time data streaming.

How Data Streaming with Kafka and Flink Adds Value

• Instant Settlement – Kafka ensures that CBDC transactions are processed and confirmed in real time, eliminating delays in digital payments. This allows central banks to enable 24/7 instant transactions, even in cross-border scenarios.
• Scalability for Nationwide Adoption – Flink dynamically processes millions of transactions per second, ensuring that a CBDC system can handle high demand without bottlenecks or downtime.
• Cross-Border Payments & Exchange Rate Optimization – Flink analyzes foreign exchange markets in real time and ensures optimized B2B data exchange for currency conversion and detecting suspicious cross-border activities for fraud prevention.
• Regulatory Monitoring & Compliance – Kafka continuously streams transaction data to regulatory bodies. This ensures governments have real-time visibility into the movement of digital currencies.

At Kafka Summit Bangalore 2024, Mindgate Solutions presented its successful integration of Central Bank Digital Currency (CBDC) into banking apps, leveraging real-time data streaming to enable seamless digital payments. Mindgate utilized Kafka-based microservices architecture to ensure scalability, security, and reliability, reinforcing its leadership in India’s real-time payments ecosystem while processing over 8 billion transactions per month.

5. Green Fintech Infrastructure: Sustainability and ESG in Finance

Green fintech focuses on tracking carbon footprints, ESG (Environmental, Social, and Governance) investments, and climate risks in real time.

As financial institutions shift towards sustainable investment strategies, they need accurate, real-time data on environmental impact, regulatory compliance, and green investment opportunities.

How Data Streaming with Kafka and Flink Adds Value

• Real-Time Carbon Tracking – Kafka streams emissions and sustainability data from supply chains to enable instant carbon footprint analysis.
• Automated ESG Compliance – Flink analyzes sustainability reports and investment portfolios, automatically flagging non-compliant companies or assets.
• Green Investment Insights – Real-time analytics match investors with eco-friendly projects, funds, and companies, helping financial institutions promote sustainable investments.

More details about optimizing the ESG footprint with data streaming: “Green Data, Clean Insights: How Kafka and Flink Power ESG Transformations“.

4. AI-Powered Personalized Banking: Hyper-Personalized Customer Experiences

AI-driven banking solutions are transforming how customers interact with financial institutions to provide real-time insights, spending recommendations, and fraud alerts based on user behavior.

How Data Streaming with Kafka and Flink Adds Value

• Real-Time Spending Analysis – Flink continuously processes live transaction data, identifying spending patterns to provide instant budgeting recommendations.
• Personalized Alerts & Recommendations – Kafka streams transaction events to banking apps, notifying users of unusual spending, low balances, or savings opportunities.
• Automated Financial Planning – Flink enables AI-driven financial assistance, helping users optimize savings, credit usage, and investments based on real-time insights.

A good example is how Erste Group Bank modernized its mobile banking experience with a hyper-personalized approach to ensure that customers receive tailored financial insights while prioritizing data consistency over real-time updates. By offloading data from expensive mainframes to a cloud-native, microservices-driven architecture, Erste Group Bank reduced costs, maintained compliance, and improved operational efficiency—ensuring a seamless flow of consistent, high-quality data across its legacy and modern banking applications. Read the entire Erste Group Bank success story in my ebook.

3. Decentralized Identity Solutions: Secure Identity Without Central Authorities

Decentralized identity solutions allow users to control their personal data, eliminating the need for centralized databases that are vulnerable to hacks. These systems use blockchain and zero-knowledge proofs for secure, passwordless authentication, but require real-time verification and fraud prevention measures.

How Data Streaming with Kafka and Flink Adds Value

• Cybersecurity in Real Time – Kafka streams biometric and identity verification data to fraud detection engines, ensuring instant risk analysis.
• Passwordless Authentication – Kafka integrates blockchain and biometric authentication to enable real-time identity validation without traditional passwords.
• Secure KYC (Know Your Customer) Processing – Flink processes identity verification requests instantly, ensuring faster onboarding and fraud-proof financial transactions.

2. Quantum-Resistant Cryptography: Securing Financial Data in the Quantum Era

Quantum computing poses a major risk to traditional encryption methods, requiring financial institutions to adopt post-quantum cryptography to secure sensitive financial transactions and user data.

How Data Streaming with Kafka and Flink Adds Value

• Scalable Cryptographic Upgrades – Streaming data pipelines allow banks to deploy cryptographic updates instantly, ensuring financial systems remain secure without downtime.
• Threat Detection & Security Analysis – Flink analyzes live transaction patterns to identify potential vulnerabilities in encryption algorithms before they are exploited.

Nobody knows where quantum computing goes. Frankly, this is the only section of the top 10 finance innovations where I am not sure how much data streaming will be able to help or if completely new paradigms come up.

1. Embedded Finance: Banking Services in Every Digital Experience

Embedded finance integrates banking, payments, lending, and insurance into non-financial platforms, allowing companies like Uber, Shopify, and Apple to offer seamless financial services within their ecosystems.

To function smoothly, embedded finance requires real-time data integration between payment processors, credit scoring systems, fraud detection tools, and regulatory bodies.

How Data Streaming with Kafka and Flink Adds Value

• Instant Payments & Transactions – Kafka streams payment data in real time, enabling seamless in-app purchases and instant money transfers.
• Real-Time Credit Scoring & Lending – Flink analyzes transaction histories to provide instant credit approvals for loans and BNPL (Buy Now, Pay Later) services.
• Fraud Prevention & Compliance – Streaming analytics detect suspicious behavior in real time, ensuring secure embedded financial transactions.

Tech giants like Uber and Shopify have embedded financial services directly into their platforms using event-driven architectures powered by Kafka, enabling real-time payments, lending, and fraud detection. By integrating finance seamlessly into their ecosystems, these companies enhance customer experience, create new revenue streams, and redefine how consumers interact with financial services.

Just like Uber and Shopify use event-driven architectures for real-time payments and financial services, Stripe and many similar FinTech companies power embedded finance for businesses by providing seamless, scalable payment infrastructure. To ensure six-nines (99.9999%) availability, Stripe relies on Apache Kafka as its financial source of truth to enable ultra-reliable transaction processing and real-time financial insights.

The Future of FinServ Is Real-Time: Are You Ready for Data Streaming?

The future of finance is real-time, intelligent, and seamlessly integrated into digital ecosystems. The ability to process massive amounts of financial data instantly is no longer optional—it’s a competitive necessity for operational and analytical use cases.

Data streaming with Apache Kafka and Apache Flink provides the foundation for scalability, security, and real-time analytics that modern financial services demand. By embracing data streaming, financial institutions can deliver:

• Faster transactions
• Proactive fraud prevention
• Better customer experiences
• Regulatory compliance

Finance is evolving from batch processing to real-time intelligence—and the companies that adopt streaming-first architectures will lead the industry into the future.

How do you leverage data streaming with Kafka and Flink in financial services? Let’s discuss on LinkedIn or X (former Twitter). Also join the data streaming community and stay informed about new blog posts by subscribing to my newsletter and to stay in touch. And make sure to download my free book about data streaming use cases across all industries.

The post How Data Streaming with Apache Kafka and Flink Drives the Top 10 Innovations in FinServ appeared first on Kai Waehner.

As threats grow more sophisticated, the technologies powering cybersecurity solutions must advance to stay ahead. Data streaming technologies like Apache Kafka and Apache Flink have become foundational in this evolution, enabling real-time threat detection, prevention, and rapid response. These tools transform cybersecurity from static defenses to dynamic systems capable of identifying and neutralizing threats as they occur.

A notable example is McAfee, a global leader in cybersecurity, which has embraced data streaming to revolutionize its operations. By transitioning to an event-driven architecture powered by Apache Kafka, McAfee processes massive amounts of real-time data from millions of endpoints, ensuring instant threat identification and mitigation. This integration has enhanced scalability, reduced infrastructure complexity, and accelerated innovation, setting a benchmark for the cybersecurity industry.

Real-time data streaming is not just an advantage—it’s now a necessity for organizations aiming to safeguard digital environments against ever-evolving threats.

Join the data streaming community and stay informed about new blog posts by subscribing to my newsletter and follow me on LinkedIn or X (former Twitter) to stay in touch.

Antivirus is NOT Enough: Supply Chain Attack

A supply chain attack occurs when attackers exploit vulnerabilities in an organization’s supply chain, targeting weaker links such as vendors or service providers to indirectly infiltrate the target.

For example, an attacker compromises Vendor 1, a software provider, by injecting malicious code into their product. Vendor 2, a service provider using Vendor 1’s software, becomes infected. The attacker then leverages Vendor 2’s connection to the Enterprise to access sensitive systems, even though Vendor 1 has no direct interaction with the enterprise.

Traditional antivirus software is insufficient to prevent such complex, multi-layered attacks. Ransomware often plays a role in supply chain attacks, as attackers use it to encrypt data or disrupt operations across compromised systems.

Modern solutions focus on real-time monitoring and event-driven architecture to detect and mitigate risks across the supply chain. These solutions utilize behavioral analytics, zero trust policies, and proactive threat intelligence to identify and stop anomalies before they escalate.

By providing end-to-end visibility, they protect organizations from cascading vulnerabilities that traditional endpoint security cannot address. In today’s interconnected world, comprehensive supply chain security is critical to safeguarding enterprises.

The Role of Data Streaming in Cybersecurity

Cybersecurity platforms must rely on real-time data for detecting and mitigating threats. Data streaming provides a backbone for processing massive amounts of security event data as it happens, ensuring swift and effective responses. My blog series on Kafka and cybersecurity looks deeply into these use cases.

To summarize:

• Data Collection: A data streaming platforms powered by Apache Kafka collect logs, telemetry, and other data from devices and applications in real time.
• Data Processing: Stream processing frameworks like Kafka Streams and Apache Flink continuously process this data with low latency at scale for analytics, identifying anomalies or potential threats.
• Actionable Insights: The processed data feeds into Security Information and Event Management (SIEM) and Security Orchestration, Automation, and Response (SOAR) systems, enabling automated responses and better decision-making.

This approach transforms static, batch-driven cybersecurity operations into dynamic, real-time processes.

McAfee: A Real-World Data Streaming Success Story

McAfee is a global leader in cybersecurity, providing software solutions that protect computers, networks, and devices. Founded in 1987, the company has evolved from traditional antivirus software to a comprehensive suite of products focused on threat prevention, identity protection, and data security.

McAfee’s products cater to both individual consumers and enterprises, offering real-time protection through partnerships with global integrated service providers (ISPs) and telecom operators.

Mahesh Tyagarajan (VP, Platform Engineering and Architecture at McAfee) spoke with Confluent and Forrester about McAfee’s transition from a monolith to event-driven Microservices leveraging Apache Kafka in Confluent Cloud.

Data Streaming at McAfee with Apache Kafka Leveraging Confluent Cloud

As cyber threats have grown more complex, McAfee’s reliance on real-time data streaming has become essential. The company transitioned from a monolithic architecture to a microservices-based ecosystem with the help of Confluent Cloud, powered by Apache Kafka. The fully managed data streaming platform simplified infrastructure management, boosted scalability, and accelerated feature delivery for McAfee

Use Cases for Data Streaming

1. Real-Time Threat Detection: McAfee processes security events from millions of endpoints, ensuring immediate identification of malware or phishing attempts.
2. Subscription Management: Data streaming supports real-time customer notifications, updates, and billing processes.
3. Analytics and Reporting: McAfee integrates real-time data streams into analytics systems, providing insights into user behavior, threat patterns, and operational efficiency.

Transition to an Event-Driven Architecture and Microservices

By moving to an event-driven architecture with Kafka using Confluent Cloud, McAfee:

• Standardized its data streaming infrastructure.
• Decoupled systems using microservices, enabling scalability and resilience.
• Improved developer productivity by reducing infrastructure management overhead.

This transition to data streaming with a fully managed, complete and secure cloud service empowered McAfee to handle high data ingestion volumes, manage hundreds of millions of devices, and deliver new features faster.

Business Value of Data Streaming

The adoption of data streaming delivered significant business benefits:

• Improved Customer Experience: Real-time threat detection and personalized updates enhance trust and satisfaction.
• Operational Efficiency: Automation and reduced infrastructure complexity save time and resources.
• Scalability: McAfee can now support a growing number of devices and data sources without compromising performance.

Data Streaming as the Backbone of an Event-Driven Cybersecurity Evolution in the Cloud

McAfee’s journey showcases the transformative potential of data streaming in cybersecurity. By leveraging Apache Kafka as fully managed cloud service as the backbone of an event-driven microservices architecture, the company has enhanced its ability to detect threats, respond in real time, and deliver exceptional customer experiences.

For organizations looking to stay ahead in the cybersecurity race, investing in real-time data streaming technologies is not just an option—it’s a necessity. To learn more about how data streaming can revolutionize cybersecurity, explore my cybersecurity blog series and follow me for updates on LinkedIn or X (formerly Twitter).

The post The Role of Data Streaming in McAfee’s Cybersecurity Evolution appeared first on Kai Waehner.

What is Dynamic Pricing?

Dynamic pricing is a strategy where prices are adjusted automatically based on real-time data inputs, such as demand, customer behavior, supply levels, and competitor actions. This model allows companies to optimize profitability, boost sales, and better meet customer expectations.

Relevant Industries and Examples

Dynamic pricing has applications across many industries:

• Retail and eCommerce: Dynamic pricing in eCommerce helps adjust product prices based on stock levels, competitor actions, and customer demand. Companies like Amazon frequently update prices on millions of products, using dynamic pricing to maximize revenue.
• Transportation and Mobility: Ride-sharing companies like Uber and Grab adjust fares based on real-time demand and traffic conditions. This is commonly known as “surge pricing.”
• Gaming: Context-specific in-game add-ons or virtual items are offered at varying prices based on player engagement, time spent in-game, and special events or levels.
• Energy Markets: Dynamic pricing in energy adjusts rates in response to demand fluctuations, energy availability, and wholesale costs. This approach helps to stabilize the grid and manage resources.
• Sports and Entertainment Ticketing: Ticket prices for events are adjusted based on seat availability, demand, and event timing to allow venues and ticketing platforms to balance occupancy and maximize ticket revenue.
• Hospitality: Adaptive room rates and promotions in real-time based on demand, seasonality, and guest behavior, using dynamic pricing models.

These industries have adopted dynamic pricing to maintain profitability, manage supply-demand balance, and enhance customer satisfaction through personalized, responsive pricing.

Relation of Dynamic Pricing to Data Streaming with Apache Kafka and Flink

Dynamic pricing relies on up-to-the-minute data on market and customer conditions, making real-time data streaming critical to its success. Traditional batch processing, where data is collected and processed periodically, is insufficient for dynamic pricing. It introduces delays that could mean lost revenue opportunities or suboptimal pricing. This scenario is where data streaming technologies come into play.

• Apache Kafka serves as the real-time data pipeline, collecting and distributing data streams from diverse sources. For instance, user behaviour on websites, competitor pricing, social media signals, IoT data, and more. Kafka’s capability to handle high throughput and low latency makes it ideal for ingesting large volumes of data continuously.
• Apache Flink processes the data in real-time, applying complex algorithms to identify pricing opportunities as conditions change. With Flink’s support for stream processing and complex event processing, businesses can apply sophisticated logic to assess and adjust prices based on multiple real-time factors.

Together, Kafka and Flink create a powerful foundation for dynamic pricing, enabling real-time data ingestion, analysis, and action. This empowers companies to implement pricing models that are not only highly responsive but also resilient and scalable.

Clickstream Analytics in Real-Time with Data Streaming Replacing Batch with Hadoop and Spark

Years ago, companies relied on Hadoop and Spark to run batch-based clickstream analytics. Data engineers ingested logs from websites, online stores, and mobile apps to gather insights. Processing took hours. Therefore, any promotional offer or discount often arrived a day later — by which time the customer may have already made their purchase elsewhere, like on Amazon.

With today’s data streaming platforms like Kafka and Flink, clickstream analytics has evolved to support real-time, context-specific engagement and dynamic pricing. Instead of waiting on delayed insights, businesses can now analyze customer behavior as it happens, instantly adjusting prices and delivering personalized offers at the moment. This dynamic pricing capability allows companies to respond immediately to high-intent customers, presenting tailored prices or promotions when they’re most likely to convert. Dynamic pricing with Kafka and Flink can create a much better seamless and timely shopping experience that maximizes sales and customer satisfaction.

Success Stories for Dynamic Pricing with Data Streaming using Kafka and Flink Across Industries

Here’s how businesses across various sectors are harnessing Kafka and Flink for dynamic pricing.

• Retail: Hyper-Personalized Promotions and Discounts
• Logistics and Transportation: Intelligent Tolling
• Technology: Surge Pricing
• Energy Markets: Manage Supply-Demand and Stabilize Grid Loads
• Gaming: Context-Specific In-Game Add-Ons
• Sports and Entertainment: Optimize Ticketing Sales Sports and Entertainment

Learn more about data streaming with Kafka and Flink for dynamic pricing in the following success stories:

AO: Hyper-Personalized Promotions and Discounts (Retail and eCommerce)

AO, a major UK eCommerce retailer, leverages data streaming for dynamic pricing to stay competitive and drive higher customer engagement. By ingesting real-time data on competitor prices, customer demand, and inventory stock levels, AO’s system processes this information instantly to adjust prices in sync with market conditions. This approach allows AO to seize pricing opportunities and align closely with customer expectations. The result is a 30% increase in customer conversion rates.

Dynamic pricing has also allowed AO to provide a hyper-personalized shopping experience, delivering relevant product recommendations and timely promotions. This real-time responsiveness has enhanced customer satisfaction and loyalty, as customers receive offers that feel customized to their needs. During high-traffic periods like holiday sales, AO’s dynamic pricing ensures competitiveness and optimizes margins. This drives both profitability and customer retention. The company has applied this real-time approach not just to pricing, but also to other areas like delivery to make things run smoother. The retailer is now much more efficient and provides better customer service.

Quarterhill: Intelligent Tolling (Logistics and Transportation)

Quarterhill, a leader in tolling and intelligent transportation systems, uses Kafka and Flink to implement dynamic toll pricing. Kafka ingests real-time data from traffic sensors and road usage patterns. Flink processes this data to determine congestion levels and calculate the optimal toll based on real-time conditions.

This dynamic pricing strategy allows Quarterhill to manage road congestion effectively, reward off-peak travel, and optimize toll revenues. This system not only improves travel efficiency but also helps regulate traffic flows in high-density areas, providing value both to drivers and the city infrastructure.

Uber, Grab, and FreeNow: Surge Pricing (Technology)

Ride-sharing companies like Uber, Grab, and FreeNow are widely known for their dynamic pricing or “surge pricing” models. With data streaming, these platforms capture data on demand, supply (available drivers), location, and traffic in real time. This data is processed continuously by Apache Flink, Kafka Streams or other stream processing engines to calculate optimal pricing, balancing supply with demand, while considering variables like route distance and current traffic.

Surge pricing enables these companies to provide incentives for drivers to operate in high-demand areas, maintaining service availability and ensuring customer needs are met during peak times. This real-time pricing model improves revenue while optimizing customer satisfaction through prompt service availability.

Uber’s Kappa Architecture is an excellent example for how to build a data pipeline for dynamic pricing and many other use cases with Kafka and Flink:

2K Games / Take-Two Interactive: Context-Specific In-Game Purchases (Gaming Industry)

In the gaming industry, dynamic pricing is becoming a strategy to improve player engagement and monetize experiences. Many gaming companies use Kafka and Flink to capture real-time data on player interactions, time spent in specific game sections, and in-game events. This data enables companies to offer personalized pricing for in-game items, bonuses, or add-ons, adjusting prices based on the player’s current engagement level and recent activities.

For instance, if players are actively taking part in a particular game event, they may be offered special discounts or dynamic prices on related in-game assets. Thereby, the gaming companies improve conversion rates and player engagement while maximizing revenue.

2K Games,A leading video game publisher and a subsidiary of Take-Two Interactive, has shifted from batch to real-time analytics to enhance player engagement across popular franchises like BioShock, NBA 2K, and Borderlands. By leveraging Confluent Cloud as fully managed data streaming platform, the publisher scales dynamically to handle high traffic, processing up to 3000 MB per second to serve 4 million concurrent users.

Real-time telemetry analytics now allow them to analyze player actions and context instantly, enabling personalized, context-specific promotions and enhancing the gaming experience. Cost efficiencies are achieved through data compression, tiered storage, and reduced data transfer, making real-time engagement both effective and economical.

50hertz: Manage Supply-Demand and Stabilize Grid Loads (Energy Markets)

Dynamic pricing in energy markets is essential for managing supply-demand fluctuations and stabilizing grid loads. With Kafka, energy providers ingest data from smart meters, renewable energy sources, and weather. Flink processes this data in real-time, adjusting energy prices based on grid conditions, demand levels, and renewable supply availability.

50Hertz, as a leading electricity transmission system operator, indirectly (!) affects dynamic pricing in the energy market by sharing real-time grid data with partners and energy providers. This allows energy providers and market operators to adjust prices dynamically based on real-time insights into supply-demand fluctuations and grid stability.

To support this, 50Hertz is modernizing its SCADA systems with data streaming technologies to enable real-time data capture and distribution that enhances grid monitoring and responsiveness.

Real-time pricing approach helps encourage consumption when renewable energy is abundant and discourages usage during peak times, leading to optimized energy distribution, grid stability, and improved sustainability.

Ticketmaster: Optimize Ticketing Sales (Sports and Entertainment)

In ticketing, dynamic pricing allows for optimized revenue management based on demand and availability. Companies like Ticketmaster use Kafka to collect data on ticket availability, sales velocity, and even social media sentiment surrounding events. Flink processes this data to adjust prices based on real-time market conditions, such as proximity to the event date and current demand.

By dynamically pricing tickets, event organizers can maximize seat occupancy, boost revenue, and respond to last-minute demand surges, ensuring that prices reflect real-time interest while enhancing fan satisfaction.

Real-time inventory data streams allow Ticketmaster to monitor ticket availability, pricing, and demand as they change moment-to-moment. With data streaming through Apache Kafka and Confluent Platform, Ticketmaster tracks sales, venue capacity, and customer behavior in a single, live inventory stream. This enables quick responses, such as adjusting prices for high-demand events or boosting promotions where conversions lag. Teams gain actionable insights to forecast demand accurately and optimize inventory. This approach ensures fans have timely access to tickets. The result is a dynamic, data-driven approach that enhances customer experience and maximizes event success.

Conclusion: Business Value of Dynamic Pricing Built with Data Streaming

Dynamic pricing powered by data streaming with Apache Kafka and Flink brings transformative business value by:

• Maximizing Revenue and Margins: Real-time price adjustments enable companies to capture value during demand surges, optimize for competitive conditions, and maintain healthy margins.
• Improving Operational Efficiency: By automating pricing decisions based on real-time data, organizations can reduce manual intervention, speed up reaction times, and allocate resources more effectively.
• Boosting Customer Satisfaction: Responsive pricing models allow companies to meet customer expectations in real time, leading to improved customer loyalty and engagement.
• Supporting Sustainability Goals: In energy and transportation, dynamic pricing helps manage resources and reward environmentally friendly behaviors. Examples include off-peak travel and renewable energy usage.
• Empowering Strategic Decision-Making: Real-time data insights provide business leaders with the information needed to adjust strategies and respond to developing market demands quickly.

Building a dynamic pricing system with Kafka and Flink represents a strategic investment in business agility and competitive resilience. Using data streaming to set prices instantly, businesses can stay ahead of competitors, improve customer service, and become more profitable. Dynamic pricing powered by data streaming is more than just a revenue tool; it’s a vital lever for driving growth, differentiation, and long-term success.

Did you already implement dynamic pricing? What is your data platform and strategy? Do you use Apache Kafka and Flink? Let’s connect on LinkedIn and discuss it! Stay informed about new blog posts by subscribing to my newsletter.

The post A New Era in Dynamic Pricing: Real-Time Data Streaming with Apache Kafka and Flink appeared first on Kai Waehner.

What is Intersport?

Intersport International Corporation GmbH, commonly known as Intersport, is headquartered in Bern, Switzerland, but its roots trace back to Austria. Intersport is a global sporting goods retail group that operates a network of stores selling sports equipment, apparel, and related products. It is one of the world’s largest sporting goods retailers and has a presence in many countries around the world.

Intersport stores typically offer a wide range of products for various sports and outdoor activities, including sports clothing, footwear, equipment for sports such as soccer, tennis, skiing, cycling, and more. The company often partners with popular sports brands to offer a variety of products to its customers.

Intersport actively promotes sports and physical activity and frequently sponsors sports events and initiatives to encourage people to lead active and healthy lifestyles. The specific products and services offered by Intersport may vary from one location to another, depending on local market demand and trends.

The company automates and innovates continuously with software capabilities like fully automated replenishment, drop shipping, personalized recommendations for customers, and other applications.

How does Intersport leverage Data Streaming with Apache Kafka?

Intersport presented its data streaming success story together with the system integrator DCCS at the Data in Motion Tour 2023 in Vienna, Austria.

Here is a summary about the deployment, use cases, and project lifecycle at Intersport:

• Apache Kafka as the strategic integration hub powered by fully managed Confluent Cloud
• Central nervous system to enable data consistency between real-time data and non-real-time data, i.e., batch systems, files, databases, and APIs.
• Loyalty platform with real-time bonus point system
• Personalized marketing and hybrid omnichannel customer experience across online and stores
• Integration with SAP ERP, financial accounting (SAP FI) and 3rd Party B2B like bike rental, 100s of POS, and legacy like FTP and XML interfaces
• Fast time-to-market because of the fully managed cloud: The pilot project with 100 stores and 200 Point of Sale (POS) was finished in 6 months. The entire production rollout took only 12 months.

Is Apache Kafka a Database? No. But…

No, Apache Kafka is NOT a database. Apache Kafka is a distributed streaming platform that is designed for building real-time data pipelines and streaming applications. Users frequently apply it for ingesting, processing, and storing large volumes of event data in real time.

Apache Kafka does not provide the traditional features associated with databases, such as random access to stored data or support for complex queries. If you need a database for storage and retrieval of structured data, you would typically use a database system like MySQL, PostgreSQL, MongoDB, or others with Kafka to address different aspects of your data processing needs.

However, Apache Kafka is a database if you focus on cost-efficient long-term storage and the replayability of historical data. I wrote a long article about the database characteristics of Apache Kafka. Read it to understand when (not) to use Kafka as a database. The emergence of Tiered Storage for Kafka created even more use cases.

In this blog post, I want to focus on one specific feature of Apache Kafka for long-term storage and query functionality: Compacted Topics.

What is a Compacted Topic in Apache Kafka?

Kafka is a distributed event streaming platform, and topics are the primary means of organizing and categorizing data within Kafka. “Compacted Topic” in Apache Kafka refers to a specific type of Kafka Topic configuration that is used to keep only the most recent value for each key within the topic.

In a compacted topic, Kafka ensures that, for each unique key, only the latest message (or event) associated with that key is retained. The system effectively discards older messages with the same key. A Compacted Topic is often used for scenarios where you want to maintain the latest state or record for each key. This can be useful in various applications, such as maintaining the latest user profile information, aggregating statistics, or storing configuration data.

Here are some key characteristics and use cases for compacted topics in Kafka:

1. Key-Value Semantics: A compacted topic supports scenarios where you have a key-value data model, and you want to query the most recent value for each unique key.
2. Log Compaction: Kafka uses a mechanism called “log compaction” to ensure that only the latest message for each key is retained in the topic. This means that Kafka will retain the entire history of changes for each key, but it will remove older versions of a key’s data once a newer version arrives.
3. Stateful Processing: Compacted topics are often used in stream processing applications where maintaining the state is important. Stream processing frameworks like Apache Kafka Streams and ksqlDB leverage a compacted topic to perform stateful operations.
4. Change-Data Capture (CDC): Change-data capture scenarios use compacted topics to track changes to data over time. For example, capturing changes to a database table and storing them in Kafka with the latest version of each record.

Compacted Topic at Intersport to Store all Retail Articles in Apache Kafka

Intersport stores all articles in Compacted Topics, i.e., with no retention time. Article records can change several times. Topic compaction cleans out outdated records. Only the most recent version is relevant.

Article Data Structure

A model comprises several SKUS as a nested array:

• An SKU represents an article with its size and color
• Every SKU has shop based prices (purchase price, sales price, list price)
• Not every SKU is available in every shop

Accurate Stock Information across the Supply Chain

Intersport and DCCS presented their important points and benefits of leveraging Kafka. The central integration hub uses compacted topics for storing and retrieving articles:

• Customer facing processes demand real time
• Stock info needs to be accurate
• Distribute master data to all relevant sub system as soon as it changes
• Scale flexible on high load (shopping weekends before Christmas)

Providing the right information at the right time is crucial across the supply chain. Data consistency matters, as not every system is real-time. This is one of the most underestimated sweet spots of Apache Kafka combining real-time messaging with a persistent event store.

Log Compaction in Kafka does NOT Replace BUT Complement other Databases

Intersport is an excellent example in the retail industry for persisting information long-term in Kafka Topics leveraging Kafka’s feature “Compacted Topics“. The benefits are simple usage, cost-efficient event store of the latest up-to-date information, and fast key/value queries, and no need for another database. Hence, Kafka can replace a database for some specific scenarios, like storing and querying the inventory of each store at Intersport.

If you want to learn about other use cases and success stories for data streaming with Kafka and Flink in the retail industry, check out these articles:

• The State of Data Streaming in the Retail Industry
• Use Cases for Apache Kafka in Retail
• Real-Time Supply Chain with Apache Kafka in the Food and Retail Industry
• Kafka for Live Commerce to Transform the Retail and Shopping Metaverse
• Omnichannel Retail and Customer 360 in Real Time with Apache Kafka
• A Hybrid Streaming Architecture for Smart Retail Stores with Apache Kafka
• Apache Kafka is the New Black at the Edge in Industrial IoT, Logistics and Retailing

How do you use data streaming with Kafka and Flink? What retail use cases did you implement? Let’s connect on LinkedIn and discuss it! Stay informed about new blog posts by subscribing to my newsletter.

The post How the Retailer Intersport uses Apache Kafka as Database with Compacted Topic appeared first on Kai Waehner.

The State of Data Streaming for Manufacturing in 2023

The evolution of industrial IoT, manufacturing 4.0, and digitalized B2B and customer relations require modern, open, and scalable information sharing. Data streaming allows integrating and correlating data in real-time at any scale. Trends like software-defined manufacturing and data streaming help modernize and innovate the entire engineering and sales lifecycle.

I have recently presented an overview of trending enterprise architectures in the manufacturing industry and data streaming customer stories from BMW, Mercedes, Michelin, and Siemens. A complete slide deck and on-demand video recording are included:

This blog post explores one of the enterprise architectures and case studies in more detail: Data streaming between edge infrastructure (like a smart factory) and applications in the data center or public cloud.

What is a Smart Factory? And how does Data Streaming help?

Smart Factory is a term from research in manufacturing technology. It refers to the vision of a production environment in which manufacturing plants and logistics systems primarily organize themselves without human intervention to produce the desired products.

The technical basis is cyber-physical systems, i.e., physical manufacturing objects and virtual images in a centralized system. Digital Twins often play a crucial role in smart factories for simulation, engineering, condition monitoring, predictive maintenance, and other tasks.

In the broader context, the Internet of Things (IoT) is the foundation of a smart factory. Communication between the product (e.g., workpiece) and the manufacturing system continues to be part of this future scenario: The product brings its manufacturing information in machine-readable form, e.g., on an RFID chip. This data controls the product’s path through the production system and the individual production steps. Other transmission technologies, such as WLAN, Bluetooth, color coding, or QR codes, are also being experimented with.

Data streaming helps connect high-volume sensor data from machines, PLCs, robots, and other IoT devices. Integrating and pre-processing the events with data streaming is a prerequisite for data correlation with information systems like the MES or ERP (that might run at the edge or more often in the cloud). The latter is possible in real-time at scale with stream processing. The de facto standard for data streaming is Apache Kafka and its ecosystems, like Kafka Stream and Kafka Connect.

BMW Group: Data from 30 Smart Factories Streamed to the Cloud

BMW Group needed to make all data generated by its 30+ production facilities and worldwide sales network available in real-time to anyone across the global business.

The data ingested by BMW from its smart factories into the cloud with data streaming enables simple access to the data for visibility and new automation applications by any business unit.

The Apache Kafka ecosystem facilitates the decoupling between logistics and production systems. Transparent data flows and the flexibility of building innovative new services are possible with this access to events from everywhere in the company.

Stability is vital in manufacturing across the supply chain. This begins with Tier 1 and Tier 2 suppliers up to the aftersales and service management. Direct integration from the shop floor to serverless Confluent Cloud on Azure ensures a mission-critical data streaming environment for data pipelines between edge and cloud.

The use case enables reliable data sharing across the logistics and supply chain processes for BMW’s global plants.

Data streaming enables:

• Access to information about the right stock in place (physically and in ERP systems like SAP)
• Just in time, just in sequence process optimization
• Improved overall equipment effectiveness (OEE) for a lot of critical applications
• Last-mile integration between Apache Kafka and IoT standards like OPC-UA

Read more about BMW’s success story for IoT and cloud-native data streaming.

Lightboard Video: How Data Streaming Connects Smart Factory and Cloud

Here is a five-minute lightboard video that describes how data streaming helps with the integration between production facilities (or any other edge environments) and the cloud:

If you liked this video, make sure to follow the YouTube channel for many more lightboard videos across all industries.

IoT and Edge are not contradictory to Cloud and Data Streaming

The BMW case study shows how you can build reliable real-time synchronization between smart factories and cloud applications. However, there are more options. For more case studies, check out the free “The State of Data Streaming in Manufacturing” on-demand recording or read the related blog post.

MQTT is combined with Kafka regularly if the use case requires supporting bad networks or millions of IoT clients. Another alternative is data streaming at the edge with highly available Kafka clusters on industrial PCs, e.g., for air-gapped environments, or embedded single Kafka brokers, e.g., deployment in a machine.

Humans are still crucial for the success of a smart factory. Improving the OEE requires a smart combination of software, robots, and people. Augmented Reality leveraging Data Streaming is an excellent example. VR/AR platforms like Unity enable remote services, training, or simulation. Apache Kafka is the foundation for real-time data sharing across these different technologies and interfaces.

How do you leverage data streaming in your manufacturing use cases? Do you deploy at the edge, in the cloud, or both? Let’s connect on LinkedIn and discuss it! Join the data streaming community and stay informed about new blog posts by subscribing to my newsletter.

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