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The Top Data Streaming Trends

Some followers might notice that this became a series with articles about the top 5 data streaming trends for 2021, the top 5 for 2022, the top 5 for 2023, and the top 5 for 2024. Trends change over time, but the huge value of having a scalable real-time infrastructure as the central data hub stays. Data streaming with Apache Kafka is a journey and evolution to set data in motion.

I recently explored the past, present, and future of data streaming tools and strategies from the past decades. Data streaming is becoming more and more mature and standardized, but also innovative.

Let’s now look at the top trends coming up more regularly in conversations with customers, prospects, and the broader data streaming community across the globe:

1. The Democratization of Kafka: Apache Kafka has transitioned from a specialized tool to a key pillar in modern data infrastructure.
2. Kafka Protocol as the Standard: Vendors adopt the Kafka wire protocol, enabling flexibility with compatibility and performance trade-offs.
3. BYOC Deployment Model: Bring Your Own Cloud gains traction for balancing security, compliance, and managed services.
4. Flink Becomes the Standard for Stream Processing: Apache Flink rises as the premier framework for stream processing, building integration pipelines and business applications.
5. Data Streaming for Real-Time Predictive AI and GenAI: Real-time model inference drives predictive and generative AI applications.
6. Data Streaming Organizations: Companies unify real-time data strategies to standardize processes, tools, governance, and collaboration.

The following sections describe each trend in more detail. The trends are relevant for many scenarios; no matter if you use open-source frameworks like Apache Kafka and Flink, a commercial platform, or a fully managed cloud service like Confluent Cloud.

Trend 1: The Democratization of Kafka

In the last decade, Apache Kafka has become the standard for data streaming, evolving from a specialized tool to an essential utility in the modern tech stack. With over 150,000 organizations using Kafka today, it has become the de facto choice for stream processing. Yet, with a market crowded by offerings from AWS, Microsoft Azure, Google GCP, IBM, Oracle, Confluent, and various startups, companies can no longer rely solely on Kafka for differentiation. The vast array of Kafka-compatible solutions means that businesses face more choices than ever, but also new challenges in selecting the solution that balances cost, performance, and features.

The Challenge: Finding the Right Fit in a Crowded Kafka Market

For end users, choosing the right Kafka solution is becoming increasingly complex. Basic Kafka offerings cover standard streaming needs but may lack advanced features, such as enhanced security, data governance, or integration and processing capabilities, that are essential for specific industries. In such a diverse market, businesses must navigate trade-offs, considering whether a low-cost option meets their needs or whether investing in a premium solution with added capabilities provides better long-term value.

The Solution: Prioritizing Features for Your Strategic Needs

As Kafka solutions evolve, users must look beyond price and consider features that offer real strategic value. For example, companies handling sensitive customer data might benefit from Kafka products with top-tier security features. Those focused on analytics may look for solutions with strong integrations into data platforms and low cost for high throughput. By carefully selecting a Kafka product that aligns with industry-specific requirements, businesses can leverage the full potential of Kafka while optimizing for cost and capabilities.

For instance, look at Confluent’s various cluster types for different requirements and use cases in the cloud:

As an example, Freight Clusters was introduced to provide an offering with up to 90 percent less cost. The major trade-off is higher latency. But this is perfect for high volume log analytics at GB/sec scale.

The Business Value: Affordable and Customized Data Streaming

Kafka’s commoditization means more affordable, customizable options for businesses of all sizes. This competition reduces costs, making high-performance data streaming more accessible, even to smaller organizations. By choosing a tailored solution, businesses can enhance customer satisfaction, speed up decision-making, and innovate faster in a competitive landscape.

Trend 2: The Kafka Protocol, not Apache Kafka, is the New Standard for Data Streaming

With the rise of cloud-native architectures, many vendors have shifted to supporting the Kafka protocol rather than the open-source Kafka framework itself, allowing for greater flexibility and cloud optimization. This change enables businesses to choose Kafka-compatible tools that better align with specific needs, moving away from a one-size-fits-all approach.

Confluent introduced its KORA engine, i.e., Kafka re-architected to be cloud-native. A deep technical whitepaper goes into the details (this is not a marketing document but really for software engineers).

Other players followed Confluent and introduced their own cloud-native “data streaming engines”. For instance, StreamNative has URSA powered by Apache Pulsar, Redpanda talks about its R1 Engine implementing the Kafka protocol, and Ververica recently announced VERA for its Flink-based platform.

Some vendors rely only on the Kafka protocol with a proprietary engine from the beginning. For instance, Azure Event Hubs or WarpStream. Amazon MSK also goes in this direction by adding proprietary features like Tiered Storage or even introducing completely new product options such as Amazon MSK Express brokers.

The Challenge: Limited Compatibility Across Kafka Solutions

When vendors implement the Kafka protocol instead of the entire Kafka framework, it can lead to compatibility issues, especially if the solution doesn’t fully support Kafka APIs. For end users, this can complicate integration, particularly for advanced features like Exactly-Once Semantics, the Transaction API, Compacted Topics, Kafka Connect, or Kafka Streams, which may not be supported or working as expected.

The Solution: Evaluating Kafka Protocol Solutions Critically

To fully leverage the flexibility of Kafka protocol-based solutions, a thorough evaluation is essential. Businesses should carefully assess the capabilities and compatibility of each option, ensuring it meets their specific needs. Key considerations include verifying the support of required features and APIs (such as the Transaction API, Kafka Streams, or Connect).

It is also crucial to evaluate the level of product support provided, including 24/7 availability, uptime SLAs, and compatibility with the latest versions of open-source Apache Kafka. This detailed evaluation ensures that the chosen solution integrates seamlessly into existing architectures and delivers the reliability and performance required for modern data streaming applications.

The Business Value: Expanded Options and Cost-Efficiency

Kafka protocol-based solutions offer greater flexibility, allowing businesses to select Kafka-compatible services optimized for their specific environments. This flexibility opens doors for innovation, enabling companies to experiment with new tools without vendor lock-in.

For instance, innovations such as a “direct write to S3 object store” architecture, as seen in WarpStream, Confluent Freight Clusters, and other data streaming startups that also build proprietary engines around the Kafka protocol. The result is a more cost-effective approach to data streaming, though it may come with trade-offs, such as increased latency. Check out this video about the evolution of Kafka Storage to learn more.

Trend 3: BYOC (Bring Your Own Cloud) as a New Deployment Model for Security and Compliance

As data security and compliance concerns grow, the Bring Your Own Cloud (BYOC) model is gaining traction as a new way to deploy Apache Kafka. BYOC allows businesses to host Kafka in their own Virtual Private Cloud (VPC) while the vendor manages the control plane to handle complex orchestration tasks like partitioning, replication, and failover.

This BYOC approach offers organizations enhanced control over their data while retaining the operational benefits of a managed service. BYOC provides a middle ground between self-managed and fully managed solutions, addressing specific regulatory and security needs without sacrificing scalability or flexibility.

The Challenge: Balancing Security and Ease of Use

Ensuring data sovereignty and compliance is non-negotiable for organizations in highly regulated industries. However, traditional fully managed cloud solutions can pose risks due to vendor access to sensitive data and infrastructure. Many BYOC solutions claim to address these issues but fall short when it comes to minimizing external access to customer environments. Common challenges include:

• Vendor Access to VPCs: Many BYOC offerings require vendors to have access to customer VPCs for deployment, cluster management, and troubleshooting. This introduces potential security vulnerabilities.
• IAM Roles and Elevated Privileges: Cross-account Identity and Access Management (IAM) roles are often necessary for managing BYOC clusters, which can expose sensitive systems to unnecessary risks.
• VPC Peering Complexity: Traditional BYOC solutions often rely on VPC peering, a complex and expensive setup that increases operational overhead and opens additional points of failure.

These limitations create significant challenges for security-conscious organizations, as they undermine the core promise of BYOC: control over the data environment.

The Solution: Gaining Control with a “Zero Access” BYOC Model

WarpStream redefines the BYOC model with a “zero access” architecture, addressing the challenges of traditional BYOC solutions. Unlike other BYOC offerings using the Kafka protocol, WarpStream ensures that no data leaves the customer’s environment, delivering a truly secure-by-default platform. Hence this section discusses specifically WarpStream, not BYOC Kafka offerings in general.

Key features of WarpStream include:

• Zero Access to Customer VPCs: WarpStream eliminates vendor access by deploying stateless agents within the customer’s environment, handling compute operations locally without requiring cross-account IAM roles or elevated privileges to reduce security risks.
• Data/Metadata Separation: Raw data remains entirely within the customer’s network for full sovereignty, while only metadata is sent to WarpStream’s control plane for centralized management, ensuring data security and compliance.
• Simplified Infrastructure: WarpStream avoids complex setups like VPC peering and cross-IAM roles, minimizing operational overhead while maintaining high performance.

Comparison with Other BYOC Solutions using the Kafka protocol:

Unlike most other BYOC offerings (e.g., Redpanda), WarpStream doesn’t require direct VPC access or elevated permissions, avoiding risks like data exposure or remote troubleshooting vulnerabilities. Its “zero access” architecture ensures unparalleled security and compliance.

The Business Value: Secure, Compliant, and Scalable Data Streaming

WarpStream’s innovative approach to BYOC delivers exceptional business value by addressing security and compliance concerns while maintaining operational simplicity and scalability:

• Uncompromised Security: The zero-access architecture ensures that raw data remains entirely within the customer’s environment, meeting the strictest security and compliance requirements for regulated industries like finance, healthcare, and government.
• Operational Efficiency: By eliminating the need for VPC peering, cross-IAM roles, and remote vendor access, WarpStream simplifies BYOC deployments and reduces operational complexity.
• Cost Optimization: WarpStream’s reliance on cloud-native technologies like object storage reduces infrastructure costs compared to traditional disk-based approaches. Stateless agents also enable efficient scaling without unnecessary overhead.
• Data Sovereignty: The data/metadata split guarantees that data never leaves the customer’s environment, ensuring compliance with regulations such as GDPR and HIPAA.
• Peace of Mind for Security Teams: With no vendor access to the VPC or object storage, WarpStream’s zero-access model eliminates concerns about external breaches or elevated privileges, making it easier to gain buy-in from security and infrastructure teams.

BYOC Strikes the Balance Between Control and Managed Services

BYOC offers businesses the ability to strike a balance between control and managed services, but not all BYOC solutions are created equal. WarpStream’s “zero access” architecture sets a new standard, addressing the critical challenges of security, compliance, and operational simplicity. By ensuring that raw data never leaves the customer’s environment and eliminating the need for vendor access to VPCs, WarpStream delivers a BYOC model that meets the highest standards of security and performance. For organizations seeking a secure, scalable, and compliant approach to data streaming, WarpStream represents the future of BYOC data streaming.

But just to be clear: If a data streaming project goes to the cloud, fully managed Kafka (and Flink) should always be the first option as it is much easier to manage and operate to focus on fast time-to-market and business innovation. Choose BYOC only if fully managed does not work for you because of security requirements.

Trend 4: Flink Becomes the De Facto Standard for Stream Processing

Apache Flink has emerged as the premier choice for organizations seeking a robust and versatile framework for continuous stream processing. Its ability to handle complex data pipelines with high throughput, low latency, and advanced stateful operations has solidified its position as the de facto standard for stream processing. Flink’s support for Java, Python, and SQL further enhances its appeal, enabling developers to build powerful data-driven applications using familiar tools.

As Flink adoption grows, it increasingly complements Apache Kafka as part of the modern data streaming ecosystem, while the Kafka Streams (Java-only) library remains relevant for lightweight, application-embedded use cases.

The Challenge: Handling Complex, High-Throughput Data Streams

Modern businesses increasingly rely on real-time data for both operational and analytical needs, spanning mission-critical applications like fraud detection, predictive maintenance, and personalized customer experiences, as well as Streaming ETL for integrating and transforming data. These diverse use cases demand robust stream processing capabilities that can address the challenges of:

The Solution: Apache Flink for Scalability and Versatility

Apache Flink’s versatility makes it uniquely positioned to meet the demands of both streaming ETL for data integration and building real-time business applications. Flink provides:

• Low Latency: Near-instantaneous processing is crucial for enabling real-time decision-making in business applications, timely updates in analytical systems, and supporting transactional workloads where rapid processing and immediate consistency are essential for ensuring smooth operations and seamless user experiences.
• High Throughput and Scalability: The ability to process millions of events per second, whether for aggregating operational metrics or moving massive volumes of data into data lakes or warehouses, without bottlenecks.
• Stateful Processing: Support for maintaining and querying the state of data streams, essential for performing complex operations like aggregations, joins, and pattern detection in business applications, as well as data transformations and enrichment in ETL pipelines.
• Multiple Programming Languages: Support for Java, Python, and SQL ensures accessibility for a wide range of developers, enabling efficient implementation across various use cases.

The rise of cloud services has further propelled Flink’s adoption, with offerings from major providers like Confluent, Amazon, IBM, and emerging startups. These cloud-native solutions simplify Flink deployments, making it easier for organizations to operationalize real-time analytics.

Apache Spark and Spark Streaming as Alternative for Flink?

While Apache Flink has emerged as the de facto standard for stream processing, other frameworks like Apache Spark and its streaming module, Structured Streaming, continue to compete in this space. However, Spark Streaming has notable limitations that make it less suitable for many of the complex, high-throughput workloads modern enterprises demand.

The Challenges with Spark Streaming

Apache Spark, originally designed as a batch processing framework, introduced Spark Streaming and later Structured Streaming to address real-time processing needs. However, its batch-oriented roots present inherent challenges:

• Micro-Batch Architecture: Spark Structured Streaming relies on micro-batches, where data is divided into small time intervals for processing. This approach, while effective for certain workloads, introduces higher latency compared to Flink’s true streaming architecture. Applications requiring millisecond-level processing or transactional workloads may find Spark unsuitable.
• Limited Stateful Processing: While Spark supports stateful operations, its reliance on micro-batches adds complexity and latency. This makes Spark Streaming less efficient for use cases that demand continuous state updates, such as fraud detection or complex event processing (CEP).
• Fault Tolerance Complexity: Spark’s recovery model is rooted in its lineage-based approach to fault tolerance, which can be less efficient for long-running streaming applications. Flink, by contrast, uses checkpointing and savepoints to handle failures more gracefully to ensure state consistency with minimal overhead.
• Performance Overhead: Spark’s general-purpose design often results in higher resource consumption compared to Flink, which is purpose-built for stream processing. This can lead to increased infrastructure costs for high-throughput workloads.
• Scalability Challenges for Stateful Workloads: While Spark scales effectively for batch jobs, its scalability for complex stateful stream processing is more limited, as distributed state management in micro-batches can become a bottleneck under heavy load.

By addressing these limitations, Apache Flink provides a more versatile and efficient solution than Apache Spark for organizations looking to handle complex, real-time data processing at scale.

Flink’s architecture is purpose-built for streaming, offering native support for stateful processing, low-latency event handling, and fault-tolerant operation, making it the preferred choice for modern real-time applications. But to be clear: Apache Spark, including Spark Streaming, has its place in data lakes and lakehouses to process analytical workloads.

The Business Value: Real-Time Analytics and Improved Decision-Making with Apache Flink

Flink’s technical capabilities bring tangible business benefits, making it an essential tool for modern enterprises. By providing real-time insights, Flink enables businesses to respond to events as they occur, such as detecting and mitigating fraudulent transactions instantly, reducing losses, and enhancing customer trust.

The support of Flink for both transactional workloads (e.g., fraud detection or payment processing) and analytical workloads (e.g., real-time reporting or trend analysis) ensures versatility across a range of critical business functions. Scalability and resource optimization keep infrastructure costs manageable, even for demanding, high-throughput workloads, while features like checkpointing streamline failure recovery and upgrades, minimizing operational overhead.

Flink stands out with its dual focus on streaming ETL for data integration and building business applications powered by real-time analytics. Its rich APIs for Java, Python, and SQL make it easy for developers to implement complex workflows, accelerating time-to-market for new applications.

Trend 5: AI and Data Streaming: Real-Time Model Inference with Flink

Data streaming has powered AI/ML infrastructure for many years because of its capabilities to scale to high volumes, process data in real-time, and integrate with transactional (payments, orders, ERP, etc.) and analytical (data warehouse, data lake, lakehouse) systems. My first article about Apache Kafka and Machine Learning was published in 2017: “How to Build and Deploy Scalable Machine Learning in Production with Apache Kafka“.

As AI continues to evolve, real-time model inference powered by data streaming is opening up new possibilities for predictive and generative AI applications. By integrating model inference with stream processors such as Apache Flink, businesses can perform on-demand predictions for fraud detection, customer personalization, and more.

The Challenge: Provide Context for AI Applications In Real-Time

Traditional batch-based AI inference is too slow for many applications, delaying responses and leading to missed opportunities or wrong business decisions. To fully harness AI in real-time, businesses need to embed model inference directly within streaming pipelines.

Generative AI (GenAI) demands new design patterns like Retrieval Augmented Generation (RAG) to ensure accuracy, relevance, and reliability in its outputs. Without data streaming, RAG struggles to provide large language models (LLMs) with the real-time, domain-specific context they need, leading to outdated or hallucinated responses. Context is essential to ensure that LLMs deliver accurate and trustworthy outputs by grounding them in up-to-date and precise information.

The Solution: Leveraging Apache Flink for Real-Time Context and Model Inference

Apache Flink enables real-time model inference by connecting data streams to external AI models through APIs. This setup allows companies to use centralized model servers for inference, providing flexibility and scalability while keeping data streams fast and responsive.

Flink’s ability to process data in real-time also enables advanced machine learning workflows, supporting predictive analytics and generative AI use cases that drive innovation.

Apache Flink enables real-time model inference by connecting data streams to external AI models through APIs. This setup allows companies to use centralized model servers for inference, providing flexibility and scalability while keeping data streams fast and responsive. By processing data in real-time, Flink ensures that generative AI models operate with the most current and relevant context, reducing errors and hallucinations.

Flink’s real-time processing capabilities also support advanced machine learning workflows. This enables use cases like predictive analytics, anomaly detection, and generative AI applications that require instantaneous decision-making. The ability to join live data streams with historical or external datasets enriches the context for model inference, enhancing both accuracy and relevance.

Additionally, Flink facilitates feature extraction and data preprocessing directly within the stream to ensure that the inputs to AI models are optimized for performance. This seamless integration with model servers and vector databases allows organizations to scale their AI systems effectively, leveraging real-time insights to drive innovation and deliver immediate business value.

The Business Value: Immediate, Actionable AI Insights

Real-time AI model inference with Flink enables businesses to provide personalized customer experiences, detect fraud as it happens, and perform predictive maintenance with minimal latency. This real-time responsiveness empowers companies to make AI-driven decisions in milliseconds, improving customer satisfaction and operational efficiency.

By integrating Flink with event-driven architectures like Apache Kafka, businesses can ensure that AI systems are always fed with up-to-date and trustworthy data, further enhancing the reliability of predictions.

The integration of Flink and data streaming offers a clear path to measurable business impact. By aligning real-time AI capabilities with organizational goals, they can drive innovation while reducing operational costs, such as automating customer support to lower reliance on service agents.

Furthermore, Flink’s ability to process and enrich data streams at scale supports strategic initiatives like hyper-personalized marketing or optimizing supply chains in real-time. These benefits directly translate into enhanced competitive positioning, faster time-to-market for AI-driven solutions, and the ability to make more confident, data-driven decisions at the speed of business.

Trend 6: Becoming a Data Streaming Organization

To harness the full potential of data streaming, companies are shifting toward structured, enterprise-wide data streaming strategies. Moving from a tactical, ad-hoc approach to a cohesive top-down strategy enables businesses to align data streaming with organizational goals, driving both efficiency and innovation.

The Challenge: Fragmented Data Streaming Efforts

Many companies face challenges due to disjointed streaming efforts, leading to data silos and inconsistencies that prevent them from reaping the full benefits of real-time data processing. At Confluent, we call this the enterprise adoption barrier:

This fragmentation results in inefficiencies, duplication of efforts, and a lack of standardized processes. Without a unified approach, organizations struggle with:

• Data Silos: Limited data sharing across teams creates bottlenecks for broader use cases.
• Inconsistent Standards: Different teams often use varying schemas, patterns, and practices, leading to integration challenges and data quality issues.
• Governance Gaps: A lack of defined roles, responsibilities, and policies results in limited oversight, increasing the risk of data misuse and compliance violations.

These challenges prevent organizations from scaling their data streaming capabilities and realizing the full value of their real-time data investments.

The Solution: Building an Integrated Data Streaming Organization

By adopting a comprehensive data streaming strategy, businesses can create a unified data platform with standardized tools and practices. A dedicated streaming platform team, often called the Center of Excellence (CoE), ensures consistent operations. An internal developer platform provides governed, self-serve access to streaming resources.

Key elements of a data streaming organization include:

• Unified Platform: Move from disparate tools and approaches to a single, standardized data streaming platform. This includes consistent policies for cluster management, multi-tenancy, and topic naming, ensuring a reliable foundation for data streaming initiatives.
• Self-Service: Provide APIs, UIs, and other interfaces for teams to onboard, create, and manage data streaming resources. Self-service capabilities ensure governed access to topics, schemas, and streaming capabilities, empowering developers while maintaining compliance and security.
• Data as a Product: Adopt a product-oriented mindset where data streams are treated as reusable assets. This includes formalizing data products with clear contracts, ownership, and metadata, making them discoverable and consumable across the organization.
• Alignment: Define clear roles and responsibilities, from platform operators and developers to data product owners. Establishing an enterprise-wide data streaming function ensures coordination and alignment across teams.
• Governance: Implement automated guardrails for compliance, quality, and access control. This ensures that data streaming efforts remain secure, trustworthy, and scalable.

The Business Value: Consistent, Scalable, and Agile Data Streaming

Becoming a Data Streaming Organization unlocks significant value by turning data streaming into a strategic asset. The benefits include:

• Enhanced Agility: A unified platform reduces time-to-market for new data-driven products and services, allowing businesses to respond quickly to market trends and customer demands.
• Operational Efficiency: Streamlined processes and self-service capabilities reduce the overhead of managing multiple tools and teams, improving productivity and cost-effectiveness.
• Scalable Innovation: Standardized patterns and reusable data products enable the rapid development of new use cases, fostering a culture of innovation across the enterprise.
• Improved Governance: Clear policies and automated controls ensure data quality, security, and compliance, building trust with customers and stakeholders.
• Cross-Functional Collaboration: By breaking down silos, organizations can leverage data streams across teams, creating a network effect that accelerates value creation.

To successfully adopt a Data Streaming Organization model, companies must combine technical capabilities with cultural and structural change. This involves not just deploying tools but establishing shared goals, metrics, and education to bring teams together around the value of real-time data. As organizations embrace data streaming as a strategic function, they position themselves to thrive in a data-driven world.

Embracing the Future of Data Streaming

As data streaming continues to mature, it has become the backbone of modern digital enterprises. It enables real-time decision-making, operational efficiency, and transformative AI applications. Trends such as the commoditization of Kafka, the adoption of the Kafka protocol, BYOC deployment models, and the rise of Flink as the standard for stream processing demonstrate the rapid evolution and growing importance of this technology. These innovations not only streamline infrastructure but also empower organizations to harness real-time insights, foster agility, and remain competitive in the ever-changing digital landscape.

These advancements in data streaming present a unique opportunity to redefine data strategy. Leveraging data streaming as a central pillar of IT architecture allows businesses to break down silos, integrate machine learning into critical workflows, and deliver unparalleled customer experiences. The convergence of data streaming with generative AI, particularly through frameworks like Flink, underscores the importance of embracing a real-time-first approach to data-driven innovation.

Looking ahead, organizations that invest in scalable, secure, and strategic data streaming infrastructures will be positioned to lead in 2025 and beyond. By adopting these trends, enterprises can unlock the full potential of their data, drive business transformation, and solidify their place as leaders in the digital era. The journey to set data in motion is not just about technology—it’s about building the foundation for a future where real-time intelligence powers every decision and every experience.

What trends do you see for data streaming? Which ones are your favorites? Let’s connect on LinkedIn and discuss it! Stay informed about new blog posts by subscribing to my newsletter.

The post Top Trends for Data Streaming with Apache Kafka and Flink in 2025 appeared first on Kai Waehner.

This is part three of a blog series about Microsoft Fabric and its relation to other data platforms on the Azure cloud:

1. What is Microsoft Fabric for Azure Cloud (Beyond the Buzz) and how it Compares (or Competes) with Snowflake and Databricks
2. How Microsoft Fabric Complements Data Streaming (Apache Kafka, Flink, et al.)
3. When to Choose Apache Kafka vs. Azure Event Hubs vs. Confluent Cloud for a Microsoft Fabric Lakehouse

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Please read the other two articles to understand why Microsoft Fabric is not a silver bullet for every data problem. And how data streaming and Microsoft Fabric are complementary. This article focuses on choosing the right data streaming service for Microsoft Fabric data ingestion and beyond for many other use cases.

Apache Kafka – The De Facto Standard for Data Streaming

Apache Kafka has established itself as the cornerstone of data streaming, offering far more than traditional messaging systems. It provides a persistent event log that guarantees ordering and enables true decoupling of data producers and consumers and data consistency across real-time, batch and request-response APIs. Kafka Connect, which facilitates seamless integration with various data sources and sinks, and Kafka Streams, which allows for continuous stateless and stateful stream processing, complement the Kafka architecture. With its robust capabilities, Kafka is used by over 150,000 organizations worldwide. This underscores its status as a new software category, as recognized in the Forrester Wave for Streaming Data.

Benefits:

• Vibrant Open Source Community: Kafka’s extensive community fosters continuous innovation and support, ensuring that the platform remains at the forefront of data streaming technology.
• Reliability and Scalability: Kafka is battle-tested in diverse environments, offering unmatched reliability and scalability for critical applications.
• Continuous Innovation: Kafka’s evolution is marked by significant advancements, such as the removal of ZooKeeper and support for tiered storage. Upcoming features include queues for Kafka and support for two-phase commit transactions, further enhancing its capabilities.

Cons:

• Self-Managed Complexity: Operating Kafka as a self-managed system can be challenging, especially for critical use cases requiring 24/7 uptime and low latency.
• Core-Only Offering: Kafka’s core requires additional components for a complete solution, including security, data governance, connectivity, operations tooling, monitoring, and support.
• Cloud Integration: In cloud environments where SaaS solutions like Microsoft Fabric, Snowflake, Databricks, and MongoDB Atlas are prevalent, self-managed Kafka may not be the most cost-effective option from a total cost of ownership (TCO) perspective.

In summary, self-managed Apache Kafka does not make much sense in the cloud when you leverage other SaaS like Microsoft Fabric, Snowflake, Databricks, MongoDB Atlas, etc. Also from a TCO perspective.

The Kafka Protocol as Standard for Data Streaming

The Kafka protocol has become a de facto standard for many cloud-native services, such as Azure Event Hubs, Confluent’s KORA Engine or WarpStream. It is the foundation of these cloud services without relying on some or all of the open source Kafka implementation itself to enable a cloud-native experience.

Azure Events Hubs vs. Confluent Cloud for Kafka as a Service

Plenty of Kafka cloud services exist in the meantime. Every large software and cloud vendor has some kind of fully managed or partially managed Kafka cloud offering. Amazon, Microsoft, Google, IBM, Oracle, etc. While Confluent is the leader in the cloud-agnostic Kafka space, there are plenty of other vendors, such as Cloudera, instaclustr, Aiven, Redpanda, Streamnative, to name a few.

Check out the latest data streaming landscape to learn more about all these Kafka (and Flink) vendors and their trade-offs.

The following focuses on a comparison between Azure Event Hubs vs. Confluent Cloud, the two most common options for Kafka on the Azure cloud. Each offers unique advantages and limitations. The following is not a complete list, but the most critical aspects to compare.

Azure Event Hubs – Fully Managed Azure Services Using the Kafka Protocol

Azure Event Hubs is a proprietary, real-time data ingestion service on Microsoft Azure, designed for large-scale data ingestion into lakehouses. While it offers some Kafka API compatibility, it is not a complete replacement for Kafka.

Benefits of Azure Event Hubs

• Fully Managed Service: In contrary to most competing Kafka cloud services, Azure Event hubs is a truly fully managed and not just provisioning some brokers and handing over all the operations, tuning and bug-fixing to the end user.
• Real-Time Data Ingestion: Designed for real-time data ingestion, Event Hubs enables organizations to capture and process data from a wide range of sources, including IoT devices, applications, and cloud services, in real-time.
• Integration with Azure Ecosystem: Event Hubs seamlessly integrates with other Azure services, such as Azure Stream Analytics, Azure Functions, and Azure Data Lake Storage, providing a comprehensive ecosystem for building end-to-end data processing and analytics solutions.

Limitations of Azure Event Hubs

• Partial Kafka Compatibility: Event Hubs supports some Kafka APIs but lags behind in version updates. For instance, it recently added support for the Transaction API and Kafka Streams but is still several Kafka versions behind.
• Scalability Constraints: Event Hubs can elastically scale only to a given quota, with low limits on partitions and latencies exceeding 100ms at gigabytes per second scale.
• Short Data Retention: The Standard tier offers only a 7-day retention policy, making it unsuitable for long-term storage or as a system of record.
• Separate Stream Processing: Requires additional services like Azure HDInsight on AKS for Flink or Azure Stream Analytics for stream processing. The “no code option” available through Azure Stream Analytics is yet another separate PaaS service requiring integration and has its own set of quotas and limitations.
• Cost and total cost of ownership (TCO): Can be high for certain workloads, and because it lacks a complete data streaming platform, it often requires integration with multiple other products to achieve comprehensive functionality.

Confluent Cloud – A Fully Managed Data Streaming Platform powered by Apache Kafka, Flink and Iceberg

Confluent Cloud offers a fully managed data streaming platform powered by Apache Kafka and Flink and integrates seamlessly with the Azure ecosystem. As a strategic Microsoft partner, Confluent provides a unified security, management, and billing experience, with integrations across Azure services.

Benefits of Confluent

• Fully Managed Service: In contrary to most competing Kafka cloud services, Confluent Cloud is a truly fully managed and not just provisioning some brokers and handing over all the operations, tuning and bug-fixing to the end user. In contrast to Azure Event Hubs, it includes an entire data streaming platform, not just the Kafka streaming service.
• Comprehensive Data Streaming Platform: Confluent Cloud’s fully managed service includes various capabilities to stream, process and integrate. It includes data governance and security features for the most critical and data privacy sensitive projects.
• Azure Integration: Pay with Azure cloud credits in the Azure marketplace and enjoy seamless integration with Azure services, including Microsoft Fabric, SQL Data Warehouse, Synapse, Cosmos DB, Databricks Analytics, Azure ML, Azure Data Lake Storage, and Azure Blob Storage.
• Edge, Hybrid and Multi-Cloud: Confluent extends beyond just the Azure cloud, offering seamless deployment options at the edge, on-premises, and across multi-cloud environments to provide unparalleled flexibility and scalability for diverse data streaming needs.
• Data Streaming Expertise: Confluent provides a robust data streaming product, along with unparalleled expertise and comprehensive support and consulting, enabling organizations to effectively leverage data streaming technologies to their fullest potential.
• Innovation: If you want to get the latest features (and security fixes) for data streaming (also beyond just Kafka), Confluent is the only way to go for a cloud service.

Drawbacks of Confluent

• Vendor Lock-in: If you choose any SaaS, you are always locked in. The same is true for any Azure service (including Azure Event Hubs). While the benefits usually exceed, some organizations only choose open source and build everything themselves. Though, with Confluent, you can also migrate across cloud providers. And because Confluent is powered by open source Kafka, you can also migrate back to a vendor-less implementation if you really want to.
• Cost Complexity: Confluent’s fully managed services typically result in a lower TCO with less operational risk than alternatives, though open source or CSP data streaming offerings may appear to have a lower monthly cost before networking and operational management are considered.  Make sure to review all available Confluent Cloud products with your account team to understand the SKUs that make the most for you and to get the best pricing. Different offerings exist for less critical applications, high volume, and small startups. And do a TCO and risk analysis. There is a lot of potential hidden cost (like networking and other cloud provider costs).

Technical Decision: Find the Right Apache Kafka Option for Your Use Cases (Beyond the Lakehouse)

Azure Event Hubs works well as the data ingestion layer into Microsoft Fabric (if you can live with the drawbacks listed above). However, it has many limitations so that you can easily quality out Azure Event Hubs as the right Kafka solution.

“Qualify out” because of product limitations is often much easier than trying to fit several products into an architecture and comparing them.

Choosing the right Kafka option requires careful consideration of your specific use cases. Here are scenarios where Azure Event Hubs may not be suitable:

• Multiple Consumers: Beyond simple lakehouse ingestion, Kafka is usually the data fabric for diverse data sources and sinks, including databases like Oracle and MongoDB, SaaS applications like Salesforce and ServiceNow, and microservices built with Java, Python, JavaScript, and Go.
• Operational and Analytical Use Cases:  Unified data storage and infinite retention with native Apache Iceberg integration is essential for using a data streaming platform for operational and analytical use cases.
• Critical SLAs and/or High Throughput: Transactional workloads require uptime guarantees, low latency (even at scale), and a good disaster recovery strategy across multiple clusters.
• Serverless Stream Processing: Leverage a complete serverless architecture as part of the data streaming platform for efficient stream processing. Implement a shift left architecture for better data quality and reduced costs.
• Data Contracts, Policy Enforcement and Governance: Ensure robust data governance with features like data lineage, data catalog, self-service data portal, audit logs, end-to-end encryption, and data contracts and policy enforcement.

If you have any of the above requirements, it is an easy decision to qualify out Azure Event Hubs. Instead, look at Confluent and other vendors that provide the required capabilities.

Strategic Decision: Data Streaming Organization (Beyond a Lakehouse)

When embarking on a data streaming journey, it’s essential to focus on business value and long-term strategy. Establishing a data streaming organization with a center of excellence can maximize the platform’s strategic value.

Don’t just look at the first use case; a data streaming platform is strategic and adds more value as more people use the same data products. Expertise and 24/7 support are crucial, and Confluent excels in this area focusing dedicatedly on data streaming and a vast customer base. By fostering a data-driven culture, organizations can unlock the full potential of their data streaming investments.

Data Streaming in Azure Cloud: Choose the Right Tool for the Job

Choosing the right data streaming platform – Apache Kafka, Azure Event Hubs, or Confluent Cloud – depends on your specific use case within the Microsoft Fabric Lakehouse and beyond. Apache Kafka offers flexibility and scalability but requires self-management. Azure Event Hubs is a good choice for plain data ingestion into the Azure ecosystem powered by OneLake and Microsoft Fabric, but has limitations in Kafka compatibility and advanced features for a more complex enterprise architecture and especially critical, operational workloads. Confluent Cloud provides a full-featured, managed service with enterprise-level capabilities, making it ideal for strategic deployments across multiple use cases. Each option has its strengths, and careful consideration of your requirements will guide you to the best fit.

What cloud services do you use for data streaming on the Azure cloud? Is the use case just data ingestion into one lakehouse or do you have multiple consumers of the data? Do you also build operational applications with the Apache Kafka ecosystem, maybe including hybrid cloud or disaster recovery scenarios? Let’s connect on LinkedIn and discuss it! Stay informed about new blog posts by subscribing to my newsletter.

The post When to Choose Apache Kafka vs. Azure Event Hubs vs. Confluent Cloud for a Microsoft Fabric Lakehouse appeared first on Kai Waehner.

This is part two of a blog series about Microsoft Fabric and its relation to other data platforms on the Azure cloud:

1. What is Microsoft Fabric for Azure Cloud (Beyond the Buzz) and how it Competes with Snowflake and Databricks
2. How Microsoft Fabric Lakehouse Complements Data Streaming (Apache Kafka, Flink, et al.)
3. When to Choose Apache Kafka vs. Azure Event Hubs vs. Confluent Cloud for a Microsoft Fabric Lakehouse

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Data at Rest (Lakehouse) vs. Data in Motion (Data Streaming)

Data streaming technologies like Apache Kafka and Apache Flink enable continuous data processing while the data is in motion in an event-driven architecture. Data streaming enables immediate insights and seamless integration of data across systems. Kafka provides a robust real-time messaging and persistence platform, while Flink excels in low-latency stream processing, making them ideal for dynamic, stateful applications. A data streaming platform supports operational/transactional and analytical use cases.

Data lakes and data warehouses store data at rest before processing the data. The platforms are optimized for batch processing and long-term analytics, including AI/ML use cases such as model training. Some components provide near real-time capabilities, e.g. data ingestion or dashboards. Data lakes offer scalable, flexible storage for raw data, and data warehouses provide structured, high-performance environments for business intelligence and reporting, complementing the real-time capabilities of streaming technologies. Most leading data platforms provide a unified combination of data lake and data warehouse called lakehouse. Lakehouses are almost exclusively used for analytical workloads as they typically lack the SLAs and tight latency required for operational/transactional use cases.

Data streaming and lakehouses are complementary, with some overlaps but different sweet spots. If you want to learn more, check out these articles:

• Data Warehouse vs. Data Lake vs. Data Streaming – Friends, Enemies, Frenemies?
• Snowflake Data Integration Options for Apache Kafka (including Iceberg)

I also created a short ten minute video explaining the above concepts:

Let’s explore why data streaming and a lakehouse like Microsoft Fabric are complementary (with a few overlaps). I explained in the first blog of this series what Microsoft Fabric is. To understand the differences, it is important to understand what a data streaming platform really is.

Data Streaming in an Event-driven Architecture with Apache Kafka and Flink

There is a lot of confusion in the market. For instance, some folks still compare Apache Kafka to a message broker like RabbitMQ or IBM MQ. I mainly focus on Apache Kafka and Apache Flink as these are the de facto standards for data streaming across industries. Before talking about technologies and solutions, we need to start with the concept of an event-driven architecture as the foundation of data streaming.

Event-driven Architecture for Operational and Analytical Workloads

In today’s digital world, getting real-time data quickly is more important than ever. Traditional methods that process data in batches or via request-response APIs often cannot keep up when you need immediate insights.

Event-driven architecture offers a different approach by focusing on handling events – like transactions or user actions – as they happen. One of the key benefits of an event-driven architecture is its ability to decouple systems, meaning that different parts of a system can work independently. This makes it easier to scale and adapt to changes. An event-driven architecture excels in handling both operational and analytical workloads.

For operational tasks, the event-driven architecture enables real-time data processing, automating processes, enhancing customer experiences, and boosting efficiency. In e-commerce, for example, an event-driven system can instantly update inventory, trigger marketing campaigns, and detect fraud.

On the analytical side, the event-driven architecture allows organizations to derive insights from data as it flows, enabling real-time analytics and trend identification without the delays of batch processing. This is invaluable in sectors like finance and healthcare, where timely insights are crucial.

Building an event-driven architecture with data streaming technologies like Apache Kafka and Apache Flink enhances its potential. These platforms provide the infrastructure for high-throughput, low-latency data streams, enabling scalable and resilient event-driven systems.

Apache Kafka – The De Facto Standard for Event-driven Messaging and Integration

Apache Kafka has become the go-to platform for event-driven messaging and integration, transforming how organizations manage data in motion. Developed by LinkedIn and open-sourced, Kafka is a distributed streaming platform adept at handling real-time data feeds. Over 150,000 organizations use Kafka in the meantime.

Kafka’s architecture is based on a distributed commit log, ensuring data durability and consistency. It decouples data producers and consumers, allowing for flexible and scalable data architectures. Producers publish data to topics, and consumers subscribe independently, facilitating system evolution.

Beyond messaging, Kafka serves as a robust integration platform, connecting diverse systems and enabling seamless data flow. Its ecosystem of connectors allows integration with databases, cloud services, and legacy systems. This helps organizations in modernizing their data infrastructure step-by-step.

Kafka’s stream processing capabilities, through Kafka Streams and integration with Apache Flink, further enhance the value of the streaming data pipelines. Kafka Streams allows real-time data processing within Kafka to enable complex transformations and enrichments, driving data-driven innovation.

Apache Flink – The De Facto Standard for Stream Processing

Apache Flink stands out as the leading framework for stream processing. It offers a versatile platform for streaming ETL and stateful business applications. Flink processes data streams with low latency and high throughput, suitable for diverse use cases.

Flink provides a unified programming model for batch and stream processing that allows developers to use the same API for real-time transactional or analytical batch tasks. This flexibility is a significant advantage as it enables varied data processing without separate tools.

A key feature of Flink is its stateful stream processing. This is crucial for maintaining state across events in real-time applications. Flink’s state management ensures accurate processing in complex scenarios. In contrast to many other stream processing solutions, Flink can do stateful processing even at an extreme scale (i.e., with a throughput of gigabytes per second).

Flink’s event time processing capabilities handle out-of-order or delayed events and ensure consistent results. Developers can define windows and triggers based on event timestamps, accommodating late-arriving data.

Apache Flink supports multiple programming languages, including Java, Python, and SQL, offering developers the flexibility to use their preferred language for building stream processing applications. This is a key differentiator to other stream processing engines, such as Kafka Streams or KSQL.

Kafka and Flink are a “Match Made in Heaven” for Data Streaming

The integration of Flink with Apache Kafka enhances its capabilities. Kafka serves as a reliable data source for Flink to enable seamless real-time data ingestion and processing. With Kafka’s persistent commit log, you can travel back in time and replay historical data in guaranteed ordering for analytical use cases. This combination supports high-volume, low-latency data pipelines, unlocking transactional real-time scenarios and batch analytics.

In summary, Apache Flink’s robust stream processing, combined with Apache Kafka, offers a powerful solution for organizations seeking to leverage real-time data. Whether for operational tasks, real-time analytics, or complex event processing (CEP), Flink provides the necessary flexibility and performance for data-driven innovation.

Microsoft Fabric and Data Streaming (Kafka, Flink): Complementary Forces, NOT Competitors

In the growing landscape of data management, it’s crucial to understand the complementary roles of Microsoft Fabric and data streaming technologies. While some may perceive these technologies as competitors, they actually serve distinct yet interconnected purposes that enhance an organization’s data strategy. And keep in mind that Microsoft Fabric is not just an offering for Azure cloud. Hybrid edge scenarios in the IoT space are perfect for Microsoft Fabric and data streaming together.

Microsoft Fabric’s Streaming Ingestion: A Common Feature Among Lakehouses

Microsoft Fabric, like other modern lakehouse platforms such as Snowflake and Databricks, offers streaming ingestion capabilities. This feature is essential for handling near real-time data flows. It allows organizations to capture and process data as it arrives in the lakehouse. However, it’s important to distinguish between operational and analytical workloads when considering the role of streaming ingestion.

Operational workloads benefit from the immediacy of streaming data, enabling real-time decision-making and process automation. In contrast, analytical workloads often require data to be stored at rest for in-depth analysis and reporting. Microsoft Fabric’s architecture focuses on streaming ingestion into robust storage solutions for analytical purposes.

The Fabric Real Time Intelligence Hub: Understanding Its Capabilities and Limitations

The integration of streaming ingestion into Microsoft Fabric is part of its Real Time Intelligence Hub, which aims to provide a comprehensive platform for managing real-time data. However, beyond the marketing and buzz around Fabric Real Time Intelligence Hub, it’s important to note that it doesn’t operate in true real-time.

Instead, Fabric’s “Real Time Intelligence Hub” uses Spark Streaming jobs to manipulate data, which can introduce some latency. And the infrastructure is not meant for critical SLAs that are required by operational / transactional systems. Additionally, the ingestion process is throttled when using Power BI and other batch analytics tools via an API gateway with a Kafka client.

Microsoft is strong in introducing new names for products or feature for Fabric that are actually just a new brand of existing services. If you find new terms such as “eventhouse” or “event streams feature in the Microsoft Fabric Real-Time Intelligence”, make sure to evaluate if this is really a new component or just some Fabric marketing.

Therefore, despite some overlapping with a data streaming platform, the collaboration between Microsoft Fabric and data streaming vendors like Confluent (Kafka, Flink) underscores the complementary nature of these platforms. By leveraging the strengths of both, organizations can build a robust data infrastructure that supports real-time operations and comprehensive analytics.

In conclusion, Microsoft Fabric and data streaming technologies such as Kafka and Flink are not competitors but complementary tools that, when used together, can significantly enhance an organization’s ability to manage and analyze data. By understanding the distinct roles each plays, businesses can create a more agile and responsive data strategy that meets both operational and analytical needs.

Enterprise Architecture with Data Streaming like Kafka / Flink and Lakehouse(s) like Microsoft Fabric

In the modern enterprise architecture landscape, data streaming and lakehouse platforms are pivotal in creating a robust and flexible data ecosystem. Data streaming technologies enable continuous data ingestion and processing for operational and analytical use cases.

Lakehouse platforms, like Microsoft Fabric, Snowflake and Databricks, provide a unified architecture that combines the best of data lakes and data warehouses, offering scalable storage and advanced analytics capabilities.

Together, these technologies empower businesses to handle both operational and analytical workloads efficiently, breaking down data silos and fostering a data-driven culture. By integrating data streaming with lakehouse architectures, enterprises can achieve seamless data flow and comprehensive insights across their operations.

Reverse ETL is an Anti-Pattern

Reverse ETL is the process of moving data from a data store at rest back into operational systems. It is often considered an anti-pattern in modern data architecture. This approach can lead to data inconsistencies, increased complexity, and higher maintenance costs, as it essentially reverses the natural flow of data in motion. Do NOT store data in MIcrosoft Fabric Lakehouse just to reverse it later into other operational systems!

Instead of relying on reverse ETL, organizations should focus on building real-time data pipelines that enable direct integration between data sources and operational systems. By leveraging an event-driven architecture and data streaming technologies, businesses can ensure that data is consistently updated and available where it’s needed most. This approach not only simplifies data management, but also enhances the accuracy and timeliness of insights.

Apache Iceberg as the De Facto Standard for an Open Table Format – Store Once, Analyze with any Tool

Apache Iceberg has emerged as the de facto standard for an open table format. It offers a opportunity for storing data once in an object store like Amazon S3 and analyzing data across various tools. With its ability to handle large-scale datasets and support ACID transactions, Iceberg provides a reliable and efficient way to manage data in a lakehouse environment.

Organizations can use their preferred analytics and processing tools without being locked into a specific vendor. This flexibility is crucial for businesses looking to maximize their data investments and adapt to changing technological landscapes. By adopting Apache Iceberg together with data streaming, enterprises can ensure data consistency and accessibility across all business units to drive better data-quality, insights and decision-making.

Shift Left Architecture to Support Operational and Analytical Workloads with an Event-driven Architecture

Traditionally, many organizations use data streaming with Kafka as a dumb pipeline to ingest all raw data into a data lake. The consequences are high compute cost for multiple (re-)processing of the raw data, inconsistencies across business units, and slow time to market for new applications.

The Shift Left Architecture is a forward-thinking approach that integrates operational and analytical workloads within an event-driven architecture. By shifting data processing closer to the source, this architecture enables real-time data ingestion and analysis, improving data quality for lakehouse ingestion, reducing latency and improving responsiveness.

Event-driven architectures, powered by technologies like Apache Kafka and Flink, facilitate the seamless flow and processing of data across systems. Shift Left ensures that both operational and analytical needs are met. This approach not only enhances the agility of data-driven applications, but also supports continuous improvement and innovation. By adopting a Shift Left Architecture, organizations can streamline their data processes, improve efficiency, and gain a competitive edge in the market.

Example: Confluent (Data Streaming) + Microsoft Fabric (Lakehouse) + Snowflake (Another Lakehouse)

An example of integrating data streaming and lakehouse technologies is the combination of Confluent, Microsoft Fabric, and Snowflake.

Confluent, built on Apache Kafka and Flink, provides a robust platform for real-time data streaming, enabling organizations to integrate with operational and analytical workloads.

Microsoft Fabric and Snowflake, both lakehouse platforms, offer scalable storage and advanced analytics capabilities to allow businesses performing in-depth analysis and reporting on historical data, near real-time analytics and AI model training.

Apache Iceberg enables storing data once and connects any analytical engine to the data, including lakehouses such as Microsoft Fabric or Snowflake, and unified batch and streaming frameworks such as Apache Flink. Iceberg improves the overall data quality for data sharing, reduces storage cost and enables a much faster rollout of new analytical applications.

By leveraging Confluent for data streaming and integrating it with Microsoft Fabric and Snowflake, organizations can create a comprehensive data architecture that supports both real-time operations and long-term analytics. This synergy not only enhances data accessibility and consistency but also empowers businesses to make data-driven decisions with confidence.

Microsoft Fabric Lakehouse + Data Streaming (Kafka, Flink) = Match Made in Heaven

In conclusion, the synergy between Microsoft Fabric and data streaming technologies like Apache Kafka and Apache Flink creates a powerful combination for modern data management. While Microsoft Fabric excels in providing robust analytics and storage capabilities, data streaming platforms offer real-time data processing and integration, ensuring that businesses can respond swiftly to operational demands.

By leveraging both technologies together, organizations can build a comprehensive data architecture that supports both immediate and long-term needs, enhancing their ability to make informed, data-driven decisions. This complementary relationship not only breaks down data silos, but also fosters a more agile and responsive data strategy. As businesses continue to navigate the complexities of data management, understanding and using the strengths of both Microsoft Fabric and data streaming with data streaming vendors like Confluent will be key to achieving a competitive edge.

The Shift Left Architecture, when paired with Apache Iceberg’s open table format, simplifies the integration of data streaming with one or more lakehouses. This combination enhances data quality for all data consumers and significantly reduces overall storage costs.

In part three of this blog series, I will dig deeper into the data streaming alternatives. When to choose open source frameworks such as Apache Kafka and Flink, a leading data streaming platform such as Confluent, or a native Azure service like Event Hubs. Primer: The trade-offs are huge. Do a proper evaluation BEFORE choosing your data streaming solution.

How do you see the combination of a lakehouse like Microsoft Fabric with data streaming? Do you already use both together? And what is your strategy for other data lakes and data warehouses you already have in your enterprise architecture, such as Databricks or Snowflake? Let’s connect on LinkedIn and discuss it! Stay informed about new blog posts by subscribing to my newsletter.

The post How Microsoft Fabric Lakehouse Complements Data Streaming (Apache Kafka, Flink, et al.) appeared first on Kai Waehner.

This is part one of a blog series about Microsoft Fabric and its relation to other data platforms on the Azure cloud:

1. What is Microsoft Fabric for Azure Cloud (Beyond the Buzz) and how it Compares (or Competes) with Snowflake and Databricks
2. How Microsoft Fabric Complements Data Streaming (Apache Kafka, Flink, et al.)
3. When to Choose Apache Kafka vs. Azure Event Hubs vs. Confluent Cloud for a Microsoft Fabric Lakehouse

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What is Microsoft Fabric?

If you listen to Microsoft’s sales and marketing, then Microsoft Fabric is a silver bullet for every use case. Let’s take a two-step approach. Look at the sales and marketing definition. Then deconstruct it a bit from a more realistic point of view…

GenAI Definition (= Sales and Marketing)

If you ask your favourite large language model or search engine, it you something like the following when I ask “What is Microsoft Fabric” (based on sales and marketing content):

“Microsoft Fabric is an end-to-end analytics platform designed to integrate various data services and enable businesses to manage, analyze, and act on their data seamlessly. It was launched as part of Microsoft’s data ecosystem and builds upon key features from platforms like Power BI, Azure Synapse Analytics, and Azure Data Factory.

Here are some key aspects of Microsoft Fabric:

• Unified Platform: It combines data engineering, data science, data warehousing, and real-time analytics into a single platform. This helps businesses eliminate the need to use multiple services for data management and analysis.
• Lakehouse Architecture: Fabric is designed around the lakehouse concept, which merges the best of data lakes and data warehouses. It allows for both structured and unstructured data to be stored and processed together.
• Tightly Integrated with Microsoft 365 and Azure: Microsoft Fabric connects seamlessly with other Microsoft services like Microsoft 365, Power BI, and Azure Machine Learning, enabling better collaboration, reporting, and AI-driven insights.
• Low-code/No-code Experience: The platform provides intuitive tools for data analysts, developers, and business users, allowing non-technical users to work with data through drag-and-drop interfaces, while also enabling more complex scenarios for advanced users.
• AI and Machine Learning Integration: Microsoft Fabric incorporates AI tools, making it easier for businesses to build predictive models and automate data-driven decisions.
• End-to-End Security and Governance: The platform supports robust security measures and compliance requirements, offering features like data encryption, role-based access control, and regulatory compliance support.
• Real-time Data Processing: With support for real-time analytics, Fabric enables organizations to derive insights from live data streams, improving decision-making speed and accuracy.

Microsoft Fabric is designed to streamline how businesses use data, combining the power of analytics with cloud-scale capabilities.”

Wow. Just wow. Microsoft Fabric seems to be everything you ever need for your data challenges.

Microsoft Developer has an excellent 45 minute presentation about OneLake and Microsoft Fabric with a few more technical details. This video is also the source of the screenshots below.

Well, let’s dig deeper. What is Microsoft Fabric really? Let’s deconstruct it a bit…

Microsoft Fabric is a Data Analytics Platform ( = NOT for Operational / Transactional Workloads)

Microsoft Fabric is part of Microsoft’s data analytics portfolio. That’s already the first alarm signal when you consider building operational workloads. This is not a criticism, but important to understand!

Microsoft Fabric is NOT a platform for transactional workloads like payments, fraud detection, order management or ERP integration. You should not build an operational application like an Azure Serverless Function or a self-managed Spring Boot container for Fabric.

Furthermore, within the data analytics layer, the foundation of Microsoft Fabric is (only) an optimized storage layer. And this storage layer called OneLake is a SaaS offering, i.e., the storage is part of the Microsoft tenant. Contrary to many other data lakes and lakehouses like Databricks, you do not control or own the storage.

While the conversation is usually around cloud analytics, Microsoft Fabric is a unified analytics platform that integrates with Azure Cloud but is sold independently. This allows organizations to deploy it in various environments, edge, and hybrid setups. For instance, Microsoft sells Fabric for hybrid IoT projects where data needs to be processed both locally and in the cloud.

OneLake – Cloud-based Storage Layer on Top of Azure Data Lake Storage (ADLS)

Microsoft OneLake is a unified, cloud-based data lake that acts as the central storage layer within Microsoft Fabric:

Microsoft OneLake is built on top of Azure Data Lake Storage (ADLS), using its scalable and secure data storage capabilities for long-term data retention. OneLake inherits ADLS’s features like hierarchical namespaces and advanced security, while adding a unified data lake experience across multiple clouds and deep integration with Microsoft’s analytics and data tools through Microsoft Fabric.

The message is obvious: Store all data in OneLake and connect your favourite compute engines, such as Microsoft Fabric, Azure Databricks and Snowflake. Open Table Formats like Delta Lake and Apache Iceberg allow simple integration without the need to copy data again.

Microsoft Fabric Connects to Many Existing Azure Services

On top of the storage layer OneLake, Microsoft Fabric connects to plenty of different existing Microsoft Azure services, including Power BI, Data Explorer, various Synapse services, and so on. This explains why Microsoft Fabric can magically provide every capability you are looking for a few months after the initial announcement.

Here are a few integrations of Azure services into the unified storage of Microsoft Fabric:

• Power BI: A critical component of Microsoft Fabric, enabling data visualization and business intelligence. It allows users to create interactive dashboards and reports directly from data stored in the lakehouse, providing real-time insights with minimal data movement.
• Azure Data Explorer: Used for analyzing large volumes of streaming and historical data. Microsoft Fabric connects to Data Explorer, allowing users to perform fast, complex, real-time queries on structured and semi-structured data.
• Azure Synapse Analytics: Fabric integrates Synapse’s data engineering capabilities, allowing users to prepare, transform, and orchestrate data pipelines. It provides a unified workspace to manage end-to-end data engineering workflows, reducing the need for complex data movement.
• Synapse Data Warehousing: Fabric connects with Synapse’s data warehousing services, making it easy to run massively parallel processing (MPP) queries for large-scale analytics on structured data.
• Synapse Spark Pools: Fabric integrates with Apache Spark in Synapse, supporting big data processing, AI, and machine learning workloads. Users can leverage Spark’s distributed computing power within Fabric for data transformation, advanced analytics, and machine learning.
• Azure Machine Learning (AML): Enables data scientists to build, train, and deploy machine learning models on data stored within the Fabric lakehouse. Users can perform machine learning experiments, automate ML model training, and deploy models with an unified data platform.
• Azure Data Factory: Used for data ingestion, ETL (extract, transform, load), and data orchestration. Fabric connects with Azure Data Factory, making it easy to create data pipelines that move and transform data from a wide variety of sources, including on-premises databases, cloud storage, and third-party systems.
• Azure Purview: Provides a unified data catalog, allowing users to discover, classify, and govern data assets across the Fabric ecosystem. It also provides compliance and auditing capabilities.
• Azure Event Hubs and Stream Analytics: Real-time data processing and analytics. Event Hubs enables streaming data ingestion from sources like IoT devices, applications, and logs, while Stream Analytics allows for real-time data querying and analysis.

Expect more Azure services to be integrated with Microsoft Fabric in the coming months to provide a “complete lakehouse experience”. Also expect more fancy marketing brands, such as the new “Real Time Intelligence Hub” that is built by connecting / re-using existing Microsoft Azure services.

So, what is the main idea behind building this lakehouse product and brand within Microsoft’s huge cloud portfolio?

Microsoft Fabric is a Lakehouse Competing with Snowflake and Databricks

A lakehouse is a data architecture that combines the features of data lakes and data warehouses, allowing for both structured and unstructured data to be stored and processed together. It provides the scalability and flexibility of a data lake with the data management, governance, and performance features of a data warehouse. This unified approach enables real-time analytics and machine learning on diverse types of data, reducing the need for separate infrastructures.

Most analytical data vendors transition to a full-blown lakehouse. While Databricks moved from the data lake foundation powered by Apache Spark into the lakehouse, Snowflake comes from the data warehouse approach but has incorporated a lot of lakehouse features over time (even though Snowflake calls it a more general “data cloud”).

Microsoft Fabric competes with platforms like Databricks and Snowflake in the realm of data analytics, data engineering, and data warehousing by providing an integrated, cloud-native solution for data management and analytics.

Microsoft Fabric positions itself as a more holistic and integrated platform, offering a unified solution for businesses that need to handle everything from data ingestion to real-time analytics and AI. Its Microsoft ecosystem integration is a key competitive advantage.

There are also trade-offs. For instance:

• Microsoft Fabric is only available on Azure cloud
• Not a mature product yet
• Starting a much more competitive approach with strategic partners like Databricks

The support of open table formats like Delta Lake and Apache Iceberg is great. But this is coming in all lakehouses because of market pressure. Not because the data cloud vendors like Databricks, Snowflake and now Microsoft with Fabric have a new business model. All of these vendors still want to collect all the data, store it forever, and put (their own!) compute services on top.

Microsoft Fabric is Azure’s Future Lakehouse

Microsoft Fabric’s integration with many Azure services allows it to offer a broad range of capabilities – from data ingestion, storage, and transformation to real-time analytics, machine learning, and governance. This interconnected ecosystem explains how Fabric can quickly meet diverse enterprise needs by leveraging Microsoft’s existing suite of powerful tools, providing a comprehensive data platform with minimal friction and seamless workflows.

In the end, Microsoft Fabric is a new lakehouse built on top of the optimized cloud storage OneLake. It directly competes with other lakehouses and data clouds such as Databricks and Snowflake to become the leading unified solution for all the things analytics. The future will show where this competition goes. Snowflake and Databricks have a very strong product and customer base already. They will not give up to Microsoft Fabric voluntarily.

Microsoft Fabric includes integrations with Azure Event Hubs (based on the Kafka protocol) and is building a brand around real-time intelligence. In the next article of this blog series, I will explore how this new lakehouse on Azure cloud competes or overlaps with data streaming technologies such as Apache Kafka, Flink, et al. Primer: Data Streaming and Microsoft Fabric are mostly complementary and have very different sweet spots.

How do you see the future of Microsoft Fabric? Do you already use it? What is the plan in the future, also keeping in mind that you likely already have other lakehouses in your enterprise architecture? Let’s connect on LinkedIn and discuss it! Stay informed about new blog posts by subscribing to my newsletter.

The post What is Microsoft Fabric for Azure Cloud (Beyond the Buzz) and how it Competes with Snowflake and Databricks appeared first on Kai Waehner.