Approaches to Streaming Data into Apache Iceberg Tables
This article discusses three primary approaches to streaming data into Apache Iceberg tables. It highlights the operational trade-offs associated with each method, focusing on the balance between data freshness and table health. The piece is part of a larger masterclass on Apache Iceberg, aimed at data engineers and architects.
- ▪Apache Iceberg was designed for batch analytics, but streaming ingestion is necessary for continuous data arrival.
- ▪Spark Structured Streaming, Apache Flink, and Kafka Connect are the three main architectures for streaming data into Iceberg tables.
- ▪Flink provides exactly-once delivery guarantees, making it suitable for financial and transactional data pipelines.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 288069) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Alex Merced Posted on May 22 Approaches to Streaming Data into Apache Iceberg Tables #architecture #database #dataengineering #tutorial This is Part 13 of a 15-part Apache Iceberg Masterclass. Part 12 covered Python and MPP engines. This article covers the three primary approaches to streaming data into Iceberg tables and the operational trade-offs each creates. Iceberg was designed for batch analytics, but most production data arrives continuously.
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