Hudi (formerly Hoodie) is an open source analytical storage system created at Uber to manage petabytes of data on HDFS-like distributed storage. Hudi enables near-real-time ingestion and provides different views of the data—a read-optimized view for batch analytics, a real-time view for driving dashboards, and an incremental view for powering data pipelines. Hudi also effectively manages files on underlying storage to maximize operational health and reliability.
Nishith Agarwal, Balaji Varadarajan, and Vinoth Chandar outline the design and architecture of merge-on-read storage and explain how it lowers data latency across the board while simultaneously achieving orders of magnitude of efficiency over traditional batch ingestion. They make the case for near-real-time dashboarding on top of Hudi datasets, which can be cheaper than pure streaming architectures, and detail how Uber leverages Hudi for use cases around ingestion, incremental ETL, and GDPR compliance.
Nishith Agarwal is a senior software engineer at Uber, where he works on the Hudi project and the Hadoop platform at large. His interests lie in large-scale distributed and data systems.
Balaji Varadarajan is a senior software engineer at Uber, where he works on the Hudi project and oversees data engineering broadly across the network performance monitoring domain. Previously, he was one of the lead engineers on LinkedIn’s databus change capture system as well as the Espresso NoSQL store. Balaji’s interests lie in distributed data systems.
Vinoth Chandar is the cocreator of the Hudi project at Uber and also PMC and lead of Apache Hudi (Incubating). Previously, he was a senior staff engineer at Uber, where he led projects across various technology areas like data infrastructure, data architecture, and mobile and network performance; was the LinkedIn lead on Voldemort; and worked on Oracle Server’s replication engine, HPC, and stream processing . Vinoth has keen interest in unified architectures for data analytics and processing.
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