The State & Future of Apache Kafka (with Anatoly Zelenin)
May 8, 2025
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Anatoly Zelenin, co-author of 'Apache Kafka in Action,' shares his expertise on the evolution of Apache Kafka and its impact on real-time data processing. He discusses Kafka's advantages over traditional databases and the complexities of integrating microservices. The conversation covers the challenges of building scalable data infrastructure, including data silos and transformation limitations. Zelenin highlights Kafka's promising future in enhancing transaction management and its critical role in optimizing modern data architectures.
Using a write-ahead log as a foundational database architecture enhances flexibility and optimizes data management compared to traditional systems.
Apache Kafka facilitates real-time data processing, enabling businesses to make quicker decisions despite challenges in integrating data connectors.
The need for organizational change is crucial for maximizing Kafka's efficiency, emphasizing data ownership and the creation of a data product culture.
Deep dives
The Central Role of Logs in Database Architecture
The concept of using a write-ahead log as the foundational component of database architecture is highlighted as a significant insight. Instead of evolving existing databases around this concept, the idea is to start with an append-only log and build functionalities like replication and durability from this core. The vision posited by Martin Kleppmann suggests that an event-based system, such as Apache Kafka, can provide advantages over traditional database approaches. This rethinking of database architecture leads to a more optimized and flexible approach where logs serve as the center of data management.
Challenges and Opportunities in Real-Time Data Processing
Apache Kafka enables businesses to move towards real-time or near-real-time data processing, addressing the delay often seen in legacy systems. The discussion emphasizes the importance of adapting to a data-driven world where speed is paramount, allowing for quicker decision-making and operations. Although Kafka is powerful for processing significant volumes of data in real time, its requirement for users to build their connectors presents a barrier compared to traditional systems with pre-packaged solutions. The necessity for businesses to think creatively about data integration and the potential for independent system collaboration is underscored.
Kafka's Role vs. Traditional Databases
There is a distinct difference between how Kafka and traditional relational databases function regarding data storage and retrieval. While relational databases are designed to handle current states of data and enforce a structure, Kafka operates as a log that captures the history of events over time. This distinction raises questions about how organizations should approach data management, with Kafka serving to store historical data while relational databases provide the current state. The conversation suggests that using both systems can complement each other to maximize data utility within an organization.
Future Prospects for Querying and Data Management with Kafka
The ongoing development of querying and analytical capabilities on top of Kafka is seen as a critical area for future improvement. Current solutions, while enabling streaming data processing, often require complex implementation and lack the simplicity of traditional SQL querying seen in relational databases. As the ecosystem matures, there is hope for tools that would allow ease of use in data querying, similar to what developers expect from relational systems. This creates an opportunity for enhancing the developer experience and further solidifying Kafka's role in modern data architectures.
Navigating Organizational Challenges in Data Architecture
The podcast underscores that many challenges surrounding Apache Kafka are not purely technical but also organizational in nature. Issues such as data ownership, decentralized governance, and the need for data products can lead to complications in managing data flows effectively. The importance of creating a culture where teams can own and manage their data as products is highlighted, which would streamline interactions and increase productivity. As organizations continue to adapt to data as a product mindset, addressing these structural hurdles will be crucial for realizing the full potential of systems like Kafka.
I’m joined this week by one of the authors of Apache Kafka In Action, to take a look at the state of Kafka, event systems & stream-processing technology. It’s an approach (and a whole market) that’s had at least a decade to mature, so how has it done? What does Kafka offer to developers and businesses, and which parts do they actually care about? What have streaming data systems promised and what have they actually delivered? What’s still left to build?
