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MinervaDB Blog

Master data management on PostgreSQL: source, candidate, master, attribute lineage, effective-dated crosswalk and history tables
AI & Analytics

Master Data Management on PostgreSQL: 6 Proven Tables That Make Every Golden Record Explainable

Most master data management programmes we are asked to rescue did not fail on matching algorithms. They failed because nobody could answer, for a given customer number on a given invoice, which source system's address [...]
Metrics layer on dbt and MetricFlow: sources, marts, semantic models and metrics compiled into BI, notebook and decision API queries
AI & Analytics

Metrics Layer with dbt: 5 Proven Tests That Keep One Definition per Number

Ask three teams in the same company for last quarter's net revenue and you will usually get three numbers. Finance excludes returns booked after period close, marketing counts gross of discounts because that is what [...]
Feature store architecture on PostgreSQL, ClickHouse and Valkey: registry, two write paths, offline store, online store, prediction log and SLOs
AI & Analytics

Feature Store Architecture: 7 Proven Decisions on PostgreSQL and ClickHouse

A feature store architecture is three contracts, not a product. The first contract is that a feature has exactly one definition, one owner and one freshness promise. The second is that any training set can [...]
Retail Detail Strategy
Cloud Data Warehouse

Retail Data Strategy: 6 Proven Layers for Marketing and Sales Ops Analytics

A retail data strategy is not a tooling decision. It is a set of commitments about four facts that marketing and sales operations both depend on and rarely agree about: who the customer is, what [...]
AzureSQL Performance
Azure SQL

Azure SQL Performance Troubleshooting: 7 Proven Steps

Azure SQL performance troubleshooting starts in a different place from on-premises SQL Server work. There is no operating system to inspect, no sys.dm_os_wait_stats that means what it used to, and the first suspect is almost [...]
Databricks
Data Analytics

Databricks Long-Running Queries: 6-Step Troubleshooting Guide

Databricks long-running queries are rarely one problem. When a statement that used to finish in forty seconds starts taking nine minutes, the nine minutes are usually spread across a cold warehouse, a queue, a compilation [...]
expensive SQL
Data Reliability Engineering

Expensive SQL on Exadata 26.1: Read the Offload Numbers Before You Build the Index

The Exadata ticket we see most often is not about a slow query. It is about a cluster that runs hot for hours, an IORM plan that somebody keeps re-tuning, and a licence conversation about [...]
Expensive Queries
Azure SQL

SQL Server 2025 Expensive Queries: 7 Proven Ways to Find and Fix Them by Latency and Resource Cost

"Expensive queries" is a phrase that hides two different problems. One is response time: a statement the application waits too long for. The other is resource efficiency: a statement that burns CPU, logical reads and [...]
Index Efficiency
Data Reliability Engineering

Db2 13 for z/OS Index Efficiency: 8 Proven Troubleshooting Techniques

Most index efficiency problems on Db2 13 for z/OS are not I/O problems. They are CPU problems that look like I/O problems from a distance. Index efficiency is measured in getpages, and a four-level index [...]
AlloyDB pricing, internals and FinOps: performance per dollar on Google AlloyDB
AlloyDB

AlloyDB Pricing and Internals: 2026 FinOps Best Practices Guide

AlloyDB pricing is simple to read and easy to get wrong, because every line on the invoice is driven by a piece of the engine's internals that most teams never look at. Compute is billed [...]

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* Everything changes over time – Our blogs/posts and comments changes over time, That’s how it should be! Whatever we comment from MinervaDB Inc. Teams (including Shiv Iyer) and other stakeholders or guest bloggers posted here are never permanent, These things worked for us. But, there is no guarantee they will work for you too, When using the recommendations from ChistaDATA or MinervaDB or MinervaSQL or any other online resources / Google,  You must test the advice before applying them to your production systems, and always invest for a robust Database DR solution, Thank you for understanding. 

Recent Posts ✏

  • Databricks FinOps Workshop: 12 Metrics That Expose Costly DBU Waste
  • Supabase FinOps: 10 Essential Metrics to Stop Overspending in 2026
  • Db2 z/OS Performance Tuning: The Best Practical FL 509 Playbook
  • Valkey Real-Time Analytics for FinTech: A Proven 9.1 Design
  • Postgres Lakehouse Integration on Snowflake: 5 Powerful Ways

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