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

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.

This blog summarizes a demonstration delivered to a customer interested in understanding how Greenplum can be used for machine learning, without exporting data to external systems. The objective was to showcase how churn modeling can be prototyped and iterated entirely within Greenplum using the gpmlbot utility, which automates feature preparation, model training, and evaluation.