My Data Streaming Journey with Kafka & Flink: 7 Years at Confluent

Time flies… I joined Confluent seven years ago when Apache Kafka was mainly used by a few tech giants and the company had ~100 employees. This blog post explores my data streaming journey, including Kafka becoming a de facto standard for over 100,000 organizations, Confluent doing an IPO on the NASDAQ stock exchange, 5000+ customers adopting a data streaming platform, and emerging new design approaches and technologies like data mesh, GenAI, and Apache Flink. I look at the past, present and future of my personal data streaming journey. Both, from the evolution of technology trends and the journey as a Confluent employee that started in a Silicon Valley startup and is now part of a global software and cloud company.

My Data Streaming Journey with Kafka and Flink - 7 Years at Confluent

Disclaimer: Everything in this article reflects my personal opinions. This is particularly important when you talk about the outlook of a publicly listed company.

PAST: Apache Kafka is pretty much unknown outside of Silicon Valley in 2017

When I joined Confluent in 2017, most people did not know about Apache Kafka. Confluent was in the early stage with ~100 employees.

Tech: Big data with Hadoop and Spark as “game changer”; Kafka only the ingestion layer

2017 was a time where most companies installed Cloudera or Hortonworks. Hadoop + Spark and Kafka as ingestion layer. That was the starting point of using Apache Kafka. The predominant use case for Kafka at that time was data ingestion into Hadoop’s storage system HDFS. Map Reduce and later Apache Spark batch processes analyzed big data sets.

“The cloud” was not that big thing yet and the container wars were still going on (Kubernetes vs. Cloud Foundry vs. Mesosphere).

I announced my departure from TIBCO and the fact that I will join Confluent in a blog post in May 2017: “Why I move (back) to open source for messaging, integration and stream processing“. If you look at my predictions, my outlook was not too bad.

I was right about disruptive trends:

Massive adoption of open source (beyond Linux)
Companies moved from batch to real-time because real-time data beats slow data in almost all use cases across industries
Adoption of machine learning for improving existing business processes and innovation
From the Enterprise Service Bus (ESB) – called iPaaS in the cloud today – to more cloud-native middleware, i.e., Apache Kafka (many Kafka projects today are integration projects)
Kafka being complementary to other data platforms, including data warehouse, data lake, analytics engines, etc.

What I did not see coming:

The massive transition to the public cloud
Apache Kafka being an event store for out-of-the-box capabilities like true decoupling of applications (what is the foundation and de facto standard for event-based microservices and data mesh today) and replayability of historical events in guaranteed ordering with timestamps
Generative AI (GenAI) as a specific pillar of AI / ML (did anyone see this coming seven years ago?)

Company: Confluent is a silicon valley startup with ~100 people making Kafka enterprise-ready

Confluent was a traditional Silicon Valley startup in 2017 with ~100 employees and backed by venture capital. The initial HR conversation and first interview (and company pitch) was done by our CEO Jay Kreps. I still have Jay’s initial email in my mailbox. It started with this:

“Hi Kai, I’m the CEO of Confluent and one of the co-creators of Apache Kafka. Given your experience at Tibco maybe you’ve run into Kafka before? Confluent is the company we created that is taking Kafka to the enterprise. […] We aim to go beyond just messaging and really make the infrastructure for real-time data streams a foundational element of a modern company’s data architecture.“

In 2017, Confluent was just starting to kick off its global business. The United Kingdom and Germany are usually the first two countries outside the US for Silicon Valley startups because of their large economy and no language barrier in the UK.

I will not publish my response to Jay’s email with respect to my former employer, but I still can quote the following sentence I responded: “I really like to hear from you that you want to go beyond just messaging and really make the infrastructure for real-time data streams a foundational element of a modern company’s data architecture“. Mission accomplished. That’s where Confluent is today.

Working in an international overlay role for Confluent…

I also pointed out in the very first email to Confluent that I am already in an overlay role and work internationally. While I was officially starting as the first German employee for presales and solution consulting, I only signed the contract because everybody in Confluent’s executive team understood and agreed on supporting me to continue my career in an overlay role doing a mix of sales, marketing, enablement, evangelism, etc. internationally.

A few weeks later, I was already in the Confluent headquarters in Palo Alto, California:

I am still more or less in the same role today. However, with much more focus on executive conversations and business perspective instead of “just” technology. Like the technology developed, so did the conversations. Check out my career analysis describing what I do as Field CTO if you want to learn more.

While Confluent moved to a bigger office in Mountain View, California, the Kafka tree still exists today:

Kafka Tree at the Confluent Headquarters Mountain View California USA

PRESENT: Everyone knows Kafka (and Confluent); most companies use it already in 2024

Apache Kafka is used in most organizations in 2024. Many enterprises are still in the early stage of the adoption curve and are building some streaming data pipelines. However, several companies, not just in Silicon Valley but worldwide and across industries, already matured and leverage stream processing with Kafka Streams or Apache Flink for advanced and critical use cases like fraud detection, context-specific customer personalization or predictive maintenance.

Tech: Apache Kafka is the de facto standard for data streaming

Over 100,000 organizations use Apache Kafka in 2024. This is an insane number. Apache Kafka became the de facto standard for data streaming. Data streaming is much more than just real-time messaging for transactional and analytics workloads. Most customers leverage Kafka Connect for data integration scenarios to legacy and cloud-native data sources and sinks. Confluent provides an entire ecosystem of integration capabilities like 100+ connectors, clients for any programming language, Confluent Stream Sharing, any many other integration alternatives. Always with security and data governance in mind. Enterprise-ready, as most people call it. And in the cloud, all of this is fully managed.

In the meantime, Apache Flink establishes itself as de facto standard for stream processing. Here is an interesting diagram showing the analogy of growth:

The Rise of Open Source Streaming Processing with Apache Kafka and Apache Flink — Source: Confluent

Various vendors build products and cloud services around the two successful open source data streaming frameworks: Apache Kafka and/or Flink. Some vendors leverage the open source frameworks, while others only rely on the open protocol to implement their own solutions to differentiate:

All major cloud providers provide Kafka as a service (AWS, Azure, GCP, Alibaba).
Many of the largest traditional software players include a Kafka product (including IBM, Oracle, and many more).
Established data companies support Kafka and/or Flink, like Confluent, Cloudera, Red Hat, Ververica, etc.
New startups emerge, including Redpanda, WarpStream, Decodable, and so on.

Data Streaming Landscape (vs. Data Lake, Data Warehouse and Lakehouse)

The current “Data Streaming Landscape 2024” provides a much more detailed overview. There will probably be some consolidation in the market. But it is great to see such an adoption and growth in the data streaming market.

While new concepts (e.g., data mesh) and technologies (e.g., GenAI) emerged in the past few years, one thing is clear: The fundamental value of event-driven architectures using data streaming with Kafka and Flink does not change: data in motion is much more valuable for most use cases and business instead of just storing and analyzing data at rest in a data warehouse, data lake, or in 2024 using an innovative lakehouse.

It is worth reading my blog series comparing data streaming with data lakes and data warehouses. These technologies are complementary, (mostly) not competitive.

I also published a blog series recently exploring how data streaming changes the view on Snowflake (and cloud data platforms in general) from an enterprise architecture perspective:

Company: Confluent is a global player with 3000+ employees and listed on the NASDAQ

Confluent is a global player in the software and cloud industry employing ~3000 reaching $1 Billion ARR soon. As announced a few earnings calls back, the company now also focuses on profit instead of just growth, and the last quarter was the first profitable quarter in the company’s history. This is a tremendous achievement looking into Confluent’s future.

Unfortunately, even in 2024, many people still struggle to understand event-driven architectures and stream processing. One of my major tasks in 2024 at Confluent is to educate people – internal, partners, customers/prospects, and the broader community – about data streaming:

What is data streaming?
What are the benefits, differences, and trade-offs compared to traditional software design patterns (like APIs, databases, message brokers, etc.) and related products/cloud services?
What use cases do companies use data streaming for?
How do industry-specific deployments look like (this differs a lot in financial services vs. retail vs. manufacturing vs. telco vs. gaming vs. public sector)?
What is the business value (reduced cost, increased revenue, reduced disk, improved customer experience, faster time to market)?

“The Past, Present and Future of Stream Processing” shows the evolution and looks at new concepts like emerging streaming databases and the unification of operational and analytical systems using Apache Iceberg or similar table formats.

Confluent is a well-known software and cloud company today. As part of my job, I present at international conferences, give press interviews, brief research analysts like Gartner/Forrester, and write public articles to let people know (in as simple as possible words) what data streaming is and why the adoption is so massive across all regions and industries.

Kai Waehner Publications Articles Podcasts

Confluent Partners: Cloud Service Providers, 3rd Party Vendors, System Integrators

Confluent strategically works with cloud service providers (AWS, Azure, GCP, Alibaba), software / cloud vendors (the list is too long to name everyone), and system integrators. While some people still think about a company like AWS as an enemy, it is much more a friend to co-sell data streaming in combination with other cloud services via the Amazon marketplace.

The list of strategic partners grows year by year. One of the most exciting announcements of 2023 was the strategic partnership between SAP and Confluent to connect S/4Hana ERP and other systems with the rest of the software and cloud world using Confluent.

Confluent Customers: From Open Source Kafka to Hybrid Multi-Cloud

Confluent has over 5000 customers already. I talk about many of these customer journeys in by blogs. Just search for your favorite industry to learn more. One exciting example is the evolution of the data streaming adoption at BMW. Coming from a wild zoo of deployments, BMW has standardized on Confluent, including self-service, data governance, and global rollouts for smart factory, logistics, direct-to-consumer sales and marketing, and many other use cases.

BMW hosts an internal Kafka Wiesn (= Oktoberfest) every year where we sponsor some pretzels and internal teams and external partners like Michelin present new projects, use cases, success stories, architectures and best practices around the data streaming world for transactional and analytical workloads. Here is a picture of our event in 2023 where my colleague Evi Schneider and I visited the BMW headquarters:

Apache Kafka Wiesn Oktoberfest at BMW in Munich Germany

FUTURE: Data streaming is a new software category in 2024+

Thinking about Gartner’s famous hype cycle, we are reaching the “plateau of productivity”. Thanks to mature open source frameworks, sophisticated (but far from perfect) products, and fully managed SaaS cloud offerings make the mass adoption of data streaming possible in the next few years.

Tech: Standards and (multi-cloud) SaaS are the new black

Data streaming is much more than just a better or more scalable messaging solution, a new integration platform, or a cloud-native processing platform. Data streaming is a new software category. Period. Even open source Kafka provides so many capabilities people don’t know, for instance, exactly-once semantics (EOS) for transactions, tiered storage API for separation of compute and storage, Kafka Connect for data integration and Kafka Streams for stream processing (both natively using the Kafka protocol), and so much more.

In December 2023, the research company Forrester published “The Forrester Wave™: Streaming Data Platforms, Q4 2023“. Get free access to the report here. The leaders are Microsoft, Google, and Confluent, followed by Oracle, Amazon, Cloudera, and a few others. IDC followed in early 2024 with a similar report. This is a huge proof that the category of data streaming is attested. A Gartner Magic Quadrant for Data Streaming will hopefully (and likely) follow soon, too… 🙂

Cloud services make mass adoption very easy and affordable. Consumption-based pricing allows cost-sensitive exploration and adoption. I won’t take a look at different competing offerings in this blog post, check out the “Data Streaming Landscape 2024” and the “Comparison of Open Source Apache Kafka and Vendor Solutions / Cloud Service” for more details. I just want to say one thing: Make sure to evaluate open source frameworks and different products correctly. Read the terms and conditions. Understand the support agreements and expertise of a vendor. If a product offers you “Kafka as Windows .exe file download” or a cloud provider “excludes Kafka support in the support contract from its Kafka cloud offering”, then something is wrong with the offering. Both examples are true and available to be paid for in today’s Kafka landscape!

Company: Confluent is more than just Kafka and Flink; it is becoming the provider of a true data streaming platform

In the past years, the company transitioned from “the Kafka vendor” into a data streaming platform. Confluent still does only one thing (data streaming), but better than everyone else regarding product, support and expertise. I am huge fan of this approach compared to vendors with a similar number of employees that try to (!) solve every (!) problem.

As Confluent is a public company, it is possible to attend the quarterly earnings calls to learn about the product strategy and revenue/growth.

From a career perspective, I still enjoy doing the same thing I did when I started at Confluent seven years ago. I transitioned into the job role of a Global Field CTO, focusing more on executive and business conversations, not just focusing on the technology itself. This is a job role that comes up more and more in software companies. There is no standard definition for this job role. As I regularly get the question about what a Field CTO does, I summarized the tasks in my “Daily Life As A Field CTO“. The post concludes with the answer to how you can also become a Field CTO at a software company in your career.

Data streaming is still in an early stage…

Where are we today with data streaming as a paradigm and Confluent as a company? We are still early. This is comparable to Oracle where they started with a database. Data streaming is accepted as a new software category by many experts and research analysts. But education about the paradigm shift and business value is still one of the biggest challenges. Data streaming is a journey – learn from various companies across industries that already went through this in the past years.

I was really excited to start at Confluent in May 2017. I visited Confluent’s London and Palo Alto headquarters in the first weeks and also attended Kafka Summit in New York. It was an exciting month to get started in an outstanding Silicon Valley startup. Today, I still visit our headquarters regularly for executive briefings, and Kafka Summit or similar events from Confluent like Current and the Data in Motion Tour around the world.

I hope this was an interesting report about my past seven years in the data streaming world at Confluent. What is your opinion about the future of open source technologies like Apache Kafka and Flink, the transition to the cloud, and the outlook for Confluent as a company? Let’s connect on LinkedIn and discuss it! Stay informed about new blog posts by subscribing to my newsletter.

Kai Waehner

Error Handling via Dead Letter Queue in Apache Kafka

Technology Evangelist

Apache Kafka vs. Middleware (MQ, ETL, ESB) – Slides + Video

Deep Learning Example: Apache Kafka + Python + Keras + TensorFlow + Deeplearning4j

Open Standards for Data Lineage: OpenLineage for Batch AND Streaming