In the New Stack Makers episode, Adi Polak, Director, Advocacy and Developer Experience Engineering at Confluent discusses the operational and analytical estates in data infrastructure. The operational estate focuses on fast, low-latency event-driven applications, while the analytical estate handles long-running data crunching tasks. Challenges arise due to the "schema evolution" from upstream operational changes impacting downstream analytics, creating complexity for developers.
In the New Stack Makers episode, Adi Polak, Director, Advocacy and Developer Experience Engineering at Confluent discusses the operational and analytical estates in data infrastructure. The operational estate focuses on fast, low-latency event-driven applications, while the analytical estate handles long-running data crunching tasks. Challenges arise due to the "schema evolution" from upstream operational changes impacting downstream analytics, creating complexity for developers.
Apache Iceberg and Flink help mitigate these issues. Iceberg, a table format developed by Netflix, optimizes querying by managing file relationships within a data lake, reducing processing time and errors. It has been widely adopted by major companies like Airbnb and LinkedIn.
Apache Flink, a versatile data processing framework, is driving two key trends: shifting some batch processing tasks into stream processing and transitioning microservices into Flink streaming applications. This approach enhances system reliability, lowers latency, and meets customer demands for real-time data, like instant flight status updates. Together, Iceberg and Flink streamline data infrastructure, addressing developer pain points and improving efficiency.
Learn more from The New Stack about Apache Iceberg and Flink:
Unfreeze Apache Iceberg to Thaw Your Data Lakehouse
Apache Flink: 2023 Retrospective and Glimpse into the Future
4 Reasons Why Developers Should Use Apache Flink
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