I keep hearing how much better the Apple Watch is but I still can't see how.
To start with, the GW6 classic looks like an actual classy watch while the Apple watch looks like a tiny phone strapped to your wrist.
I keep hearing how much better the Apple Watch is but I still can't see how.
To start with, the GW6 classic looks like an actual classy watch while the Apple watch looks like a tiny phone strapped to your wrist.
https://docs.aws.amazon.com/redshift/latest/dg/querying-iceb...
I ask because, if I didn't know either word, the one would mean, to me, "tiny storage next to a big body of data" and the other would mean "a big body of data".
Databricks (with Delta as the underpinning) seems to have lead the charge of lakehouse meaning, your data lake+file formats/helpers+compute==data lake+datawarehouse==lakehouse.
The latter seems to be the prevailing definition today with the former aging in place.
Can we please fucking stop pretending that every work of fiction must be about several factions in shades of grey fighting one another in a universe where Objective Good and Evil aren't a thing?
Reminds me of the Astound application around 1995-1996. It was a pretty major PowerPoint competitor and I used it a lot. For whatever reason, they only had a Windows 3.1 version. So I called them asking when the Windows 95 version was coming out. They told me they didn't think Windows 95 will be anything more than a fad and therefore they are sticking with what they have.
I remember us laughing in the office about how an entire company could be so clueless.
But people eventually found its limitations. And did it quite fast. People learned that it is not as trustworthy as initially thought and it is also very convincing when it is wrong. It maybe very interesting to generate texts, to startup small code for functions, to query information it has "cataloged", find some trivial mistake, make suggestions and... well, not much more than that. It may save time to boot projects, but it is not capable of managing anything larger than its "memory".
I think people are now actually more impressed by things it can't do easily. It can't play hangman, chess, tic-tac-toe... It got the phase of the Moon wrong when I asked it "What was the phase of the Moon when John Lennon was killed".
So, once people get hit by one of its mistakes or limitations, it sticks more than the "impressive part". That means, people will certainly ask themselves "Should I trust a thing that can't even play tic-tac-toe?"
That mixes the uses cases of analytics and operations because everyone is led to believe that things that happened in last 10 minutes must go through the analytics lens and yield actionable insights in real time so their operational systems can react/adapt instantly.
Most business processes probably don't need anywhere near such real time analytics capability but it is very easy to think (or be convinced that) we do. Especially if I am a owner of a given business process (with an IT budget) why wouldn't I want the ability to understand trends in real-time and react to it if not get ahead of them and predict/be prepared. Anything less than that is seen as being shamefully behind on the tech curve.
In this context-- the section in article where it says present data is of virtually zero importance to analytics is no longer true. We need a real solution even if we apply those (presumably complex and costly) solutions to only the most deserving use cases (and not abuse them).
What is the current thinking in this space? I am sure there are technical solutions here but what is the framework to evaluate which use case actually deserves pursuing such a setup.
Curious to hear.
If asked people would say "I need to always be up" until they see the costs associated with it, then being out for a few hours a year tends to be ok.
One of their most interesting offerings coming up is Snowpark which lets you run a Python function as a UDF, within Snowflake. This way you don't have to transfer data around everywhere, just run it as part of your normal SQL statements. It's also possible to pickle a function and send it over... so conceivably one could train a data science model and run that as part of a SQL statement. This could get very interesting.
Is that a differentiator? I'm unfamiliar with Snowpark's actual implementation but know SQL Server introduced Python/R in engine in 2016? something like that.
Sunnier areas have less skin cancers, if that's what you were wondering.
Now is it because of the Vitamin D, or because of a more systemic application of sunscreen?
Is there really such a thing as a bad query that can be rewritten to give the same results but faster? For me, that's already the query optimizer's job.
Of course there are "bad queries" where you query for things you don't need, join on the wrong columns, etc. And yeah the optimizer isn't perfect. But a query that you expect the query optimizer to "rewrite" and execute in an optimal way is a good query.
I can't tell if your disclaimer covers it but, yes, there are lots of bad queries that take a little bit of a re-write and run significantly faster. Generally it is someone taking a procedural vs set based approach or including things they don't need to try and help (adding an index to a temp table when it is only used once and going to be full scanned anyways). That's outside the general data typing/generally missing indexes.