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It feels like these companies haven't found the right value metric to price along. Ideally it should align with the value the customer receives.
But that's almost impossible to measure by Snowflake. How would they know how much more revenue you earned because you use Snowflake?
Another example of misaligned incentives is LinkedIn. LinkedIn charges $3/message. The more messages sent on their platform, the more money they make. They are not incentivized to help sales or recruiters target the right people. It can be a cash cow in the short term, but it creates a negative experience for your users.
The fact that it has worked for so long is a testament to how strong network effects are.
In the case of Snowflake, high switching costs will protect them for a while.
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It doesn't lower barriers to entry, it's contrary to logical expectations for someone unfamiliar with how BQ works. If the query is limited to 10 results you wouldn't expect it to scan all 2 trillion of your records. Granted there are numerous warnings in the GUI for these types of things but make this mistake in Python and you're none the wiser.
Wait are you saying the BQ db engine is not following logical expectations? You do realize a "limit" clause doesn't prevent a full table scan in all cases, right?
Great article. On the surface, it's about Snowflake. At a deeper level, the article is about the perverse incentives motivating SaaS businesses to do seemingly dumb, inefficient things and avoid seemingly obvious optimizations by default. Many SaaS businesses are perfectly happy to let customers shoot themselves in the foot if it generates more revenue. The BigQuery example (presently, by default, `select * from tabl…
Wow their statement about not participating in benchmarking wars is alarming. In this day and age, when benchmarking tools are so inexpensive and almost everything is very transparent, why not participate. Or even better engage with a neutral third party such as Jepsen to get on an even playing field and duke it out.
It's like the cloud in general, the cost is high but so is the hype. When all that dust settles over the coming years the business will start shopping on price. They will then realize they have been locked in to some extent and will need to start wriggling loose of the lock-in.
Where does all this data go? It's processed and then what? Sent to decision makers? Used to run automated processes? I'm genuinely curious and would appreciate anyone who could show a real life example of this kind of pipeline where data is accumulated, then processed, then turned into revenue at the other end. I've implemented systems that do this but my experience is that accumulating data is (too) easy, processing…
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I think the point of the article is that an optimizer doesn't affect the barrier to entry at all, but adding it would save end users quite a bit of money. So they don't do it because end users' money is revenue for Snowflake/Alphabet
If you could just add an optimizer why doesn't the db engine just do that?
Me: My builds are really slow
CircleCI: Here are a few very low effort answers
Me: git checkout is taking literally 60 seconds, but it takes 3 seconds locally, why?
CircleCI: Mumble Mumble.
They charge per minute, so why would they care if builds are slow? Was about a year of this getting worse and worse, till I finally cancelled the service last week and built my own server in my basement.
I know get 200% faster builds, and the hardware payback time is not very long (6 months of my CircleCI bill?).
I think it's a huge red flag anytime the metric you care about is something that being "worse" makes the provider more money.
Great article. On the surface, it's about Snowflake. At a deeper level, the article is about the perverse incentives motivating SaaS businesses to do seemingly dumb, inefficient things and avoid seemingly obvious optimizations by default. Many SaaS businesses are perfectly happy to let customers shoot themselves in the foot if it generates more revenue. The BigQuery example (presently, by default, `select * from tabl…
It feels like these companies haven't found the right value metric to price along. Ideally it should align with the value the customer receives.