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Same playbook - show that you’re better in a key metric that’s easy to understand (performance) to get the attention, but then pitch the paradigm change.
In Snowflake’s case, that was separation of storage and compute.
In Databrick’s case, it’s the Lakehouse Architecture.
I think the reason why Snowflake is so nervous because they know they can’t win this game.
While they can crunch large datasets, they are laughably slow for the datasets most people have. So while I did propose we use these solutions for our big-ish data projects, management kept pushing for us to migrate our tiny datasets (tens of gigabytes or smaller) and the perf expectedly tanked compared to our other solutions (Postgres, Redshift, pandas etc.), never mind the immense costs to migrate everything and train everyone up.
Yes, these are very good products. But PLEASE, for the love of god, don't migrate to them unless you know you need them (and by 'need' I don't mean pimping your resume).
Before the Snowflake blog post, I did not know what Snowflake or Databricks were. I can only imagine that this rivalry is great for both of them, even if Databricks is somewhat on the advantage end, at least from a tactical standpoint; I admit though that they seem to be a bit unnecessarily defensive considering the position they're in with the exchange. In general though, I'm still not complaining. It's interesting…
Earlier quoted context omitted.
Yes, I wasn't saying they were lying about their tpc.org posted results. I'm saying both companies made use of clever indirection, wording, presentations of stats, etc. Like price/performance, and which of your competitor's tier's to select when doing that, and which of your own. Or over-provisioning the competition's setup, for example.
Ahh, fair enough there. That said, snowflake would help their case if they would actually do at least one actual tpc.org third party result
Databricks is a rapidly approaching IPO. Trying to justify their valuation with their overpriced in-memory hadoop.
Databricks is way more than hadoop or spark. A great analogy - Spark is a great engine but you need to design and build all of the other subsystems. Databricks is an F1 car - everything is built out. You get in and drive - FAST.
F1 cars really unreliable and need a lot of engineers to keep running, are very expensive, and completely impractical in normal use. They are fast but only on very specific roads, they couldn't survive on normal roads.
What do you know, you might be right! :D
As much as I love seeing competition in the space and am enjoying my popcorn, I really don't understand what Databricks is doing here: this feels like a childish foodfight rather than an obsession with the customer...
* I haven't executed the test suite, but fraud seems likely.
Serious question: Databricks, Snowflake, Dremio. All these "Data" platform companies => which one do you have for your Data Lake and Data Warehouse solution? I'm sick and tired of these companies Snake Oiling the Data industry by offering "the easiest" platform to satisfy your Data Lake + Warehouse solution only to fall hard whenever you hook it up with your production data (big dataset). PS: Anyone selling Data Lake…
Data Lake + Merge support + DW performance is now possible.
That is the game changer.
Earlier quoted context omitted.
Databricks results are available at tpc.org [1] Snowflake has shown NOTHING close to this. [1] http://tpc.org/results/fdr/tpcds/databricks~tpcds~100000~dat...
Sorry to nitpick (document seems solid), on page 32: > Due to a TPC-internal error during the production of 3.2.0 of the TPC-DS kit, the benchmark execution had to use version 2.13 of the kit. It was confirmed by the TPC that the only changes between these two versions of the kit is the version number set in the tools/release.h parameter file. How can there be that much of a delta of major/minor versions without a ch…
I've used both products in production. Both are good++. The blog wars seem extremely ridiculous to me. I don't recall ever choosing one over another based on how fast it runs on some imaginary arbitrary dataset.
Manufactured rivalries can be a great thing for business. We have been debating Coke vs Pepsi, Nike vs Reebok, McDonald's vs Burger King for decades now while these companies laugh all the way to the bank.