My Take on Google BigQuery: What I Love (and What I Don’t)

In my job, and what I love about it is, I get to work with all kinds of data stacks — from SQL, MySQL, to fully cloud-native environments. One in particular that I have not mentioned for a while is the powerhouse that is Google BigQuery. It’s quite a bit underestimated.

It’s powerful, fast, and often exactly what clients need when they’re drowning in spreadsheets, especially if your stack is already on Google.

I recently helped a client connect their data from Google Sheets, which was pulling scheduled data from NetSuite, directly into BigQuery. I built the pipeline, automated the data flows, and scheduled everything to update hourly — After teaching myself how it all works, a few mistakes here and there, it worked like a charm.

But as much as I like BigQuery, it’s not perfect. What is though, right?

Here’s my honest breakdown of the pros and cons:

✅ What I Like

No servers to manage – It’s fully managed, so you don’t have to worry about infrastructure or scaling.

It’s web-based, although, sometimes I do prefer an SQL client.

Really fast queries – Even with huge datasets, performance is quick.

SQL-based – You can use SQL perfectly.

Easy integrations – Works well with Google Sheets, Looker, Tableau, Power BI and so on.

Pay-as-you-go – You only pay for what you query

⚠️ What to Watch Out For

Frustrating that you can’t connect Tableau to a base table, it needs to connect to a scheduled table.

Query costs can sneak up on you – Especially if you’re not careful with how much data you’re running.

It’s web-based, I know i added this above, but sometimes, you want to use an SQL client, and BigQuery, does not do that well

Not ideal for real-time dashboards – Great for hourly or daily updates, not so much for second-by-second monitoring.

Limited traditional database features – It’s not built for transactions or row-level operations.

Pricing isn’t always easy to understand – You need to understand how slots, storage, and on-demand queries work to avoid surprises.

Regional limitations – Cross-region queries can slow things down and cost more.

🎯 Final Thoughts

BigQuery is a great choice if you’re handling a lot of data and want something out of google sheets, CSV, Google Drive, that scales without the headaches of server management. Just be careful when it comes to costs and the use-case fit.

If you’re thinking about using BigQuery, or stuck trying to connect it to other tools like GSheets, GDrive, NetSuite, or Tableau, Power BI etc — happy to help, I have been there and done it, learnt it, now I know how it works.

#BigQuery #GCP #DataEngineering #DGDataServices #ModernDataStack #SQL #Netsuite #Analytics #PowerBI #Tableau #googlesheets