Improve query performance 4.2 times in average compared with Apache Spark SQL which is widely used parallel query processing system in both academia and industry. Can have a huge impact on large ...

For instance, in the above example, parallel databases with simple federation capabilities would still use the execution strategy A. This means 695 million rows would need to be transferred through ...

Parallel Query Processing in a Cluster using MPI and File System Caching. Data intensive applications that rely heavily on huge databases waste a lot of time in searching and retrieval especially ...

This property of MapReduce makes it ideal for transforming data prior to load into the Parallel DBMS.” MapReduce has parallel DBMS beat in I/O efficiency, cost, surviving node failure, ability to ...

TigerGraph recently introduced a distributed computation mode that significantly improves performance for analytical queries that traverse a large portion of the graph.

Furthermore, systems that integrate intelligent compression strategies directly into database indices, such as those utilising B+-trees, have reported impressive gains in query throughput by ...

SkySQL, the cloud database based on the well-known MariaDB open source RDBMS, adds distributed database capabilities for write-intensive applications scalability and high availability.

CitusDB’s innovation comes in its high-performance parallel processing capability within the Postgres core, said Matt Ocko, a partner with Data Collective and an investor in the company.

Sybase has changed the structure of its Sybase IQ column-based database software to allow queries to be run in parallel, increasing the efficiency with which large datasets can be mined, queried ...

Online analytical processing (OLAP) databases are purpose-built for handling analytical queries. Analytical queries run on online transaction-processing (OLTP) databases often take a long time to ...