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Storage & Warehousing
Greenplum

Greenplum

Massively parallel analytics database built on PostgreSQL

https://greenplum.org

What It Is

Greenplum is an open-source massively parallel processing (MPP) database built on PostgreSQL. It distributes data and query processing across multiple segments, enabling analytical query performance on datasets from gigabytes to petabytes. Greenplum combines the familiar PostgreSQL SQL syntax with the power of distributed computing โ€” making it a strong choice for organizations that need on-premise or hybrid analytical capabilities.

How We Use It

We deploy Greenplum for clients who need high-performance analytical processing in on-premise or hybrid environments where cloud-only solutions aren’t viable. Our expertise covers cluster design, distribution key strategies, resource group management, and integration with upstream and downstream data systems. We also assist with migrations from legacy MPP platforms to Greenplum.

Our Expertise

  • Cluster Architecture

    We design Greenplum clusters: segment host sizing, mirror configuration, master/standby setup, and interconnect network optimization.

  • Distribution Strategy

    We select distribution keys that minimize data movement during JOINs and aggregations. Hash, random, and replicated distribution based on query patterns.

  • Resource Management

    We configure resource groups to isolate workloads: ETL processes, ad-hoc queries, and scheduled reports running without interference.

  • PostgreSQL Compatibility

    We leverage Greenplum's PostgreSQL foundation: familiar SQL syntax, extensions support, and compatibility with PostgreSQL-based tools and connectors.

Use Cases

Typical Use Cases

1

On-Premise Analytics

High-performance analytical processing where cloud isn’t an option.

2

Legacy MPP Migration

Migrating from proprietary MPP databases to open-source Greenplum.

3

Hybrid Architectures

Combining on-premise Greenplum with cloud storage and processing.

4

Large-Scale Reporting

Enterprise reporting on multi-terabyte datasets with complex queries.

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