111–120 of 214 posts
$2-3B in 2024 revenue based on estimates I can find. That’s a 33-50x revenue multiple lol. Also announcing the signed term sheet but not the close so this is a PR push to find more investors?
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This! We did some simple testing on their platform to integrate it into our product for a customer. In a few days of light work rang up a huge bill. Many multiples of what we spend on OpenAI, which gets heavy use.
Its unfair to compare Databricks to OpenAI because they're at very different points in the enshittification[1] process. OpenAI is still early, burning VC money to acquire customers by operating at a loss. This makes it appear cheap. DataBricks is further along, attempting to claw back the value they provided to customers by raising prices. [1] https://en.wikipedia.org/wiki/Enshittification
It doesn't look like a typical round for raising capital for investments. Instead: 1. Liquidity: Early investors could sell to late-stage investors, since they are not IPO. Their previous round looked like that. 2. Markup: The previous investors can increase their valuation by doing a round again. It also provides a paper valuation for acquiring new companies. That combined with preferred stock (always get 1x back) m…
So if I understand well, investors are not really investing for the company results, but more on the hope that people will continue to invest in the company? In a kind of a ... ponzi pyramid?
$2-3B in 2024 revenue based on estimates I can find. That’s a 33-50x revenue multiple lol. Also announcing the signed term sheet but not the close so this is a PR push to find more investors?
PE ratio of 40 isn't bad is this market actually. Mature companies like Google/Meta are hovering around 30.
It doesn't look like a typical round for raising capital for investments. Instead: 1. Liquidity: Early investors could sell to late-stage investors, since they are not IPO. Their previous round looked like that. 2. Markup: The previous investors can increase their valuation by doing a round again. It also provides a paper valuation for acquiring new companies. That combined with preferred stock (always get 1x back) m…
So if I understand well, investors are not really investing for the company results, but more on the hope that people will continue to invest in the company? In a kind of a ... ponzi pyramid?
Databricks is a fast-growing company with ~$4B in annualised revenue and huge potential.
Many rounds got some portion of the round for liquidity. Similarly, markup strategies are common and valid. For existing investors, it works because they have already done research on the company and believe in it, so they put their money where their mouth is. For the company, it may speed up their fundraising process.
Though those strategies carry some risks.
Are there any cheaper alternatives to Databricks, EC2, DynamoDB, S3 solution? Where cost is more predictable and controlled? What's a good roll your own solution? DB storage doesn't need to be dynamic like with DynamoDB. At max 1TB - maybe double in the future. Could this be done on a mid size VPS (32GB RAM) hosting Apache Spark etc - or better to have a couple? P.S. total beginner in this space, hence the (naive) qu…
For a few TBs of data, well partitioned and stored in parquet or some such format, you could just use duckdb on a single node.
Are there any cheaper alternatives to Databricks, EC2, DynamoDB, S3 solution? Where cost is more predictable and controlled? What's a good roll your own solution? DB storage doesn't need to be dynamic like with DynamoDB. At max 1TB - maybe double in the future. Could this be done on a mid size VPS (32GB RAM) hosting Apache Spark etc - or better to have a couple? P.S. total beginner in this space, hence the (naive) qu…
I don't think there is anything out there that really bundles everything exactly like databricks does. 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…
Maybe I wasn't super clear. Wasn't looking for a 1:1 replacement.
Trying to understand what other options are out there for small teams / projects that don't need all those enterprise features that Databricks offers (governance etc).
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Depends on how you define cheaper - you could set up Apache Iceberg, Spark, MLFlow, AirFlow, JupyterLab, etc and create an abomination that sort of looks like Databricks if you squint, but then you have to deal with set up, maintenance, support, etc. 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…
Don't worry, most places go straight with databricks and get a flaky as hell system that falls apart anyway, but then they can blame databricks instead of their own incompetence.
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This! We did some simple testing on their platform to integrate it into our product for a customer. In a few days of light work rang up a huge bill. Many multiples of what we spend on OpenAI, which gets heavy use.
It does have some good aspects for engineers working with these tools in some cases: https://ludic.mataroa.blog/blog/i-accidentally-saved-half-a-...
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yeah where IT blocks half of the config, and you disable half of the features that could make it great, just to make sure they definitely don't give control to..GASP... A DATA ENGINEER
To be fair, the Data Engineer is probably either a data analyst turned DBA who yearns for the comfort of SQL Server or worse, the lowest bidder.