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I can’t help but feel this is mostly hype driven by a company that’s looking to reinvent themselves while facing the prospect of irrelevance. The piece reads li

I can’t help but feel this is mostly hype driven by a company that’s looking to reinvent themselves while facing the prospect of irrelevance. The piece reads like it’s a press release, with abstract things such as “corporate leaders under pressure to get their data ready for AI”. That has nothing to do with LLMs, as the current AI “hype” cycle has been going on for almost a decade already. Corporate leaders have always been under pressure to get their data cleaned and in the correct shape, and Databricks’ business model is to sell to these corporate leaders expensive packages combined with consultancy with generally very high operational costs. Compared to 5 years ago, nowadays I rarely encounter people that actually care about Databricks’ product offering.

anonylizard 3y ago
A rising tide lifts all boats. Databricks may have its thunder stolen by snowflake. But the true AI boom happening right now, benefits many data product vendors. The basic requirement for enterprises to use LLMs is having their data in order, which basically requires a cloud data warehouse. It is simply responsible to profit off the hype for a established data company.
- DebtDeflation 3y ago
  > The basic requirement for enterprises to use LLMs is having their data in order, which basically requires a cloud data warehouse. I'm curious why you would say this. The main use cases for LLMs involve things like customer service chatbots, knowledgebase search, document summarization, co-pilot/code generation, and content generation for product descriptions and marketing emails. The main data sources would be things like document repositories on Sharepoint and transcripts of customer calls. Not a bunch of historical sales data or financial data sitting in the typical data warehouse. I think there's a big misconception about this. Data sources for Generative AI are very different than data sources for 2013 era data science projects (which perhaps not coincidentally is what led to the development of things like Databricks).
  - deleted 3y ago
    [deleted]
kadomony 3y ago
As someone new to the data ops platform world.. what does Databricks even do? They get so much hype as a great employer, but why are they attracting people if their secret sauce is irrelevant?
- moneywoes 3y ago
  Further, what is it different from snowflake that they do?
  - pram 3y ago
    The biggest difference to consider is storage, I guess. Databricks clusters can store in S3 or wherever you want really. Snowflake has their own storage layer and it always lives in their cloud. This precludes any on-prem deployments.
    - ramraj07 3y ago
      You can’t be more wrong? Snowflake also stores in s3 or elsewhere, in fact it’s like the only common feature. Snowflake uses proprietary formats mostly though and only gives a sql interface. Think of it as Apple vs android.
      - cfeduke 3y ago
        > You can’t be more wrong? Snowflake also stores in s3 or elsewhere Snowflake stores data in S3 in Snowflake's various AWS accounts. Egress fees are necessary when performing anything outside of Snowflake. Databricks operates on data stored in your S3 on your AWS account(s). Databricks also runs on your compute contained within your AWS account(s). Both approaches have valid use cases.
        chrisjc 3y ago
        > Snowflake stores data in S3 in Snowflake's various AWS accounts. Egress fees are necessary when performing anything outside of Snowflake. Egress fees are only if you are unloading into a different region/cloud. If your data is in SF AWS us-east-1, and you unload to your S3 in us-east-1, there is no egress fee. > Snowflake charges a per-byte fee for data egress when users transfer data from a Snowflake account into a different region on the same cloud platform or into a completely different cloud platform. Data transfers within the same region are free. https://docs.snowflake.com/en/user-guide/cost-understanding-data-transfer https://docs.snowflake.com/en/user-guide/cost-understanding-...
throwaway54232 3y ago
This was my guess as well. I am wondering what you see as the "prospect of irrelevance" they are facing. I have my guess but I would like to hear your take.
- stingraycharles 3y ago
  People are doing more and more self-hosting and desire PaaS-like offerings that run in their own cloud, rather than SaaS “we manage your spark cluster for you” which is what DataBricks was founded upon. PaaS has significantly thinner margins, but it’s desirable from the customers’ perspective as it’s cheaper and much better from a (data) security point of view. As such, they’ll be forced to do a lot more services oriented work rather than product / platform oriented work, because it pays well. Their sales team is also excellent. I see a similar fate for them as Cloudera.
  - kthejoker2 3y ago
    > People are doing more and more self-hosting and desire PaaS-like offerings that run in their own cloud, rather than SaaS Do you have some data to support this? This is a pretty bold claim.
    - hodgesrm 3y ago
      +1. I would be delighted to see that data as well. I mean we all like the idea of local control but local PaaS is hard to do in a general way.
  - bobbruno 3y ago
    You can find an article on Bloomberg stating that Databricks’ annual revenue has grown by 60%, which sort of backs the SaaS hypothesis it bets on. Do you have data on your PaaS hypothesis that is bigger than that? Source: https://www.bloomberg.com/news/articles/2023-06-13/databricks-hits-1-billion-in-annual-sales-while-adding-data-warehouse-tool https://www.bloomberg.com/news/articles/2023-06-13/databrick...
spmurrayzzz 3y ago
I don't know if I'd say this is reinventing themselves, or at least not from the ground up, they've been somewhat active in the open source LLM scene for some time now. Not that releasing dolly alone caused some major foundational shift, but it was a solid contribution. Additionally, acquiring Mosaic fills a pretty gaping hole in their existing product offering. The whole process of pretraining, fine-tuning, and evaluating open source LLMs in production is a total nightmare currently. Mosaic's stack at least attempts to make that better (on top of releasing solid foundational models themselves). > “corporate leaders under pressure to get their data ready for AI”. That has nothing to do with LLMs I agree that its buzzwordy and a little abstract. But also in my experience, getting "data ready for AI" is actually the primary constraint many orgs have with respect to using LLMs in an enterprise context. Their data is not stored in a way thats easy to tokenize/label/embed for effective training or fine-tuning. And you could argue the preprocessing actually should the easy part for non-ML devs to tackle, as its primarily a software engineering problem. Yet, its still the thing that keeps many folks from getting started (before you even get to attempt to tackle the inference problem).
- ethbr0 3y ago
  From my understanding, Databrick's core value proposition is -- 'We let you do things you want to, with the people you have.' It may surprise HN to learn, but most companies don't have top-tier technical talent. Consequently, how do you do {cutting edge thing board and investors are demanding company do} with the people you have/can afford? Use an offering that decreases the necessary skill level by providing powerful prebuilt components. From this perspective, LLM integration sounds like a perfect fit. It's something every company is being asked to have a plan on, but one few are technically staffed to execute on their own.
  - stakhanov 3y ago
    > [...] LLM integration [...] is something every company is being asked to have a plan on, but one few are technically staffed to execute on their own. But this is still the "language of hype". LLM integration to do what? I'm not saying they're not doing anything useful, just that this is not a way to describe either a productivity-enhancing technology (feature) or an actual value proposition (benefit) in a meaningful way.
pletnes 3y ago
What has replaced Databricks, in your opinion? Doesn’t seem «dead» to me, yet. Serious question.
- aynyc 3y ago
  Their moat was to be able to spin up spark clusters for you. This was very useful when companies just starting in that space and don’t have the skills. But with K8s and clusters becoming more common for normal workload, it’s not hard to manage your own spark clusters anymore. There goes their moat. Their other offerings just aren’t that amazing. People now don’t just want to buy hand tools anymore, they want power tools. AI/ML and its cousins are in the power tool space. Databrick cost is stupid high, like snowflake. Any companies looking to chop budget would put those contracts up first.
  - disgruntledphd2 3y ago
    Their moat is their phenomenal sales team. Like, I'm at company 3 that pays for databricks. Amount of those companies that use Spark = 0. I've given up complaining now.
    - esafak 3y ago
      So what are they using, the notebooks?
      - pletnes 3y ago
        But in the notebooks you run pyspark / scala spark / spark sql code?
        esafak 3y ago
        Yes, you can, if you want.
        disgruntledphd2 3y ago
        Yes, but for most of these companies they don't actually run (much or any) spark stuff, they just use the notebooks. It's really depressing, tbh but I've made my peace with it now.
quickthrower2 3y ago
Looking at the Mosaic website, and looking at many AI startups, I can't help feeling like they are all small AWS Sagemaker, Bedrock, Trainium, ... (etc.) competitors, and not sure how they will compete with the sheer capital that Amazon has, and potential to offer that compute power cheaper (at least long enough to kill off most of the competition). Maybe it is it the off chance that one of these companies might become a big player worth billions that makes the odds worth it?
- george_123 3y ago
  A lot of AWS services (especially SageMaker) aren’t very good in customer experience. People buy them for nominal capabilities and AWS core bread and butter — short-term and long-term reliability. Most of these startups (AI and others) have to offer a compelling product before even being notable. Besides, AWS top level doesn’t care if you use sagemaker or not. There’s a premium but if you’re still using EC2 via another startup, they’re still capturing lions share of value.
  - dbish 3y ago
    Sagemaker is probably a bad example since most folks who run ML workloads on AWS don't use it (from what I've seen talking to many ML teams). It's partially a scattered focus on their customer type (are they for experts/non-experts/something in between?, what exact use cases are they covering?) and partially I think just bad UI/UX (related to customer focus). AWS will converge on what is working for ML platforms and just build their own as time goes on. First mover advantage won't matter and these earlier ML platforms will be consumed imho
- tim_sw 3y ago
  The devil is in the details. Training large LLMs requires a lot of custom infra (handling GPUs going down, efficiently pushing data to keep the accelators busy, deciding on which mechanism of parallelizing model training is better - data vs model parallelism or both, tuning hyperparams of optimizers which can be different for larger batch sizes, etc) Mosaic is one of the better providers for this. AWS is nowhere near ready at this current point in time, it is pretty much a "dumb" infra provider in large LLM training at this point. (Of course they won't be standing still and will prob acquire that capability one way or another)
fakedang 3y ago
Just curious, how is Databricks going to be irrelevant? I thought they were a reasonably powerful database provider for data analytics platforms in many companies?
- millenseed 3y ago
  Yeah that's an absolutely garbage idea. Databricks is basically the opposite of irrelevant at this point.

anonylizard 3y ago

A rising tide lifts all boats. Databricks may have its thunder stolen by snowflake. But the true AI boom happening right now, benefits many data product vendors. The basic requirement for enterprises to use LLMs is having their data in order, which basically requires a cloud data warehouse. It is simply responsible to profit off the hype for a established data company.

DebtDeflation 3y ago
> The basic requirement for enterprises to use LLMs is having their data in order, which basically requires a cloud data warehouse. I'm curious why you would say this. The main use cases for LLMs involve things like customer service chatbots, knowledgebase search, document summarization, co-pilot/code generation, and content generation for product descriptions and marketing emails. The main data sources would be things like document repositories on Sharepoint and transcripts of customer calls. Not a bunch of historical sales data or financial data sitting in the typical data warehouse. I think there's a big misconception about this. Data sources for Generative AI are very different than data sources for 2013 era data science projects (which perhaps not coincidentally is what led to the development of things like Databricks).
- deleted 3y ago
  [deleted]

kadomony 3y ago

As someone new to the data ops platform world.. what does Databricks even do? They get so much hype as a great employer, but why are they attracting people if their secret sauce is irrelevant?

moneywoes 3y ago
Further, what is it different from snowflake that they do?
- pram 3y ago
  The biggest difference to consider is storage, I guess. Databricks clusters can store in S3 or wherever you want really. Snowflake has their own storage layer and it always lives in their cloud. This precludes any on-prem deployments.
  - ramraj07 3y ago
    You can’t be more wrong? Snowflake also stores in s3 or elsewhere, in fact it’s like the only common feature. Snowflake uses proprietary formats mostly though and only gives a sql interface. Think of it as Apple vs android.
    - cfeduke 3y ago
      > You can’t be more wrong? Snowflake also stores in s3 or elsewhere Snowflake stores data in S3 in Snowflake's various AWS accounts. Egress fees are necessary when performing anything outside of Snowflake. Databricks operates on data stored in your S3 on your AWS account(s). Databricks also runs on your compute contained within your AWS account(s). Both approaches have valid use cases.
      - chrisjc 3y ago
        > Snowflake stores data in S3 in Snowflake's various AWS accounts. Egress fees are necessary when performing anything outside of Snowflake. Egress fees are only if you are unloading into a different region/cloud. If your data is in SF AWS us-east-1, and you unload to your S3 in us-east-1, there is no egress fee. > Snowflake charges a per-byte fee for data egress when users transfer data from a Snowflake account into a different region on the same cloud platform or into a completely different cloud platform. Data transfers within the same region are free. https://docs.snowflake.com/en/user-guide/cost-understanding-data-transfer https://docs.snowflake.com/en/user-guide/cost-understanding-...

throwaway54232 3y ago

This was my guess as well. I am wondering what you see as the "prospect of irrelevance" they are facing. I have my guess but I would like to hear your take.

stingraycharles 3y ago
People are doing more and more self-hosting and desire PaaS-like offerings that run in their own cloud, rather than SaaS “we manage your spark cluster for you” which is what DataBricks was founded upon. PaaS has significantly thinner margins, but it’s desirable from the customers’ perspective as it’s cheaper and much better from a (data) security point of view. As such, they’ll be forced to do a lot more services oriented work rather than product / platform oriented work, because it pays well. Their sales team is also excellent. I see a similar fate for them as Cloudera.
- kthejoker2 3y ago
  > People are doing more and more self-hosting and desire PaaS-like offerings that run in their own cloud, rather than SaaS Do you have some data to support this? This is a pretty bold claim.
  - hodgesrm 3y ago
    +1. I would be delighted to see that data as well. I mean we all like the idea of local control but local PaaS is hard to do in a general way.
- bobbruno 3y ago
  You can find an article on Bloomberg stating that Databricks’ annual revenue has grown by 60%, which sort of backs the SaaS hypothesis it bets on. Do you have data on your PaaS hypothesis that is bigger than that? Source: https://www.bloomberg.com/news/articles/2023-06-13/databricks-hits-1-billion-in-annual-sales-while-adding-data-warehouse-tool https://www.bloomberg.com/news/articles/2023-06-13/databrick...

spmurrayzzz 3y ago

I don't know if I'd say this is reinventing themselves, or at least not from the ground up, they've been somewhat active in the open source LLM scene for some time now. Not that releasing dolly alone caused some major foundational shift, but it was a solid contribution. Additionally, acquiring Mosaic fills a pretty gaping hole in their existing product offering. The whole process of pretraining, fine-tuning, and evaluating open source LLMs in production is a total nightmare currently. Mosaic's stack at least attempts to make that better (on top of releasing solid foundational models themselves). > “corporate leaders under pressure to get their data ready for AI”. That has nothing to do with LLMs I agree that its buzzwordy and a little abstract. But also in my experience, getting "data ready for AI" is actually the primary constraint many orgs have with respect to using LLMs in an enterprise context. Their data is not stored in a way thats easy to tokenize/label/embed for effective training or fine-tuning. And you could argue the preprocessing actually should the easy part for non-ML devs to tackle, as its primarily a software engineering problem. Yet, its still the thing that keeps many folks from getting started (before you even get to attempt to tackle the inference problem).

ethbr0 3y ago
From my understanding, Databrick's core value proposition is -- 'We let you do things you want to, with the people you have.' It may surprise HN to learn, but most companies don't have top-tier technical talent. Consequently, how do you do {cutting edge thing board and investors are demanding company do} with the people you have/can afford? Use an offering that decreases the necessary skill level by providing powerful prebuilt components. From this perspective, LLM integration sounds like a perfect fit. It's something every company is being asked to have a plan on, but one few are technically staffed to execute on their own.
- stakhanov 3y ago
  > [...] LLM integration [...] is something every company is being asked to have a plan on, but one few are technically staffed to execute on their own. But this is still the "language of hype". LLM integration to do what? I'm not saying they're not doing anything useful, just that this is not a way to describe either a productivity-enhancing technology (feature) or an actual value proposition (benefit) in a meaningful way.

pletnes 3y ago

What has replaced Databricks, in your opinion? Doesn’t seem «dead» to me, yet. Serious question.

aynyc 3y ago
Their moat was to be able to spin up spark clusters for you. This was very useful when companies just starting in that space and don’t have the skills. But with K8s and clusters becoming more common for normal workload, it’s not hard to manage your own spark clusters anymore. There goes their moat. Their other offerings just aren’t that amazing. People now don’t just want to buy hand tools anymore, they want power tools. AI/ML and its cousins are in the power tool space. Databrick cost is stupid high, like snowflake. Any companies looking to chop budget would put those contracts up first.
- disgruntledphd2 3y ago
  Their moat is their phenomenal sales team. Like, I'm at company 3 that pays for databricks. Amount of those companies that use Spark = 0. I've given up complaining now.
  - esafak 3y ago
    So what are they using, the notebooks?
    - pletnes 3y ago
      But in the notebooks you run pyspark / scala spark / spark sql code?
      - esafak 3y ago
        Yes, you can, if you want.
      - disgruntledphd2 3y ago
        Yes, but for most of these companies they don't actually run (much or any) spark stuff, they just use the notebooks. It's really depressing, tbh but I've made my peace with it now.

quickthrower2 3y ago

Looking at the Mosaic website, and looking at many AI startups, I can't help feeling like they are all small AWS Sagemaker, Bedrock, Trainium, ... (etc.) competitors, and not sure how they will compete with the sheer capital that Amazon has, and potential to offer that compute power cheaper (at least long enough to kill off most of the competition). Maybe it is it the off chance that one of these companies might become a big player worth billions that makes the odds worth it?

george_123 3y ago
A lot of AWS services (especially SageMaker) aren’t very good in customer experience. People buy them for nominal capabilities and AWS core bread and butter — short-term and long-term reliability. Most of these startups (AI and others) have to offer a compelling product before even being notable. Besides, AWS top level doesn’t care if you use sagemaker or not. There’s a premium but if you’re still using EC2 via another startup, they’re still capturing lions share of value.
- dbish 3y ago
  Sagemaker is probably a bad example since most folks who run ML workloads on AWS don't use it (from what I've seen talking to many ML teams). It's partially a scattered focus on their customer type (are they for experts/non-experts/something in between?, what exact use cases are they covering?) and partially I think just bad UI/UX (related to customer focus). AWS will converge on what is working for ML platforms and just build their own as time goes on. First mover advantage won't matter and these earlier ML platforms will be consumed imho
tim_sw 3y ago
The devil is in the details. Training large LLMs requires a lot of custom infra (handling GPUs going down, efficiently pushing data to keep the accelators busy, deciding on which mechanism of parallelizing model training is better - data vs model parallelism or both, tuning hyperparams of optimizers which can be different for larger batch sizes, etc) Mosaic is one of the better providers for this. AWS is nowhere near ready at this current point in time, it is pretty much a "dumb" infra provider in large LLM training at this point. (Of course they won't be standing still and will prob acquire that capability one way or another)

fakedang 3y ago

Just curious, how is Databricks going to be irrelevant? I thought they were a reasonably powerful database provider for data analytics platforms in many companies?

millenseed 3y ago
Yeah that's an absolutely garbage idea. Databricks is basically the opposite of irrelevant at this point.