Snowflake Postgres Brings Transactional and Analytical Data Together

Snowflake Postgres has arrived! It gives you a fully managed, 100% compatible Postgres database running directly on the Snowflake platform! #snowflake

This is a meaningful shift—especially when you consider how much housekeeping overhead most teams are dealing with today. In a lot of conversations I’ve had, the real bottleneck isn’t just moving data between systems—it’s everything around it. Maintaining pipelines, handling failures, managing infrastructure, tuning performance. It adds up and slows down time to value. What teams need is a simpler way to reduce that operational burden so they can spend less time keeping systems running and more time delivering results/value. If you can reduce the layers in between transactional and analytical workloads, you give teams a faster path from data to impact. Curious how others are thinking about this—does reducing that overhead change how you evaluate your data stack?

Most databases don’t fail suddenly. They slow down… quietly. Over time, unused data keeps piling up: • Old transactions • Inactive user records • Historical logs no one queries anymore But they still sit in your production database: impacting performance, increasing storage costs, and making maintenance harder. We have seen cases where 30–50% of database size was just cold, unused data. That means: - Slower queries - Higher cloud bills - Longer backup and recovery times - More stress on your infrastructure And the worst part? Most teams don’t even realize this is happening. Production databases are built for fast, live transactions, not for carrying historical weight. That’s why we built PACMAN (by Mydbops). https://lnkd.in/gmxNV2CY It intelligently identifies cold data and archives it, without impacting your live application. ✔ Keep your database lean and fast ✔ Reduce infrastructure costs ✔ Improve query performance ✔ No manual effort, no risk If your database is growing faster than expected, it’s not just growth… it’s inefficiency. Time to take control of your data. Stop overpaying to store old data. 🛑 Take a look at the attached deck to see exactly how it works. DM me if you want to see a live demo. #CloudSavings #DBA #DataManagement #Mydbops #PostgreSQL #MySQL #CloudCosts #DataArchival #DevOps #CostReduction #DatabaseStorage

Snowflake Postgres is now GA, delivering fully managed, 100% community Postgres on Snowflake. ⚡️ That means lift-and-shift migrations with zero code changes and streamlined data movement via pg_lake — eliminating the need for complex, fragile ETL just to get transactional data into Snowflake.  Add built-in high availability, failover, and enterprise security — and you have a production-ready foundation from day one. Check out the full details: https://lnkd.in/eW7MdCmn

Stop letting old data slow you down. PACMAN by Mydbops keeps your production databases lean and high-performing without the complexity. ⚡️ #pacman #CloudEfficiency #DataEngineering #DBA #MySQL #PostgreSQL #MongoDB #MariaDB #TiDB #Mydbops🚀

Postgres turned 30 last year and It seems Postgres is having a moment. And I don't think its because Postgres got better. I believe the biggest tailwind is everything else got more complicated. At some point, teams started counting the real cost of specialized databases: -sync jobs -operational overhead -seven things that can break Then realized Postgres was already doing most of it just fine. Plus with the ecosystem built around it, it's now turning into the obvious answer again. https://lnkd.in/gHbakyPh

I am super happy to publish my 20th blog post project Here's a question that changed how I think about database monitoring: 💡 If you can SELECT it... why can't you graph it? Turns out — you absolutely can. And you don't need fancy tools to do it. We wrote a pattern that takes ANY metric living inside your SQL Server engine and puts it straight into CloudWatch. No Lambda. No agents. No third-party anything. The secret sauce? 🍯 T-SQL query → Agent Job → Error Log → CloudWatch Logs → 📈 Metric → 🚨 Alarm That's it. That's the whole architecture. We demo'd it with table size tracking, but here's where it gets fun — swap that query for ANYTHING: 🔢 Row counts trending over time 👥 Active user sessions 🔒 Blocking chain depth 💾 Transaction log usage % 📉 Replication lag 💀 Deadlock frequency ⏱️ Long-running query counts 🏷️ Custom app-level KPIs sitting in your tables If your T-SQL returns a number ➡️ it can be a CloudWatch metric ➡️ it can be on a dashboard ➡️ it can page you😅 🙏 Massive shoutout to my co-authors Cade Kettner, Ryan Moore and special thanks to Murilo Cerone Nascimento and Toby Xu for your guidance - Couldn't have done it without you! 💪 📖 Read it here 👇 https://lnkd.in/e7ae_cpy #AWS #RDS #SQLServer

Apache Iceberg just won the table format war. 🏆 Snowflake ✅ native Iceberg support Databricks ✅ native Iceberg support Microsoft Fabric ✅ native Iceberg support Oracle 26ai ✅ native Iceberg support DuckDB 1.4.2 ✅ full Iceberg read/write When four of the biggest data platforms ship the same format in one year, the debate is over. 👇 What this actually means for your stack: → Vendor lock-in on storage format is becoming history → Compute engine performance is now the real differentiator → The lakehouse model (unified storage + multiple compute engines) is the new standard If you're starting a new data lake project in 2026 and you're not defaulting to Iceberg, you're already building technical debt. Full state of databases 2026: https://lnkd.in/e5Z9tDMK 💬 Is your team running Iceberg in production yet? Or still on Delta/Hudi? #DataEngineering #ApacheIceberg #DataLakehouse #Snowflake #Databricks #DataOps #DataPlatform

🚀 New blog published: Understanding Class 2 Transactions in Azure SQL Database I recently worked on a customer case around unexpected transaction log growth in Azure SQL Database, where there were no obvious large user transactions. This led to a great discussion on Class 2 (system) transactions and how Azure SQL behaves differently compared to PostgreSQL. In this blog, I cover: ✅ What Class 2 transactions are and why they matter ✅ Why Azure SQL doesn’t expose PostgreSQL‑style diagnostics ✅ The key DMVs to monitor log usage, checkpoints, and version store pressure ✅ How to automate monitoring using Azure Elastic Jobs ✅ Practical takeaways from a real customer scenario If you’re troubleshooting log growth or trying to better understand background system activity in Azure SQL DB, this one’s for you. 📖 Read the full post on Microsoft Tech Community 👇 #AzureSQL #AzureDatabase #SQLServer #CloudDatabases #PerformanceTuning #DatabaseMonitoring #MicrosoftTechCommunity

The latest DB-Engines rankings are a great reminder of how the data landscape is evolving. At the top, not much has changed: Oracle, MySQL, and SQL Server still dominate. But the real story is in the trends. • PostgreSQL continues steady growth — not just popular, but gaining real momentum • Databricks is climbing fast — signaling the rise of modern data platforms • Snowflake’s growth reflects the shift to cloud-native analytics • MongoDB holding strong shows NoSQL isn’t going anywhere Meanwhile, some legacy systems are holding position but slowly losing ground in momentum. What stands out to me: The industry isn’t replacing old systems overnight — it’s layering modern tools on top of existing infrastructure. This creates a hybrid world where: • Traditional SQL still matters • Cloud platforms are accelerating • And data engineering skills are becoming just as important as analysis Are you working more with traditional databases, or shifting toward cloud/data platforms? #DataAnalytics #DataEngineering #SQL #CloudComputing #Databricks #Snowflake #PostgreSQL