Would that potentially be a situation which “running your own” doesn’t make sense?
Look into datalake architectures. RDBMS based data warehousing is obviously not economical at the petabyte scale. But storing all that data in S3 with Delta Lake/Iceberg format and querying with Spark changes things entirely. You only pay for object storage, and S3 read costs are trivial.
Yup .. comfy with iceberg/delta/hudi
> RDBMS based data warehousing is obviously not economical at the petabyte scale.
I never said it was .. I'm simply responding to "I simply cannot understand how anyone chooses this over running your own Spark clusters with Jupyterlab". I'm trying to help you understand why folks would choose a SaaS over run your own.
> But storing all that data in S3 with Delta Lake/Iceberg format and querying with Spark changes things entirely. You only pay for object storage, and S3 read costs are trivial.
No. You don't just pay for object storage + minor S3 read costs.
You pay for operations You pay for someone setting up conventions You pay to not have to optimize data layouts for streaming writes You pay to not have to discover race conditions in s3 when running multiple spark clusters writing to same delta tables You pay to not have to discover that your partitions/clustering needs have changed based on new data or query patterns
But look .. I get it. You have chosen to optimize for cost structures in one way .. and I've chosen to optimize in a different way. In the past I've done exactly as you've said as well. I think being able to seeking to see _why_ folks may have chosen a different path may help you understand other areas to consider in operations.
Or I guess, what data size do you think it's talking about? If you only have gigabytes of data, none of this matters, you can use anything pretty cheaply and easily. So is this article just for "terabytes" or does it go up to "hundreds of terabytes" but not "petabytes"?
However, I now realize that th biggest companies probably should manage their own data. If you're Google why would you use Snowflake?
So I don't know if you are the target audience for this blog post. It's pretty ambiguous.
But I'm not sure the advice is very good from the 5th percentile up to ... maybe that top 20%? A lot of the stuff in the article assumes the availability of sophisticated data architects and mature infrastructure groups that I really don't think the median company has.
We both seem have a sense of the size of companies at different percentiles. At what percentile would you put your company with petabytes of data?
But I do have very high confidence that the 99th percentile is much larger than petabytes (think: what's next after "exa"), and I believe that many companies these days crack into "peta" territory.
But as I saw another comment mention, I think another, probably more important, consideration besides size in bytes is cardinality and structure. So maybe this whole classification we're doing is kind of beside the point :)
I also agree that the variety of data plays a big part in its complexity. If you have a few petabytes of data, but it's really only a handful of tables you can real hone in on the relationships. If it's a wide array of sources with many tables between them then you have some nasty problems like entity resolution.
All happy data sets are alike; each unhappy data set is unhappy in its own way.
Ha, gonna steal that for some doc I write someday :)
https://en.m.wikipedia.org/wiki/Anna_Karenina_principle#:~:t....