Greenplum Software

About Greenplum Software

Greenplum Software provides a data warehouse and analytics platform designed for large-scale data volumes. The company was founded in September 2003 by Scott Yara and Luke Lonergan through the merger of two smaller companies, Metapa and Didera. Lonergan, with a background in mathematics, aeronautics, and astronautics from Stanford, previously founded Didera, a database clustering company. Yara had experience in strategic marketing and co-founding companies in the internet infrastructure and content delivery network (CDN) space.

The company's core product, now known as Tanzu Greenplum, is a massively parallel processing (MPP) database built on the open-source PostgreSQL. This architecture distributes data and query workloads across multiple servers, or nodes, enabling parallel processing for rapid analysis of petabyte-scale datasets. Greenplum's business model centers on providing this high-performance data platform to enterprises in sectors like finance, telecommunications, retail, and healthcare. Revenue is generated through subscriptions, often on a per-CPU-core basis, and the platform can be deployed on-premises, in private clouds, or on public cloud marketplaces like AWS, Google Cloud, and Microsoft Azure.

Greenplum's journey has been marked by a series of significant acquisitions. In July 2010, EMC acquired Greenplum for an estimated $300 million, making it the foundation of EMC's big data division. In 2012, the technology became part of Pivotal Software, a joint venture between EMC and VMware. Pivotal was later acquired by VMware in 2020, and the product was rebranded as VMware Tanzu Greenplum. Most recently, Broadcom acquired VMware in November 2023, and in May 2024, it was announced that future releases of Greenplum Database would be closed-source as part of the VMware Tanzu Data Suite.

The platform serves data professionals by consolidating operational and analytical workloads into a single environment. Key features include columnar storage for optimized data retrieval, a cost-based query optimizer for efficient large-scale workloads, and fault tolerance through segment mirroring. It supports a wide range of analytics, from standard SQL queries and business intelligence to in-database machine learning and AI with extensions for text, geospatial, and graph analysis. This allows clients to develop and deploy complex models for applications such as fraud detection, risk management, and predictive maintenance directly within the database.