Tag Archives: Adaptive Query Execution

[project ]Hadapt :Architecture


Hadapt is designed to analyze massive amounts of structured and unstructured data at high speed.  Our software was designed for the cloud, and is optimized for virtualized environments.  Hadapt integrates the latest advances in relational DBMS research with the MapReduce distributed computing framework to provide a scalable and fast analytic database system.  In addition to providing the full power of MapReduce, Hadapt offers rich SQL support and the ability to work with all of your data within one platform.

  • Scalability: By scaling out rather than up, Hadapt runs on commodity hardware or on public/private clouds, providing a lower total cost of ownership.  More importantly, Hadapt’s architecture accelerates analysis and offers significant performance benefits on structured data in particular.
  • Speed & SQL Support: SQL queries perform an order of magnitude faster in Hadapt compared with using Hadoop+Hive.  Further, Hadapt’s SQL is derived from the SQL support of its underlying relational DBMS and is richer and more standards-compliant than that of Hive.  This permits streamlined integration with existing business intelligence tools and workflows.
  • Unified Platform: In addition to these speed and scalability advantages, Hadapt provides the ability to perform analysis across all of your data sets within one platform.  Many analytical database systems take a “bolt-on” approach to Hadoop, requiring two disparate systems: Hadoop for unstructured data and a separate relational database for structured data.  Using connectors can introduce delays, create unintended data silos, and increase TCO. Hadapt’s Adaptive Analytical Platform™ requires no connectors, and instead offers a complete solution to your data warehousing and analysis needs.
  • Adaptive Query Execution: Hadapt’s patent-pending technology automatically splits query execution between the platform’s Hadoop and RDBMS layers, providing an optimized system that leverages the scalability of the MapReduce framework and the speed of relational database technology.