Kafka and Starburst: 3 Considerations for Accelerating Time to Value

Apache Kafka, initially developed at LinkedIn and open-sourced in 2011, serves as a scalable and fault-tolerant platform designed to optimize data streaming and support high-performance applications. It is widely used in various business contexts, such as customer 360 applications, hospitality, fraud detection, and predictive maintenance, due to its capability to manage real-time data effectively. However, integrating Kafka with query capabilities for better data understanding often requires additional tools like Solr or Elasticsearch. The addition of ksqlDB, a SQL-like streaming engine, enhanced access for users not familiar with programming languages like Java or Python. Meanwhile, Starburst Enterprise, based on Trino, offers a centralized access point for data consumers, allowing them to query Kafka topics using SQL. This is facilitated by Starburst's Kafka data connector, which simplifies the handling of Kafka's complex data structures through schema metadata and JSON functions, enabling analysts to enrich streaming data with external sources without the need for extensive data migration. By federating queries across multiple data sources, Starburst enhances the usability and accessibility of Kafka streaming data, improving the speed and efficiency of data analysis and reporting for users of all skill levels.