Presented By O’Reilly and Cloudera
Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
New York, NY

Hudi: Unifying storage and serving for batch and near-real-time analytics

5:25pm–6:05pm Wednesday, 09/12/2018
Secondary topics:  Data Integration and Data Pipelines

Who is this presentation for?

  • Data engineers and architects

Prerequisite knowledge

  • A basic understanding of the big data ecosystem (Hadoop, Spark, etc.)

What you'll learn

  • Explore Hudi, an open source analytical storage system created at Uber to manage petabytes of data on HDFS-like distributed storage
  • Understand the trade-offs in managing analytical storage across different use cases

Description

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.

Photo of Nishith Agarwal

Nishith Agarwal

Uber

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.

Photo of Balaji Varadarajan

Balaji Varadarajan

Uber

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.

Photo of Vinoth Chandar

Vinoth Chandar

Uber

Vinoth Chandar is a senior software engineer at Uber, where he works on data infrastructure, with a focus on Hadoop and Spark. Vinoth has keen interest in unified architectures for data analytics and processing. Previously, he was the LinkedIn lead on Voldemort and worked on Oracle Server’s replication engine, HPC, and stream processing.

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Dhiraj Nimbalkar |
10/10/2018 2:43pm EDT

How can I get access to Hudi: Unifying storage and serving for batch and near-real-time analytics webinar?