Getting Started: Incrementally process data with Apache Hudi™
Blog post from Onehouse
Incremental processing is a crucial technique in data systems for efficiently managing large data volumes by processing data subsets separately, leading to improved data freshness and reduced resource usage. Designing custom incremental pipelines requires careful consideration of factors such as data size and latency, along with robust monitoring and data validation to ensure consistency. Apache Hudi offers a streamlined alternative, equipped with built-in incremental processing capabilities, including a Change-Data-Capture (CDC) feature from version 0.13.0, which supports detailed data changes for enhanced downstream processing. This feature logs before-and-after images of data changes and supports complex operations like incremental joins and CDC processing in Spark streaming applications. The blog provides detailed code examples to illustrate these concepts and highlights the operational efficiencies gained by using Hudi for incremental data processing, citing Uber's implementation as a case study.
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