Viktor Gamov is a Principal Developer Advocate at Confluent, founded by the original creators of Apache Kafka®. With a rich background in implementing and advocating for distributed systems and cloud-native architectures, Viktor excels in open-source technologies. He is passionate about assisting architects, developers, and operators in crafting systems that are not only low in latency and scalable but also highly available.
As a Java Champion and an esteemed speaker, Viktor is known for his insightful presentations at top industry events like JavaOne, Devoxx, Kafka Summit, and QCon. His expertise spans distributed systems, real-time data streaming, JVM, and DevOps. Viktor has co-authored “Enterprise Web Development” and “Apache Kafka® in Action”. Follow Viktor on X - @gamussa to stay updated with Viktor’s latest thoughts on technology, his gym and food adventures, and insights into open-source and developer advocacy.
Streaming data with Apache Kafka® has become the backbone of modern day applications. While streams are ideal for continuous data flow, they lack built-in querying capability. Unlike databases with indexed lookups, Kafka’s append-only logs are designed for high throughput processing, not for on-demand querying. This necessitates teams to build additional infrastructure to enable query capabilities for streaming data. Traditional methods replicate this data into external stores such as relational databases like PostgreSQL for operational workloads and object storage like S3 with Flink, Spark, or Trino for analytical use cases. While useful sometimes, these methods deepen the divide between operational and analytical estates, creating silos, complex ETL pipelines, and issues with schema mismatches, freshness, and failures.
In this session, we’ll explore and see live demos of some solutions to unify the operational and analytical estates, eliminating data silos. We’ll start with stream processing using Kafka Streams, Apache Flink®, and SQL implementations, then cover integration of relational databases with real-time analytics databases such as Apache Pinot® and ClickHouse. Finally, we’ll dive into modern approaches like Apache Iceberg® with Tableflow, which simplifies data preparation by seamlessly representing Kafka topics and associated schemas as Iceberg or Delta tables in a few clicks. While there’s no single right answer to this problem, as responsible system builders, we must understand our options and trade-offs to build robust architectures.
