This curated guide follows the journey from installation and onboarding to automation, data modeling, advanced analytics, and machine learning. Each blog post here is organized to reflect the natural progression of a Greenplum deployment.

Learn why Massively Parallel Postgres (MPP) platforms like Greenplum, WarehousePG, and Cloudberry need both logical backups and modern Point-In-Time Recovery to meet real DR requirements. This blog from guest blogger Greg Spiegelberg explains how these data protection techniques all fit and complement each other.

From script sprawl to structured, drill-down diagnostics.

When I assess customer Greenplum environments, I lean on a suite of lightweight shell utilities that make deep inspection fast and repeatable. At the core is gpview.sh, an interactive catalog viewer. Around it are wrappers for scheduled health checks, single-report runs, and persistence for trending over time.

Disaster Recovery (DR) isn’t just “take a backup.” At MPP scale, you need a repeatable end-to-end flow with minimal coordination and downtime. This post walks through the DBA Operations Kickstarter framework that wraps gpbackup, gpbackup_manager, and gprestore into a fully automated DR pipeline

Self-service BI works when the data foundation removes friction for business users. Our reusable dimensional framework gives you trusted definitions, “as-was” and “as-is” choices, foolproof joins, and upstream data quality checks, so teams can explore confidently without hand-coding SQL or exporting to Excel (or god help us all MS Access).

Partitioning is a cornerstone of scalable analytics in Greenplum Database. The hardest part of partitioning isn’t design, it’s keeping partitions current. The kickstarter Partition Maintenance toolset is a set of Bash/Python utilities that operationalizes this lifecycle for Greenplum. This post will dive into this set of tools.

Demonstrate how to leverage PL/Python and the GreenplumPython library to push data transformation logic directly into the Greenplum Database. This approach aligns with a key best practice in big data architecture: move processing to the data, not the other way around. Achieve a scalable, more secure, more cost effective solution for your Python dev team.

Automating Agile Data Onboarding with Greenplum Sailfish

We understand the challenges of provisioning and operationalizing a Greenplum or Cloudberry data warehouse environment. Whether you’re deploying on physical infrastructure, virtual platforms, or the cloud, setting up a high-performance MPP database requires precision, expertise, and automation.

The DBA Operations Kickstarter is a comprehensive toolkit designed by Mugnano Data Consulting to automate and operationalize best-practice DBA workflows for Greenplum and Cloudberry environments. Whether you're onboarding a new system or stabilizing an existing one, this solution delivers an enterprise-grade operational foundation in hours, not months.

Dive deeper into how we leverage the extensibility features of the DbSchema Data Modeling Tool. Specifically, we’ll show how to design a dimensional model and generate DDL that directly integrates with Mugnano Data Consulting’s dimension utility functions

This post introduces DbSchema, a data modeling tool I found effective for Greenplum. I’ll show how I worked with the vendor to make Greenplum a first-class citizen in the platform, supporting logical and physical design as well as DDL generation.