Databricks Is Raising a Series K Investment at >$100b Valuation

[0]: https://appedus.com/indias-zomato-raised-500-million-in-seri...

[1]: https://techcrunch.com/2022/01/24/indian-food-delivery-giant...

[2]: https://www.finsmes.com/2015/12/palantir-technologies-raises...

It's a smaller piece of a bigger pie.

To answer your question, the right question to ask is why go public when you can remain private? Public means more paperwork, more legalese, more scrutiny, and less control for the founder, and all of that only to get a bit more liquidity for your stock. If you can remain private, there really isn't much of a reason to not do that.

My guess is that they might be about to embark on a shopping spree and acquire some more VC backed companies. They've actually bought quite a few companies already in the past few years. And they would need cash to buy more. The company itself seems healthy and generating revenue. So, it shouldn't strictly need a lot of extra capital. Acquisitions would be the exception. You can either do that via share swaps or cash. And of course cash would mostly go to the VCs backing the acquired companies. Which is an interesting way to liquidate investments. I would not be surprised to learn that there's a large overlap with the groups of VCs of those companies and those backing databricks. 100M$ on top of 10B sounds like somebody wants in on that action.

As a financial construction it's a bit shady of course. VCs are using money from big institutional investors to artificially inflate one of their companies so that it can create exits for some of their other investments via acquisitions financed with more investment. It creates a steady stream of "successes". But it sounds a bit like a pyramid game. At some point the big company will have to deliver some value. I assume the hope is some gigantic IPO here to offload the whole construction to the stock market.

Plus the markets are in a weird state right now.

It's a lot easier to stay long-term focused without investors breathing down your neck. As a private company you're not dealing with shortsellers, retail memers, institutional capital that wants good earnings now, etc..

Of course, the bad side is that if the company gets mismanaged, there's far less accountability and thus it could continue until it's too late. In the public markets it's far easier to oust the C-suite if things go south.

It's a shame that the trend of staying private longer means retail gets shut out from companies like this.

Databricks is great at offering a "distributed spark/kubernetes in a box" platform. But its AI integration is one of the least helpful I've experienced. It's very interuptive to a workflow, and very rarely offers genuinely useful help. Most users I've seen turn it off, something databricks must be aware of because they require admins permission for users to opt out of AI.

I don't mean to rant, there's lots that is useful in databricks, but it doesn't seem like this funding round is targeting any of that.

Just to clarify - for many years employees were getting the RSUs not options, just with the expiratation date attached - which is gone since this year.

It's easy to look on knowing lots about data tools and say "this could be better done with open source tools for a fraction of the cost", but if you're not a big tech company, hiring a team to manage your data platform for 5 analysts is probably a lot more expensive than just buying databricks.

It's a way to get those pesky Python people to shut up

Oh, and a CTO is always valued more if he manages a 5 million Databricks budget, where he can prove his worth by showing a 5% discount het negotiated very well, than a 1 million whatever-else budget that would be best in class. Everybody wins.

The CTO of a "traditional" company who is responsible for "implementing digital transition".

Aside from that I do get the feeling that most small and medium sized companies have been oversold on it - they don't really have enough data to leverage a lot of the features and they don't really have the skill a lot of the time to avoid shooting themselves in the foot. It's possible for a reporting analyst upskilling to learn the programming skill to not create a tangled web of christmas lights but not probable in most situations. There seems to be a whole cottage industry of consultancies now that purport to get you up and running with limited actual success.

At least it's an incentive for companies to get their data in order and standardise on one place and a set of processes.

In terms of actual development the notebook IDE feels like big old turd to use tho and it feels slow in general if you're at all used to local dev. People do kinda like these web based tools tho. Can't trust people all the time! There's VS code and PyCharm extensions but my team work mainly with notebooks at the moment for good or ill and the experience there is absolute flaky dogshit.

I think it's possible to make some good stuff with it and it's paying my bills at the moment, but I think a lot of the adoption may be doomed to failure lol

My team of 3 data scientists are able to support a culture of experimentation, data-informed decision making accross the entire org.

And we do all that 30k annual spend on databricks. That's less than 1/5 the cost of 1 software engineer. Excellent value for money if you ask me.

I really struggle to imagine being able to that any cheaper. How else we can engineer a hub for all of our data and manage appropriate access, run complex calculations in seconds, join data from so many disparate sources, at a total cost (tool + labor) <80k/yr. I double dare you to suggest or find me a cheaper option for our use case.

My working theory is that the UI, a low-grade web-based SQL editor and catalog browser, is more integrated that the hodgepodge of tools that we were using before, and people may gain something from that. I've seen similar with in-house tools that collect ad-hoc/reporting/ETL into one app, and one should never underestimate the value that people give to the UI.

But we give up price-performance; the only way it can work is if we shrink the workload. So it's a cleanup of stale pipelines combined with a migration. Chaos in other words.

[0]: https://www.databricks.com/company/newsroom/press-releases/d...

That costs a fair bit of dosh.

However genuinely curious about the thesis applied by the VC’s/Funds that invest in such a late stage round? Is it simply they are taking a chance that they won’t be the last person holding the potato? Like they will get out in series L or M rounds or the company may IPO by then. Either ways they will make a small return? Or is the calculus diff?

I can’t know if it’s completely true ofc, but that’s what employees are told.

/s

Computationally speaking - again depends on what your company does - Collect a lot of data? You need a lot of storage.

Train ML Models, you will need GPUs - and you need to think about how to utilise those GPUs.

Or...you could pay databricks, log in and start working.

I worked at a company who tried to roll their own, and they wasted about a year to do it, and it was flaky as hell and fell apart. Self hosting makes sense if you have the people to manage it, but the vast majority of medium sized companies will have engineers who think they can manage this, try it, fail and move on to another company.

There are better storage solutions, better compute and better AI/ML platforms, but once you start with databricks, you dig yourself a hole because the replacing it is hard because it has such a specific subset of features across multiple domains.

In our multinational environment, we have a few companies that are on different tech stacks (result of M&A). I can say Snowflake can do a lot of the things Databricks does, but not everything. Teradata is also great and somehow not gaining a lot of traction. But they are near impossible to get into as a startup, which does not attract new talent to give it a go.

On the ML side, Dataiku and Datarobot are great.

Tools like Talend, snaplogic, fivetran are also really good at replacing parts of databricks.

So you see, there are better alternatives for sure, cheaper at the same time too, but there is no drop-in replacement I can think of

If you don't need this features, specially the distributed one, going tall (single instance with high capacity, replicate when necessary) or going simpler (multiple servers but without spark coordinating the work) could be good options depending on your/the team's knowledge