𝙃𝙤𝙬 𝙄𝙣𝙚𝙛𝙛𝙞𝙘𝙞𝙚𝙣𝙩 𝙎𝙌𝙇 𝙌𝙪𝙞𝙚𝙩𝙡𝙮 𝙄𝙣𝙛𝙡𝙖𝙩𝙚𝙨 𝙔𝙤𝙪𝙧 𝘾𝙡𝙤𝙪𝙙 𝘽𝙞𝙡𝙡

Ever looked at your cloud bill and thought, “We didn’t even ship anything new, so why is this higher?” One of the most common cost leaks is not broken pipelines or new workloads. It’s 𝗶𝗻𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗦𝗤𝗟 running quietly in the background.

➡ A few “small” query issues can turn into serious spend: ➡ A query scans millions of rows when it only needs a filtered subset ➡ SELECT * pulls unnecessary columns, increasing I/O and compute ➡ Poor joins multiply rows and inflate runtime Keep Reading👇

➡Jobs run every hour “just because,” even when the business only needs daily updates ➡Old scheduled queries still run long after the report is no longer used Each one might look harmless on its own. Combine them, and your warehouse spends more time computing than delivering

𝗛𝗼𝘄 𝘁𝗼 𝘀𝘁𝗮𝘆 𝗮𝗵𝗲𝗮𝗱 🔸Identify the top cost queries (by runtime, scanned data and frequency) 🔸Be intentional with columns. Pull what you need, not everything 🔸Review joins and aggregation logic for row explosion 🔸Audit schedules. Remove stale jobs Keep Scrolling👇

🔸 Audit schedules. Remove stale jobs, reduce frequency, and consolidate where possible 🔸Track cost per query or cost per dashboard so spend ties back to business value Cloud cost efficiency isn’t magic. It’s good engineering habits

If your cloud bill keeps creeping up, start with your SQL. It’s usually the fastest win. Follow for more practical insights on data engineering, analytics, and AI. #DataEngineering #SQL #CloudCostOptimization #AnalyticsEngineering #BigQuery #Snowflake #Redshift

92% 𝗢𝗳 𝘀𝗽𝗿𝗲𝗮𝗱𝘀𝗵𝗲𝗲𝘁𝘀 𝘂𝘀𝗲𝗱 𝗶𝗻 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗺𝗮𝗸𝗶𝗻𝗴 𝗰𝗼𝗻𝘁𝗮𝗶𝗻 𝗲𝗿𝗿𝗼𝗿𝘀

Spreadsheet improves business agility and efficiency, however their usability can pose significant risks to your business decision making 𝗠𝗼𝘀𝘁 𝘁𝗲𝗮𝗺𝘀 𝗱𝗼𝗻’𝘁 𝗿𝗲𝗮𝗹𝗶𝘇𝗲 𝗶𝘁 𝘂𝗻𝘁𝗶𝗹 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗳𝗲𝗲𝗹𝘀 𝗼𝗳𝗳. Keep Reading👇

Spreadsheets aren’t the problem. The problem is how easily they turn into: 📌 Manual data pipelines 📌 Version control nightmares 📌 Logic nobody remembers building 📌 Single points of failure Continue Reading👇

And once a spreadsheet becomes “business-critical,” errors stop being harmless, they become expensive. Keep scrolling👇

This doesn’t mean stop using spreadsheets, It means use them intentionally by; ✅ Validating key inputs ✅ Protecting critical formulas ✅ Reducing manual hand-offs ✅ Documenting assumptions ✅ Knowing when it’s time to graduate to proper systems

How confident are you in the spreadsheet behind your last big decision? 👉𝗡𝗲𝗲𝗱 𝗵𝗲𝗹𝗽 𝗼𝗻 𝘁𝗵𝗶𝘀? 𝗖𝗼𝗺𝗺𝗲𝗻𝘁 𝗯𝗲𝗹𝗼𝘄 𝘁𝗼 𝗴𝗲𝘁 𝘀𝘁𝗮𝗿𝘁𝗲𝗱

𝗪𝗵𝗮𝘁 𝗮 𝗕𝗿𝗼𝗸𝗲𝗻 𝗗𝗮𝘁𝗮 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗟𝗼𝗼𝗸𝘀 𝗟𝗶𝗸𝗲

A failing data pipeline can quietly affect almost every corner of your business They don’t fail loudly, they quietly break and businesses keep making decisions on bad data.

Here’s what a broken data pipeline looks like in real life👇 🚩 Dashboards telling different stories for the same metric 🚩 Reports that need “manual adjustments” every week 🚩 Data arriving late, or not at all 🚩 Analysts spending more time fixing data than analyzing it

These failures often go unnoticed until a report or executive dashboard looks wrong A broken pipeline doesn't mean your system stopped working completely. Keep Reading👇

Here's how to detect issues early; ✅ Set data quality checks ✅ Track pipeline SLAs like you track up-time ✅ Monitor upstream changes. Schemas don’t break politely ✅ Log, alert, and review failures daily (not monthly) ✅ Treat data as a product, not a side effect

📌 Strong data teams don’t just build pipelines. They observe, test, and protect them continuously. Does this sound familiar? 👉 Follow us for more insightful post on Data and AI #dataquality #datapipeline #DataDriven

🔥 If you haven’t reviewed your cloud storage cost in the last 6–12 months, there’s a high chance you’re overpaying. 👉 When was the last time your cloud storage was audited?