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We built a modern data stack from scratch and reduced our bill by 70%

jchandra.com

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Re: We built a modern data stack from scratch and reduced our bill by 70%

#3

Good read. I do have a question on the BigQuery. i f you were experiencing unpredictable query costs or customization issues, that sounds like user error. There are ways to optimize or commit slots for reducing the cost. Did you try that ?

As for BigQuery, while it's a great tool, we faced challenges with high-volume, small queries where costs became unpredictable as it is priced per data volume scanned. Clustered tables, Materialised views helped to some extent, but they didn’t fully mitigate the overhead for our specific workloads. There are ways to overcome and optimize it for sure so i wouldn't exactly put it on GBQ or any limitations.

It’s always a trade-off, and we made the call that best fit our scale, workloads, and long-term plans

Re: We built a modern data stack from scratch and reduced our bill by 70%

#5
post #3

Good read. I do have a question on the BigQuery. i f you were experiencing unpredictable query costs or customization issues, that sounds like user error. There are ways to optimize or commit slots for reducing the cost. Did you try that ?

As for BigQuery, while it's a great tool, we faced challenges with high-volume, small queries where costs became unpredictable as it is priced per data volume scanned. Clustered tables, Materialised views helped to some extent, but they didn’t fully mitigate the overhead for our specific workloads. There are ways to overcome and optimize it for sure so i wouldn't exactly put it on GBQ or any limitations. It’s always…

Hmm, Okay.

I am not sure if managing kafka connect cluster in too expensive in long term. This solution might work for you based on your needs. I would suggest to look for alternatives.

Re: We built a modern data stack from scratch and reduced our bill by 70%

#6
We did have a discussion on Self vs Managed and TCOs associated with it. 1> We have multi regional setup so it came up with Data Sovereignty requirements. 2> Vendor Lock ins - Few of the services were not available in that geographic region 3> With managed services, you often pay for capacity you might not always use. our workloads were often consistent and predictable, so self managed solutions helped in fine tuning our resources. 4> One og the goal was to keep our storage and compute loosely coupled while staying Iceberg-compatible for flexibility. Whether it’s Trino today or Snowflake/Databricks tomorrow, we aren’t locked in.

Re: We built a modern data stack from scratch and reduced our bill by 70%

#9
post #3

Good read. I do have a question on the BigQuery. i f you were experiencing unpredictable query costs or customization issues, that sounds like user error. There are ways to optimize or commit slots for reducing the cost. Did you try that ?

As for BigQuery, while it's a great tool, we faced challenges with high-volume, small queries where costs became unpredictable as it is priced per data volume scanned. Clustered tables, Materialised views helped to some extent, but they didn’t fully mitigate the overhead for our specific workloads. There are ways to overcome and optimize it for sure so i wouldn't exactly put it on GBQ or any limitations. It’s always…

Did you consider slots based pricing model for BQ?
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