In Search of Database Nirvana

The Challenges of Delivering Hybrid Transactions/Analytical Processing

In Search of Database Nirvana

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The database pendulum is in full swing. Ten years ago, web-scale companies began moving away from proprietary relational databases to handle big data use cases with NoSQL and Hadoop. Now, for a variety of reasons, the pendulum is swinging back toward SQL-based solutions. What many companies really want is a system that can handle all of their operational, OLTP, BI, and analytic workloads. Could such an all-in-one database exist?

This O’Reilly report examines this quest for database nirvana, or what Gartner recently dubbed Hybrid Transaction/Analytical Processing (HTAP). Author Rohit Jain takes an in-depth look at the possibilities and the challenges for companies that long for a single query engine to rule them all.

With this report, you’ll explore:

  • The challenges of having one query engine support operational, BI, and analytical workloads
  • Efforts to produce a query engine that supports multiple storage engines
  • Attempts to support multiple data models with the same query engine
  • Why an HTAP database engine needs to provide enterprise-caliber capabilities, including high availability, security, and manageability
  • How to assess various options for meeting workload requirements with one database engine, or a combination of query and storage engines

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Rohit Jain

Rohit Jain is the CTO at Esgyn working on Apache Trafodion, currently in incubation. Trafodion is a transactional to analytics SQL-on-Hadoop RDBMS. Rohit worked for Tandem, Compaq, and Hewlett-Packard for the last 28 of his 40 years in application and database development. He has worked as an application developer, solutions architect, consultant, software engineer, database architect, development and QA manager, Product Manager, and CTO. His experience spans Online Transaction Processing, Operational Data Stores, Data Marts, Enterprise Data Warehouses, Business Intelligence, and Advanced Analytics, on distributed massively parallel systems.