241–250 of 257 posts
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I think this is a very privileged view. Most of the world does not have the culture of offering vegetarian/vegan meals. And if they did, living that lifestyle can be prohibitively expensive.
Weird, in my experience a vegetable based diet is way cheaper than one which involves meat...
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Here's a Github repository listing Phoenix LiveView workalikes for different languages and frameworks: https://github.com/dbohdan/liveviews ps: The BEAM is what makes LiveView so great, do not expect the same in the other environments.
Is the last sentence in your comment about developer experience, functionality, performance/scalability or something else?
May I ask, I’m curious: why is ethereum contracts/blockchain not mentioned in any reply?
I learned about DeFi recently and am now curious to learn more about smart contracts, the underlying code and everything surrounding it.
So forest admin is commercial for rails_admin or active_admin right?
I started off more bullish about ForestAdmin but then maybe I just couldn’t grok it but what it auto-generated wasn’t as elegant (connecting to a Postgres server) as I’d hoped.
Snowflake's `Snowpark` product that they recently announced, which is to bring Spark-like APIs to Snowflake. Having a DS background, I love what SQL-orchestration tool dbt (and peers) have enabled: data consumers to rapidly create our own safe data pipelines. There's easily a 10x productivity improvement for most of my transformation pipelines vs. when I write them in Python or PySpark. But batch ML and SQL are not t…
And not something like Spark on EMR?
Remember that "data is a team sport". Together, we try and make better decisions (in manual or automated ways). A DE can produce great data but it's only useful if it helps the DA/DS. There's a lot of friction there.
Most of that friction disappears with SQL-based orchestration tools (I mean specifically dbt here, but there are others). Suddenly the analyst can create the data they need! With minimal guidance from a DE.
That can be with Spark SQL (+ DeltaLake / Iceberg), or some warehouse. That's not the issue.
The issue is around keeping orchestration simple when you're not just doing simple stuff anymore. Keeping that DAG logical, clear, and smooth is difficult once you include non-SQL items.
This isn't solved by Spark UDFs unfortunately :)