Operational data stores (ODS) serve as a data staging area between transactional databases and data warehouses. Data from multiple sources are integrated, cleansed, and prepped in the ODS before populating a data warehouse for long-term storage and analytics. Traditional ODS systems encounter severe challenges when it comes to dealing with the wide variety and massive volume of data common to data warehouses built on top of the Hadoop platform. It’s time to rethink the requirements and the architecture for the next generation of an ODS on top of Hadoop. Starting from first principles, Vinayak Borkar defines the requirements for a modern operational data store and explores some possible architectures to support those requirements.
Vinayak Borkar is the CTO of X15 Software, Inc. Previously, he was a PhD candidate at UC Irvine, where he worked on big data and contributed to the Hyracks Open Source Big Data Project. Prior to going back to school, Vinayak spent eight years building data-management software.
Comments on this page are now closed.
©2016, O'Reilly Media, Inc. • (800) 889-8969 or (707) 827-7019 • Monday-Friday 7:30am-5pm PT • All trademarks and registered trademarks appearing on oreilly.com are the property of their respective owners. • firstname.lastname@example.org
Apache Hadoop, Hadoop, Apache Spark, Spark, and Apache are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries, and are used with permission. The Apache Software Foundation has no affiliation with and does not endorse, or review the materials provided at this event, which is managed by O'Reilly Media and/or Cloudera.