VMware Tanzu Greenplum is a massively parallel processing (MPP) database server that supports next generation data warehousing and large-scale analytics processing by automatically partitioning data and running parallel queries, it allows a cluster of servers to operate as a single database supercomputer performing tens or hundreds of times faster than a traditional database. It supports SQL, MapReduce parallel processing, and data volumes ranging from hundreds of gigabytes, to hundreds of terabytes.
Consolidate more workloads in a single environment.
Greenplum reduces data silos by providing a single, scale-out environment for converging analytic and operational workloads, like streaming ingestion. Perform point queries, fast data ingestion, data science exploration, and long-running reporting queries with greater scale and concurrency.
Pre-integrated components for easier consumption.
VMware Tanzu Greenplum is based on PostgreSQL and the Greenplum Database project. It offers optional use-case specific extensions like PostGIS for geospatial analysis, and GP Text (based on Apache Tika and Apache Solr) for document extraction, search, and natural language processing. These extensions are pre integrated to ensure a consistent experience. Instead of depending on expensive proprietary databases, users can benefit from the contributions of a vibrant community of developers.
Run analytics on public and private clouds, Kubernetes, or on-premises.
Greenplum provides your enterprise with flexibility and choice because it can be deployed on all major public and private cloud platforms, on-premises, and with container orchestration systems like Kubernetes. Deploy and manage hundreds of Greenplum instances easily.
Streamline data science operations and simplify workloads
Tackle data science from experimentation to massive deployment with Apache MADlib, the open-source library of in-cluster machine learning functions for the Postgres family of databases. MADlib with Greenplum provides multinode, multi-GPU and deep learning capabilities. It also offers automation-friendly features such as model versioning, and the capability to push models from training to production by a REST API. Users avoid the pain of porting and recoding analytical models.