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.

The Data Streaming Landscape: Kafka, Flink, Cloud, and More

The Data Streaming Landscape 2025 highlights how data streaming has evolved into a key software category, moving from niche adoption to a fundamental part of modern data architecture.

With frameworks like Apache Kafka and Flink at its core, the landscape now spans self-managed, BYOC, and fully managed SaaS solutions, driving real-time use cases, unifying transactional and analytical workloads, and enabling innovation across industries.

If you’re still grappling with the fundamentals of stream processing, this article is a must-read: “Stateless vs. Stateful Stream Processing with Kafka Streams and Apache Flink“.

What is SaaS in Data Streaming?

SaaS data streaming solutions are fully managed services where the provider handles all aspects of deployment, maintenance, scaling, and updates. SaaS offerings are designed for ease of use, providing a serverless experience where developers focus solely on building applications rather than managing infrastructure.

Characteristics of SaaS in Data Streaming

1. Serverless Architecture: Resources scale automatically based on demand. True SaaS solutions eliminate the need to provision or manage servers.
2. Low Operational Overhead: The provider manages hardware, software, and runtime configurations, including upgrades and security patches.
3. Pay-As-You-Go Pricing: Consumption-based pricing aligns costs directly with usage, reducing waste during low-demand periods.
4. Rapid Deployment: SaaS enables users to start processing streams within minutes, accelerating time-to-value.

Examples of SaaS in Data Streaming:

• Confluent Cloud: A fully managed Kafka platform offering serverless scaling, multi-tenancy, and a broad feature set for both stateless and stateful processing.
• Amazon Kinesis Data Analytics: A managed service for real-time analytics with automatic scaling.

What is PaaS in Data Streaming?

PaaS offerings sit between fully managed SaaS and infrastructure-as-a-service (IaaS). These solutions provide a platform to deploy and manage applications but still require significant user involvement for infrastructure management.

Characteristics of PaaS in Data Streaming

1. Partial Management: The provider offers tools and frameworks, but users must manage servers, clusters, and scaling policies.
2. Manual Configuration: Deployment involves provisioning VMs or containers, tuning parameters, and monitoring resource usage.
3. Complex Scaling: Scaling is not always automatic; users may need to adjust resource allocation based on workload changes.
4. Higher Overhead: PaaS requires more expertise and operational involvement, making it less accessible to teams without dedicated DevOps resources.

Examples of PaaS in Data Streaming (Kafka, Flink)

PaaS offerings in data streaming, while simplifying some infrastructure tasks, still require significant user involvement compared to fully serverless SaaS solutions. Below are three common examples, along with their benefits and pain points compared to serverless SaaS:

• Apache Flink (Self-Managed on Kubernetes Cloud Service like EKS, AKS or GKE)
  • Benefits: Full control over deployment and infrastructure customization.
  • Pain Points: High operational overhead for managing Kubernetes clusters, manual scaling, and complex resource tuning.
• Amazon Managed Service for Apache Flink (Amazon MSF)
  • Benefits: Simplifies infrastructure management and integrates with some other AWS services.
  • Pain Points: Users still handle job configuration, scaling optimization, and monitoring, making it less user-friendly than serverless SaaS solutions.
• Amazon MSK (Managed Streaming for Apache Kafka)
  • Benefits: Eases Kafka cluster maintenance and integrates with the AWS ecosystem.
  • Pain Points: Requires users to design and manage producers/consumers, manually configure scaling, and handle monitoring responsibilities. MSK also excludes support for Kafka if you have any operational issues with the Kafka piece of the infrastructure.

SaaS vs. PaaS: Key Differences

SaaS and PaaS differ in the level of management and user responsibility, with SaaS offering fully managed services for simplicity and PaaS requiring more user involvement for customization and control.

The big benefit of PaaS over SaaS is greater flexibility and control, allowing users to customize the platform, integrate with specific infrastructure, and optimize configurations to meet unique business or technical requirements. This level of control is often essential for organizations with strict compliance, security, or data sovereignty requirements.

SaaS is NOT Always Better than PaaS!

Be careful: The limitations and pain points of PaaS do NOT mean that SaaS is always better.

A concrete example: Amazon MSK Serverless simplifies Apache Kafka operations with automated scaling and infrastructure management but comes with significant limitations, including the lack of fully-managed connectors, advanced data governance tools, and native multi-language client support.

Amazon MSK also excludes Kafka engine support, leading to potential operational risks and cost unpredictability, especially when integrating with additional AWS services for a complete data streaming pipeline. I explored these challenges in more detail in my article “When NOT to choose Amazon MSK Serverless for Apache Kafka?“.

Bring Your Own Cloud (BYOC) as Alternative to PaaS

BYOC (Bring Your Own Cloud) offers a middle ground between fully managed SaaS and self-managed PaaS solutions, allowing organizations to host applications in their own VPCs.

BYOC provides enhanced control, security, and compliance while reducing operational complexity. This makes BYOC a strong alternative to PaaS for companies with strict regulatory or cost requirements.

As an example, here are the options of Confluent for deploying the data streaming platform: Serverless Confluent Cloud, Self-managed Confluent Platform (some consider this a PaaS if you leverage Confluent’s Kubernetes Operator and other automation / DevOps tooling) and WarpStream as BYOC offering:

While BYOC complements SaaS and PaaS, it can be a better choice when fully managed solutions don’t align with specific business needs. I wrote a detailed article about this topic: “Deployment Options for Apache Kafka: Self-Managed, Fully-Managed / Serverless and BYOC (Bring Your Own Cloud)“.

“Serverless” Claims: Don’t Trust the Marketing

Many cloud data streaming solutions are marketed as “serverless,” but this term is often misused. A truly serverless solution should:

1. Abstract Infrastructure: Users should never need to worry about provisioning, upgrading, or cluster sizing.
2. Scale Transparently: Resources should scale up or down automatically based on workload.
3. Eliminate Idle Costs: There should be no cost for unused capacity.

However, many products marketed as serverless still require some degree of infrastructure management or provisioning, making them closer to PaaS. For example:

• A so-called “serverless” PaaS solution may still require setting initial cluster sizes or monitoring node health.
• Some products charge for pre-provisioned capacity, regardless of actual usage.

Do Your Own Research

When evaluating data streaming solutions, dive into the technical documentation and ask pointed questions:

• Does the solution truly abstract infrastructure management?
• Are scaling policies automatic, or do they require manual configuration?
• Is there a minimum cost even during idle periods?

By scrutinizing these factors, you can avoid falling for misleading “serverless” claims and choose a solution that genuinely meets your needs.

Choosing the Right Model for Your Data Streaming Business: SaaS, PaaS, or BYOC

When adopting a data streaming platform, selecting the right model is crucial for aligning technology with your business strategy:

• Use SaaS (Software as a Service) if you prioritize ease of use, rapid deployment, and operational simplicity. SaaS is ideal for teams looking to focus entirely on application development without worrying about infrastructure.
• Use PaaS (Platform as a Service) if you require deep customization, control over resource allocation, or have unique workloads that SaaS offerings cannot address.
• Use BYOC (Bring Your Own Cloud) if your organization demands full control over its data but sees benefits in fully managed services. BYOC enables you to run the data plane within your cloud VPC, ensuring compliance, security, and architectural flexibility while leveraging SaaS functionality for the control plane .

In the rapidly evolving world of data streaming around Apache Kafka and Flink, SaaS data streaming platforms like Confluent Cloud provide the best of both worlds: the advanced features of tools like Apache Kafka and Flink, combined with the simplicity of a fully managed serverless experience. Whether you’re handling stateless stream processing or complex stateful analytics, SaaS ensures you’re scaling efficiently without operational headaches.

What deployment option do you use today for Kafka and Flink? Any changes planned in the future? Let’s connect on LinkedIn and discuss it! Stay informed about new blog posts by subscribing to my newsletter.

The post Fully Managed (SaaS) vs. Partially Managed (PaaS) Cloud Services for Data Streaming with Kafka and Flink 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

Subscribe to my newsletter to get an email about a new blog post every few weeks.

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.

I look at trends for digital natives to explore how data streaming helps as a business enabler, including customer stories from New Relic, Wix, Expedia, Apna, Grab, and more. A complete slide deck and on-demand video recording are included.

General trends for digital natives

Digital natives are data-driven tech companies born in the cloud. The SaaS solutions are built on cloud-native infrastructure that provides elastic and flexible operations and scale. AI and Machine Learning improve business processes while the data flows through the backend systems.

The data-driven enterprise in 2023

McKinsey & Company published an excellent article on seven characteristics that will define the data-driven enterprise:

1) Data embedded in every decision, interaction, and process
2) Data is processed and delivered in real-time
3) Flexible data stores enable integrated, ready-to-use data
4) Data operating model treats data like a product
5) The chief data officer’s role is expanded to generate value
6) Data-ecosystem memberships are the norm
7) Data management is prioritized and automated for privacy, security, and resiliency

This quote from McKinsey & Company precisely maps to the value of data streaming for using data at the right time in the right context. The below success stories are all data-driven, leveraging these characteristics.

Digital natives born in the cloud

A digital native enterprise can have many meanings. IDC has a great definition:

“IDC defines Digital native businesses (DNBs) as companies built based on modern, cloud-native technologies, leveraging data and AI across all aspects of their operations, from logistics to business models to customer engagement. All core value or revenue-generating processes are dependent on digital technology.”

Companies are born in the cloud, leverage fully managed services, and are consequently innovative with fast time to market.

AI and machine learning (beyond the buzz)

“ChatGPT, while cool, is just the beginning; enterprise uses for generative AI are far more sophisticated.” says Gartner. I can’t agree more. But even more interesting: Machine Learning (the part of AI that is enterprise ready) is already used in many companies.

While everybody talks about Generative AI (GenAI) these days, I prefer talking about real-world success stories that leverage analytic models for many years already to detect fraud, upsell to customers, or predict machine failures. GenAI is “just” another more advanced model that you can embed into your IT infrastructure and business processes the same way.

Data streaming at digital native tech companies

Adopting trends across industries is only possible if enterprises can provide and correlate information correctly in the proper context. Real-time, which means using the information in milliseconds, seconds, or minutes, is almost always better than processing data later (whatever later means):

Digital natives combine all the power of data streaming: Real-time messaging at any scale with storage for true decoupling, data integration, and data correlation capabilities. Apache Kafka is the de facto standard for data streaming.

Data streaming with the Apache Kafka ecosystem and cloud services are used throughout the supply chain of any industry. Here are just a few examples:

Search my blog for various articles related to this topic in your industry: Search Kai’s blog.

Elastic scale with cloud-native infrastructure

One of the most significant benefits of cloud-native SaaS offerings is elastic scalability out of the box. Tech companies can start new projects with a small footprint and pay-as-you-go. If the project is successful or if industry peaks come (like Black Friday or Christmas season in retail), the cloud-native infrastructure scales up; and back down after the peak:

There is no need to change the architecture from a proof of concept to an extreme scale. Confluent’s fully managed SaaS for Apache Kafka is an excellent example. Learn how to scale Apache Kafka to 10+ GB per second in Confluent Cloud without the need to re-architect your applications.

Data streaming + AI / machine learning = real-time intelligence

The combination of data streaming with Kafka and machine learning with TensorFlow or other ML frameworks is nothing new. I explored how to “Build and Deploy Scalable Machine Learning in Production with Apache Kafka” in 2017, i.e., six years ago.

Since then, I have written many further articles and supported various enterprises deploying data streaming and machine learning. Here is an example of such an architecture:

Data mesh for decoupling, flexibility and focus on data products

Digital natives don’t (have to) rely on monolithic, proprietary, and inflexible legacy infrastructure. Instead, tech companies start from scratch with modern architecture. Domain-driven design and microservices are combined in a data mesh, where business units focus on solving business problems with data products:

Architecture trends for data streaming used by digital natives

Digital natives leverage trends for enterprise architectures to improve cost, flexibility, security, and latency. Four essential topics I see these days at tech companies are:

• Decentralization with a data mesh
• Kappa architecture replacing Lambda
• Global data streaming
• AI/Machine Learning with data streaming

Let’s look deeper into some enterprise architectures that leverage data streaming.

Decentralization with a data mesh

There is no single technology or product for a data mesh!  But the heart of a decentralized data mesh infrastructure must be real-time, reliable, and scalable.

Data streaming with Apache Kafka is the perfect foundation for a data mesh: Dumb pipes and smart endpoints truly decouple independent applications. This domain-driven design allows teams to focus on data products:

Contrary to a data lake or date warehouse, the data streaming platform is real-time, scalable and reliable. A unique advantage for building a decentralized data mesh.

Kappa architecture replacing Lambda

The Kappa architecture is an event-based software architecture that can handle all data at all scale in real-time for transactional AND analytical workloads.

The central premise behind the Kappa architecture is that you can perform real-time and batch processing with a single technology stack. The heart of the infrastructure is streaming architecture.

Unlike the Lambda Architecture, in this approach, you only re-process when your processing code changes and need to recompute your results.

I wrote a detailed article with several real-world case studies exploring why “Kappa Architecture is Mainstream Replacing Lambda“.

Global data streaming

Multi-cluster and cross-data center deployments of Apache Kafka have become the norm rather than an exception.

Several scenarios require multi-cluster Kafka deployments with specific requirements and trade-offs, including disaster recovery, aggregation for analytics, cloud migration, mission-critical stretched deployments, and global Kafka.

Learn all the details in my article “Architecture patterns for distributed, hybrid, edge and global Apache Kafka deployments“.

Natural language processing (NLP) with data streaming for real-time Generative AI (GenAI)

Natural Language Processing (NLP) helps many projects in the real world for service desk automation, customer conversation with a chatbot, content moderation in social networks, and many other use cases. Generative AI (GenAI) is “just” the latest generation of these analytic models. Many enterprises have combined NLP with data streaming for many years for real-time business processes.

Apache Kafka became the predominant orchestration layer in these machine learning platforms for integrating various data sources, processing at scale, and real-time model inference.

Here is an architecture that shows how teams easily add Generative AI and other machine learning models (like large language models, LLM) to their existing data streaming architecture:

Time to market is critical. AI does not require a completely new enterprise architecture. The true decoupling allows the addition of new applications/technologies and embedding them into the existing business processes.

An excellent example is Expedia: The online travel company added a chatbot to the existing call center scenario to reduce costs, increase response times, and make customers happier. Learn more about Expedia and other case studies in my blog post “Apache Kafka for Conversational AI, NLP, and Generative AI“.

New customer stories of digital natives using data streaming

So much innovation is happening with data streaming. Digital natives lead the race. Automation and digitalization change how tech companies create entirely new business models.

Most digital natives use a cloud-first approach to improve time-to-market, increase flexibility, and focus on business logic instead of operating IT infrastructure. And elastic scalability gets even more critical when you start small but think big and global from the beginning.

Here are a few customer stories from worldwide telecom companies:

• New Relic: Observability platform ingesting up to 7 billion data points per minute for real-time and historical analysis.
• Wix: Web development services with online drag & drop tools built with a global data mesh.
• Apna: India’s largest hiring platform powered by AI to match client needs with applications.
• Expedia: Online travel platform leveraging data streaming for a conversational chatbot service incorporating complex technologies such as fulfillment, natural-language understanding, and real-time analytics.
• Alex Bank: A 100% digital and cloud-native bank using real-time data to enable a new digital banking experience.
• Grab: Asian mobility service that built a cybersecurity platform for monitoring 130M+ devices and generating 20M+ Al-driven risk verdicts daily.

Resources to learn more

This blog post is just the starting point. Learn more about data streaming and digital natives in the following on-demand webinar recording, the related slide deck, and further resources, including pretty cool lightboard videos about use cases.

On-demand video recording

The video recording explores the telecom industry’s trends and architectures for data streaming. The primary focus is the data streaming case studies. Check out our on-demand recording:

Slides

If you prefer learning from slides, check out the deck used for the above recording:

Data streaming case studies and lightboard videos of digital natives

The state of data streaming for digital natives in 2023 is fascinating. New use cases and case studies come up every month. This includes better data governance across the entire organization, real-time data collection and processing data from network infrastructure and mobile apps, data sharing and B2B partnerships with new business models, and many more scenarios.

We recorded lightboard videos showing the value of data streaming simply and effectively. These five-minute videos explore the business value of data streaming, related architectures, and customer stories. Stay tuned; I will update the links in the next few weeks and publish a separate blog post for each story and lightboard video.

And this is just the beginning. Every month, we will talk about the status of data streaming in a different industry. Manufacturing was the first. Financial services second, then retail, telcos, digital natives, gaming, and so on…

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.

The post The State of Data Streaming for Digital Natives in 2023 appeared first on Kai Waehner.

Disclaimer: I work for Confluent. However, the post is not about comparing features but explaining the concepts behind the alternatives. I talk to enterprises across the globe every week. I can assure you that many people I talk to are not aware or mislead about what you read in the following sections. Hence, I hope that the following helps you to make the right decision. Either choose to run open-source Apache Kafka or one of the various commercial Kafka offerings, or even a combination of both.

UPDATE (August 2022): This blog post was written before Amazon MSK Serverless was released. The below is still accurate and worth a read for comparing Kafka products and cloud services. Additionally, please check out the article “When NOT to choose Amazon MSK Serverless for Apache Kafka?“

Apache Kafka Components and Use Cases

The goal is not to introduce Kafka here. The minimum you should know is that Kafka is NOT just a messaging layer for data ingestion into a data lake. This is just a fraction of today’s usages.

Kafka is an open-source framework under Apache 2.0 license. It provides a combination of messaging, storage, processing, and integration of high volumes of data at scale in real-time and fault-tolerant. That’s what makes Kafka unique compared to other MQ, ETL, ESB, and API platforms.

Kafka is deployed in production for various use cases across industries. This includes analytical and mission-critical workloads. Different deployments require different SLAs. You should always ask yourself what happens if the Kafka infrastructure is in trouble. What are your RTO (Recovery Time Objective) and RPO (Recovery Point Objective)? Or in other words: How much data is okay to lose? How much downtime is acceptable? Start your Kafka projects with these questions in mind when you start your comparison of the options!

Kafka is the De Facto Standard API for Event Streaming like S3 API for Object Storage

Apache Kafka is mainstream! The latest proof: Check out the new ThoughtWorks Technology Radar: “Kafka API without Kafka“:

Kafka became the de facto event streaming API. Similar to S3 API became the de facto standard for object storage. Actually, the situation is even better for the Kafka API as the S3 API is a proprietary protocol from AWS. In contrast, the Kafka API and protocol are open source under Apache 2.0 license.

Check out the blog “Kafka API is the De Facto Standard API for Event Streaming like Amazon S3 for Object Storage” for more details.

Let’s take a look at a few very different Kafka alternatives available today:

• Open-source Apache Kafka from the Apache website under Apache 2.0 license
• Self-managed vendor offerings from Confluent, Cloudera, Red Hat, Amazon MSK, and many more
• Fully-managed cloud offerings such as Confluent Cloud
• Partly protocol-compatible products such as RedPanda for embedded / WebAssembly (WASM) use cases
• Partly protocol-compatible SaaS offerings like Azure EventHubs

That’s a lot of options. So, how do you make a Kafka comparison to choose the right one? Before we go into more detail, let’s explore how complex Kafka actually is and when you do have to care about this at all.

Should you care how complex or heavyweight your event streaming technology is?

Complexity matters (only) if you need to operate the infrastructure by yourself. The beauty of SaaS is that you just consume the service and focus on your business problems. For instance, the AWS S3 object storage is a simple API with a fully managed service under the hood. You do not need to worry about operations or monitoring. You just use the cloud service.

Having said this, it is a little bit strange that ThoughtWorks mentions the barriers and complexity of Kafka but then refers to the Pulsar wrapper. That is an (immature) single class mapping implementation that only maps a small part of Kafka’s protocol (here is the producer wrapper as an example). Developers can use that wrapper to move data between Kafka clients and Pulsar brokers. However, Pulsar has a much more complex three-tier distributed architecture with ZooKeeper, BookKeeper, and Pulsar clusters. What is the benefit here? Is this really what you want to do in mission-critical workloads? Why? Please let me know if you seriously consider using such a wrapper architecture. Also, please read my post about the “Myths of Kafka vs. Pulsar“. A lot of arguments like the Kafka wrapper are simply just marketing and not usable for real-world projects!

Therefore, when I think about using the Kafka API without operating Kafka, then I have fully managed SaaS offerings such as Confluent Cloud or Azure Event Hubs in my mind.

Having said this, a fully managed cloud service is not always an option. For instance, Kafka at the edge is the new black. Plenty of use cases exist for a single broker or highly-available Kafka clusters at the edge.

But even if you want or need to operate Kafka by yourself: With KIP-500 and the removal of ZooKeeper, it gets easier and more lightweight than ever before. A lot of arguments do not exist anymore to move to a more “lightweight alternative”. There might be good reasons to choose something like RedPanda. But the main argument of having a more simple and lightweight deployment is not given anymore. Check out this video showing Kafka without ZooKeeper.

How to Choose the Right Kafka Distribution or Cloud Service?

So, how to make a comparison to find out which Kafka distribution or cloud service is the right one for your project?

The answer is simpler than you might think: The ultimate goal is to focus and solve your business problem.

How do you do that? By implementing business logic. Ideally, you don’t have to worry about infrastructure, operations, security, scalability, reliability, and non-business characteristics. Hence, SaaS with fully managed Kafka should be the first choice.

Unfortunately, SaaS is not always possible or the best option for many reasons:

• Missing features
• Technical limitations
• Cost
• Security requirements
• On-premise or edge use cases

Therefore, we need to go one step back and understand what options you have to deploy and operate Kafka. Understanding these concepts without all the marketing fluff from the vendors is crucial to make the right decision!

The Kafka Car: An Analogy for a Product Comparison

It is often easier to compare technology by using an analogy from real life. Something everybody understands. No matter what industry you are in. No matter how technical you are. First, I thought I use the analogy of pizza, including self-made pizza, pizza ingredients, restaurants, delivery services, and other related topics. But pizza is used so often in the IT world. This originated in the early days of Amazon. Jeff Bezos instituted a rule: Every internal team should be small enough to be fed with two pizzas.

Finally, I choose to use the analogy of a car because I think many of the arguments are less debatable this way. I guess we could never agree on what would be the best pizza option for most people…

Hence, let’s talk about car engines, car brands, self-driving, connected fleets, and vintage vehicles in the following sections.

Give me a self-driving car, please!

Obviously, most people would prefer a self-driving car (if the price is right). It is safe, cost-efficient, and comfortable.

In the Kafka context, this means that the Kafka infrastructure would be

• cloud-native (= elastic, scalable and automated, ideally fully managed)
• complete (= entire set of security and operational features that enterprises require)
• everywhere (= available in multiple public clouds, private cloud, on-premise, edge outside the data center)

Unfortunately, not every Kafka setup can be self-driving. We need to disassemble a car into its parts to understand what’s going on under the hood. Then we can choose the right car for our business problem.

Car Brands: Comparison of Confluent, Cloudera, Red Hat, Amazon MSK

Competition creates innovation. Hence, it is great to see many car brands and car models on the streets. Similarly, many competing companies fight for market share around Kafka business. Let’s quickly think about the available car brands (= Kafka vendors) on the market.

I only focus on the most relevant ones that either care about the Kafka project and community, have a lot of market power, or ideally both.

The car brands are Confluent, Cloudera, Red Hat, and Amazon MSK. I have a section on other Kafka and non-Kafka streaming vendors at the end of the blog post to provide a more detailed comparison.

Again, the idea is NOT to have a feature-by-feature comparison or flame war. The following are a few facts about each vendor. I only focus on Kafka-related points. Hence, it is no surprise that Confluent looks best in the following list as they only focus on event streaming. But obviously, each vendor has strengths and weaknesses. For instance, if you want to discuss the overall cloud infrastructure capabilities and strategy, well, then AWS would look much stronger than all the other Kafka vendors…

Confluent – The Leading Apache Kafka Vendor

A few facts about Confluent:

• Focus on event streaming
• Original creators of Kafka
• The main contributor to the Apache Kafka project with 80% of Kafka commits
• Always the latest Kafka version (without limitations) and full support
• Rich Kafka ecosystem (connectors, governance, security, etc.)
• Hybrid architectures (including the only true fully-managed and complete Kafka service)
• Partnership and 1st party integration into cloud providers (AWS, GCP, Azure) – e.g., you can use your cloud provider credits and account to consume Confluent Cloud
• Certified for self-managed operations on cloud providers’ edge offerings (e.g., AWS Outpost including Wavelength, Google’s Anthos)

Cloudera – Big Data Analytics Suite

A few facts about Cloudera:

• Focus on big data analytics
• Provides a platform around tens of different big data frameworks for storage, batch, and real-time analytics
• Kafka is part of the platform (Hadoop, Spark, Flume, Flink, many more) with tooling and support for the whole platform
• Hybrid architectures (but no fully-managed Kafka service)
• Partnership and 3rd party integration into cloud providers (AWS, GCP, Azure)

Red Hat (IBM) – Cloud-native PaaS Infrastructure

A few facts about Red Hat (IBM):

• Focus on infrastructure (mainly around Linux and Kubernetes)
• Kafka is available as part of the Red Hat AMQ product portfolio, combined with other open-source frameworks like ActiveMQ or Camel
• OpenShift Streams for Apache Kafka provides integration with Kubernetes
• Focus on open source frameworks; working actively with the community (for Kafka, Red Hat, e.g., contributes to Debezium for CDC and the Strimzi Kubernetes Operator)
• Hybrid Architectures (but no fully-managed Kafka service)
• Partnership and 3rd party integration into cloud providers (AWS, GCP, Azure)

Interesting side notes for the relationship between Confluent, Red Hat, and IBM:

• IBM acquired Red Hat in 2019.
• Confluent and IBM announced a strategic partnership in 2020
• IBM deprecated its own Kafka offering (IBM Streams) in March 2021.
• Confluent is the way to go with IBM as part of the IBM Cloud Pak for Integration. Even IBM’s salespeople sell Confluent.

Amazon Web Services (AWS) – The Leading Cloud Provider

• MSK misses several key Kafka features, including Kafka Connect or Kafka Streams
• Cloud-only (but only self-managed, not fully managed)
• MSK is not cloud-native (like S3 or Kinesis) but just provisioned infrastructure
• Obviously only available on AWS
• For on-premise deployments (like AWS Outpost or AWS Wavelength), the recommended Kafka product is the certified Confluent Platform

Interesting side note about the commercial support and SLAs of AWS’s Kafka offering: Kafka is excluded from MSK support! Quote from the MSK SLAs: “The Service Commitment DOES NOT APPLY to any unavailability, suspension, or termination… caused by the underlying Apache Kafka or Apache ZooKeeper engine software that leads to request failures…”

Event Streaming Technology and Cloud-native Infrastructure are Complementary!

The above showed a few facts for the main Kafka vendors: Confluent, Cloudera, Red Hat, AWS. However, it is worth explicitly pointing out that these vendors are often complementary. For instance, most Confluent Platform deployments I see on Kubernetes on-premise are actually on Red Hat OpenShift. And with AWS’s huge market share, most self-managed Confluent deployments in the cloud are on AWS.

Also, Confluent Platform is certified on AWS Outpost and Google Anthos. Hence, you can even combine cloud-native technologies at the edge. A great example is smart factory 5G use cases leveraging Confluent Platform on AWS Wavelength. Consequently, a Kafka comparison does typically not eliminate all the other Kafka vendors from the project.

The following architecture depicts the combination of Confluent Cloud in AWS plus Confluent Platform on AWS Wavelength leveraging 5G Carrier networks:

This is not just theory. The joint teams from AWS and Confluent are working on this example in the real world while I am writing this blog post.

Cloud-native? Complete? Everywhere? What Kafka should I buy?

After exploring different vendors, let’s now walk through the different deployment options and commercial offerings.

Again, I will not make a feature-by-feature comparison. Way more important is to understand the different concepts and architecture principles: First of all, you need to decide if a self-driving car (= fully managed Kafka) works for you. In that case, why bother at all about Kafka operations? Otherwise, project teams must evaluate partially managed (= complete car) or self-managed (car engine) Kafka offerings.

Here is an overview showing the event streaming landscape. It contains native Kafka offerings, (partly) Kafka-protocol compatible products, and a few relevant non-Kafka solutions:

Let’s now take a deeper look into these alternatives to find out how to choose the right one for your next project.

Car Engine: Self-managed Open Source Apache Kafka

The car engine is the heart of the car. It provides the power. It brings you from your source to your destination. However, a lot of work is needed around the motor engine. Tires, steering wheel, breaks, and much more are required. Hence, this is a great solution for playing around, learning how a car works, or building a car by enthusiastic car fanatics.

If you download open-source Apache Kafka from the Apache website or related Docker images, then you can use it for free in all your projects. No limitations. You should be able to get it running quickly. However, be aware that similar to the motor engine of a car, there is much more to do: Operating and monitoring the ZooKeeper and Kafka Clusters, rebalancing partitions, scaling up and down, managing storage, securing and encryption the end-to-end communication between producer, Kafka cluster and consumers, and so much more.

If you can handle the operations burden and risk of downtime, open-source Apache Kafka might be a good option. Some tech giants from Silicon Valley do exactly this. They have hired masses of tech experts (or car fanatics to keep the analogy) to run huge Kafka clusters to process trillions of messages and gigabytes of data per second.

Free Kafka add-ons to build your car

Plenty of open-source Kafka add-ons originated through this. Just to name a few tools for very different purposes: kafkacat, Kafka Manager, Kafdrop, burrow, cruise control, and so many more. Some are maintained well, others not at all. Of course, you will never get guarantees to get a version upgrade or bug fix. Often built by a tech giant for their specific scenario. Not easily usable outside that organization and without a big community.

Alternatively, there are well-maintained community projects like Confluent’s Schema Registry, REST Proxy, and ksqlDB, all under Confluent Community License (CCL). This is not open source but free to use if you are not a cloud provider like AWS. Confluent also provides some components under Apache 2.0 license, such as the widely used non-Java Kafka clients based on librdkafka or the parallel-consumer to integrate with non-scalable interfaces like web services in a scalable and performant way.

Tuned Car Engine: Self-managed Kafka Product

If you want or need to self-manage your Kafka infrastructure, then you still have more options than just using open-source Apache Kafka and (well or not so well maintained) open-source add-ons:

• Open-source Apache Kafka with additional commercial tools for operations and monitoring. For instance, Lenses or Conduktor.
• Complete commercial platforms. For instance, Confluent Platform, Red Hat AMQ, Cloudera DataFlow.

These “tuned car engines” are based on top of Apache Kafka (or at least parts of it) and provide additional tooling for development, operations, monitoring, security, etc. Maturity of the tools, support SLAs, expertise, and consulting vary a lot between vendors. I recommend to talk to your potential vendors. Ask the right questions to understand if they really understand what they seem to sell and support.

Shiny user interfaces attract many people. Just be careful. The underlying technology needs to work reliably and scale for your needs. The UI is nice to have on top of the infrastructure. Nevertheless, a good UI can improve the developer experience, increase time to market, and bring other benefits.

Should I use a (Tuned) Car Engine and Build my own Car?

All the explored options above are still self-managed. If you consider building your own car with a car engine, always evaluate the cost-benefit equation.

Remember, at the beginning of this post, I talked about solving business problems. Hence, don’t forget to consider all the impacts on:

• Total Cost of Ownership (TCO)
• Risk (downtime, data loss, security, governance, etc.)
• Return on Investment (ROI)
• Time-to-market (Increased developer velocity and increased business agility)

At Confluent, we do TCO assessments with our prospects and customers so that they understand the complete costs and risks of a Kafka project. Such an assessment should be part of every Kafka comparison!

Hence, don’t forget to evaluate other alternatives to open-source Apache Kafka seriously. If self-managed Apache Kafka still works for you after the evaluation, then do it! But be aware that even the tech giants from Silicon Valley consider and buy other options today. Many had to build Kafka infrastructure because there was no other alternative when they built it years ago.

Does my favorite open-source vendor really provide open-source?

Also, be careful: Some open source solutions don’t provide an easy way to build the product. So, evaluate what exactly is available from a so-called “open source offering”: Only a binary download? Docker images? Or can you also build and deploy everything from scratch easily and documented (not just in theory !!!) using Maven, Gradle, Terraform, Ansible, or similar build and automation tools?

Before building your own car with an available car engine, why not buy a car? Let’s consider next if a complete car might make more sense for you.

Complete Car: Kafka Products and Kubernetes-based PaaS

I am not a car fanatic. I want to buy a complete car that I can drive everywhere. You should also at least consider this option!

In Kafka terms, this means you get help from the product for running the Kafka infrastructure. Buzzwords from vendors include terms like “platform as a service”, “private cloud”, “fully managed”, and “cloud-native”. In the end, the products help you with provisioning, operating, and monitoring everything.

The main benefit compared to the (tuned) car engines is that these products give you a more elastic, scalable, and automated infrastructure. Two options exist today:

• Kubernetes-based products that can run everywhere on-premise and across multiple cloud providers. Examples: Confluent Platform, Cloudera DataFlow (CDF), Red Hat AMQ respectively Red Hat OpenShift Streams for Apache Kafka.
• Proprietary cloud offerings that are typically tied to the related hyperscaler. Example: Amazon MSK.

This is similar to buying a car: It comes preassembled. Although, of course, you are still responsible for operating and maintaining it.

In Kafka terms, for many scenarios, these self-managed Kafka products (= car) are a better choice than the self-managed Kafka (= car engine) because they partially reduce the operations burden, risk, and (hopefully) TCO.

A complete car is still not self-driving!

However, as you know, today’s cars still need a lot of manual work: Driving, refueling, maintenance, and more. In Kafka terms: How much works do you still have to do by yourself? Do you have to handle rolling upgrades manually? Do you have to rebalance partitions on brokers? How do you scale up and down? Who fixes security issues and bugs? And so on.

Hence, it is really a pity that most vendors use incorrect marketing intentionally! No one of the above solutions are fully managed. All of them require work to do by you to operate the Kafka cluster. All of them! Confluent Platform. Cloudera DataFlow. Red Hat AMQ. Red Hat OpenShift Streams for Apache. Kafka Amazon MSK. None of these services is fully managed!

Each car brand and model is different. If you buy a Porsche, you probably have very different expectations than buying a small medium-priced car from another brand. The same is true for all the self-managed Kafka products on the market. Each product is very different: Confluent Platform. Cloudera DataFlow. Red Hat AMQ / OpenShift Streams for Apache Kafka. Amazon MSK. All of them have strengths and weaknesses. Make sure it fits your expectations so that you can solve your business problem within your required SLAs and budget.

Having said this, wouldn’t it be nice if you don’t have to worry about all these things? Let’s explore the self-driving Kafka car next.

Self-driving Car: Fully-managed Cloud Kafka Service

A self-driving car provides a complete solution. You just tell it where you want to go. It drives you automatically. Chooses the best route. Allows relaxing, reading, playing games, or similar things. Of course, an autonomous car with level 5 automation is not mature yet (beyond some early stages, like Waymo operating in the desert in Phoenix where no rain and other weather or traffic issues occur).

In Kafka terms, the solution needs to be fully managed by the vendor.

Fully managed means serverless (i.e., you don’t have to care and even don’t get access to the Kafka Brokers at all). Mission-critical SLAs. Usage-based billing. And so on. Like you know it from other really fully managed cloud offerings such as AWS S3 or AWS Kinesis. These are fully managed. Amazon MSK is not!

Checklist to compare partially managed and fully-managed Kafka cloud services

Please compare different Kafka cloud offerings by yourself. Here are some bullet points to check:

Infrastructure management

• Upgrades (latest stable version of Kafka)
• Patching
• Maintenance

Kafka-specific management

• Sizing (retention, latency, throughput, storage, etc.)
• Data balancing for optimal performance
• Performance tuning for real-time and latency requirements
• Fixing Kafka bugs
• Uptime monitoring and proactive remediation of issues
• Recovery support from data corruption

Scaling

• Scaling the cluster as needed
• Data balancing the cluster as nodes are added
• Support for any Kafka issue with less than 60-minute response time

Most “Kafka as a service” offerings are only partially managed. That’s like a self-driving car which you actually have to control by yourself (more like level 3, not level 5 in autonomous driving terminology).

At this point, I have to do marketing for my employer. However, it is not an advertisement, but reality: Confluent Cloud is the only offering on the market that provides a complete, fully-managed Kafka SaaS offering. And it is available everywhere – on all major cloud provides (AWS, Azure, GCP). All the other Kafka offerings are NOT fully managed – even though most vendors claim it!

Other Vehicles on the Street: Comparison of Kafka-Compatible and Non-Kafka Offerings

On the street, we don’t see just one car brand or car model. Plenty of different ones exist. Nevertheless, they have to drive on the same streets. Competition creates innovation and tackles different markets and personal interests. That’s great. The same is true for Kafka!

I focused on the “mainstream Kafka vendors” in the above sections. Namely, Confluent, Cloudera, Red Hat, Amazon MSK. Obviously, more Kafka offerings exist on the market. Some are really good for some use cases. Others are more like an April Fools’ Joke, in my opinion. Let’s quickly walk through a few other offerings.

Kafka-related offerings

• A few more car brands: Azure HD Insight’s Kafka, Aiven, cloudkarafka, Instaclustr. These Kafka-native PaaS vendors provision Kafka clusters for you. Similarly to Amazon MSK. These offerings slightly differ from each other. In summary, they typically ask you to do storage management, scalability configuration, performance tuning, etc., by yourself. This is definitely not self-driving!
• A self-driving car: Azure Event Hubs is a SaaS offering from Microsoft supporting the Kafka protocol. It has several limitations regarding support of the Kafka API and infrastructure. A solid product. Contrary to Confluent Cloud, you don’t get additional capabilities such as fully-managed connectors, Schema Registry, RBAC, Audit Logs, and much more. And obviously, this product is only available on the Azure cloud.
• A vintage car: TIBCO focuses on their legacy messaging solutions like TIBCO EMS. They (try to) provide support for Kafka (and Pulsar) to sell their proprietary technologies. Zero expertise or interest in Kafka. They even provide Kafka as .exe Windows file even though this does not work well in reality. If you need to run Kafka brokers on Windows (e.g., for development), only use Kafka Docker containers and the Windows Subsystem for Linux 2 (WSL 2).

Non-Kafka offerings

• Self-driving scooters: AWS Kinesis, GCP Pub/Sub, etc., are solid SaaS offerings that work well if you don’t need to be vendor-agnostic and if the feature set, scalability, and pricing work for you.
• A few bicycles, motorbikes, and cars: Non-Kafka solutions, including message queuing (IBM MQ, RabbitMQ, NATS), stream processing (Flink, Spark Streaming), event streaming (Pulsar, Pravega), integration middleware (many open-source/proprietary and self-managed/SaaS). These are solid frameworks and products that you can compare to Kafka. There is no silver bullet! Make sure to understand the differences between MQ/ETL/ESB and Kafka when you do your evaluation.

Connected Car Fleet: Multiple Kafka Clusters and Hybrid Integration

The digital transformation around connected vehicles is a real game-changer. Vehicles talk to each other (V2V), their infrastructure like traffic lights (V2I), and to many other backend systems (V2X).

As a side note: If you are interested in the relation of Kafka and connected vehicles/mobility services, I covered use cases for connected vehicles and V2X in my blog series about Kafka and MQTT in more detail.

Today, we usually have to drive by ourselves. This is expected to change in the next five to ten years. However, even if Waymo, Telsa, and the likes successfully deploy level 4 and level 5 cars to the street (including legal allowance), we will still only see a fraction of all cars driving themselves. It will be a connected fleet with regular cars and self-driving cars for at least a few decades. Not even sure if self-driving cars can ever go to India

The same is true for Kafka. Self-managed open-source Kafka is still mainstream today. Many enterprises move to Kubernetes and private or public cloud infrastructure, though. In parallel, most new Kafka clusters in the cloud are consumed from vendors that provide partially or fully managed services so that the enterprise can focus on their business problems.

Kafka is deployed across infrastructures. Often, new projects have a cloud-first strategy. But there are still a lot of data centers out there. Not just for legacy reasons. For instance, in Russia, there is no public cloud provider at all. Kafka has to be deployed on-premise. And there is the trend of deploying Kafka at the edge (i.e., outside a data center).

Architectures for Hybrid Kafka (SaaS + PaaS + Self-Managed)

Hence, a connected car fleet with various brands and operation types is required. Most enterprises use different vendors and cloud providers. Most enterprises have their own data centers and a multi-cloud strategy. Hybrid Kafka includes various architectures. This includes:

• Kafka in one or multiple clouds. There is no Azure or GCP in China, only Alibaba and Tencent Cloud. This is why Audi built their connected car infrastructure in the cloud, but with Kafka instead of proprietary cloud services. They need to deploy globally.
• Kafka at the edge outside the data center, e.g., in a smart factory, oil rigs, ships, retail stores, etc. Often deployed as a single broker on very lightweight hardware, without high availability.
• Kafka stretched across regions, i.e., one single cluster operating across the US west, east, central. Confluent’s Multi-Region Clusters (MRC) is mainly used for this architecture.
• Replication between different Kafka clusters. Use cases include aggregation, disaster recovery, global deployments, and more. Kafka-native technologies such as MirrorMaker 2, Confluent Replicator, or Confluent Cluster Linking enable these architectures.

“Architecture patterns for distributed, hybrid, edge and global Apache Kafka deployments” explores this topic in more detail.

Focus on the Business Problem when making your Kafka Comparison!

This blog post explored the different deployment options for Kafka. Several open-source and commercial options exist.

If you want to remember one thing from this post: A fully-managed Kafka service (= real SaaS) takes over all the operations complexity and risk for you, similarly like a self-driving car handles all the actions on the street. However, most services available today only provide self-managed Kafka clusters. Fully managed is often only a marketing term.

A hybrid architecture is the norm in most enterprises. A combination of fully-managed Kafka in the public cloud with self-managed Kafka on premise or at the edge works very well and is the way to go for most enterprises across industries.

What Kafka car do you drive today? What is your plan for the future? Maybe you are already planning to migrate to a self-driving car to focus on your business problems – and consequently reducing cost and risk this way, too? Let’s connect on LinkedIn and discuss it! Stay informed about new blog posts by subscribing to my newsletter.

The post Comparison of Open Source Apache Kafka vs Vendors including Confluent, Cloudera, Red Hat, Amazon MSK appeared first on Kai Waehner.