Did Delta Lake ever catch on? Where are they going now?
Enterprise view: delegate AI environment to Databricks unless you’re a real player. Market is too chaotic, so rely on them to keep your innovation pipeline fed. Focus on building your own core data and AI within their environment. Nobody got fired for choosing Databricks.
Oh well. Databricks notebooks were hella cool back when companies were willing to spend lavishly on having engineers write cloud hosted Scala in the first place, and at premium prices to boot.
I'm joking, but only a bit. Iceberg is open source (Apache), but a lot of the core team and the creator worked at Tabular and Databricks bought them for $1B.
0 - https://www.definite.app/blog/databricks-tabular-acquisition
Personally, I hated databricks, it caused endless pain. Our org has less than 10TB of data and so it's overkill. Good ol' Postgres or SQL Server does just fine on tables of a few hundred GB, and bigquery chomps up 1TB+ without breaking a sweat.
Everything in databricks - everything - is clunky and slow. Booting up clusters can take 15 minutes whereas something like bigquery is essentially on-demand and instant. Data ETL'd into databricks usually differs slightly from its original source in subtle but annoying ways. Your IDE (which looks like jupyter notebook, but is not) absolutely suck (limited/unfamiliar keyboard shortcuts, flakey, can only be edited in browser), and you're out of luck if you want to use your favorite IDE, vim etc.
Almost every databricks feature makes huge concessions on the functionality you'd get if you just used that feature outside of databricks. For example databricks has it's own git-like functionality (which is the 5% of git that gets most used, but no way to do the less common git operations).
My personal take is databricks is fine for users who'd otherwise use their laptop's computer/memory - this gets them an environment where they can access much more, at about 10x the cost of what you'd pay for the underlying infra if you just set it up yourself. Ironically, all the databricks-specific cruft (config files, click ops) that's required to get going will probably be difficult for that kind of user anyway, so it negates its value.
For more advanced users (i.e. those that know how to start an ec2 or anything more advanced), databricks will slow you down and be endlessly frustrating. It will basically 2-10x the time it takes to do anything, and sap the joy out of it. I almost quit my job of 12 years because the org moved to databricks. I got permission to use better, faster, cheaper, less clunky, open-source tooling, so I stayed.
Note that Databricks SQL Serverless these days can be provisioned in a few seconds.
That's the point. Our org was told databricks would solve problems we just didn't have. Serverful has some wonderful advantages: simplicity, (ironically) cheaper (than something running just 3-4 hours a day but which costs 10x), familiarity, reliability. Serverless also has advantages, but only if it runs smoothly, doesn't take an eternity to boot, isn't prohibitively expensive, and has little friction before using it - databricks meets 0/4 of those critera, with the additional downside of restrictive SQL due to spark backend, adding unnecessary refactoring/complexity to queries.
> your setup is not really practical to have a lot of people collaborating
Hard disagree. Our methods are simple and time-tested. We use git to share code (100x improvement on databricks' version of git). We share data in a few ways, the most common are by creating a table in a database or in S3. It doesn't have to be a whole lot more complicated.
But you are doing a disingenuous comparison here because one can keep a "serverful" cluster up without shutting it down, and in that case, you'd never need to wait for anything to boot up. If you shut down your EC2 instances, it will also take time to boot up. Alternatively, you can use the (relatively new) serverless offering from them that gets you compute resources in seconds.
We had 8 data engineers onboarding the org to databricks, it was only after 2 solid years before they got to working on serverless (it was because users complained of user unfriendliness of 'nodes', and managers of cost). But then, there were problems. A common pattern through my grep of slack convos is "I'm having this esoteric error where X doesn't work on serverless databricks, can you help".. a bunch of back and forth (sometimes over days) and screenshots followed by "oh, unfortunately, serverless doesn't support X".
Another interesting note is someone compared serverless databricks to bigquery, and bigquery was 3x faster without the databricks-specific cruft (all bigquery needs is an authenticated user and a sql query).
Databricks isn't useless. It's just a swiss army knife that doesn't do anything well, except sales, and may improve the workflows for the least advanced data analysts/scientists at the expense of everyone else.
Are you doing this on EBS? Honest question.
I'd have rather stuck with Spark just because I prefer Scala or Python to SQL (and that comes with e.g. being far easier to unit test), but life happened and that ecosystem was getting disrupted anyway.