🐰 #31 Starburst, Data Stack Show, We don’t need data scientists; ThDPTh #31 🐰

Data will power every piece of our existence in the near future. I collect “Data Points” to help understand & shape this future.

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I just listened to Justin Borgman on the opensourceunderdogs podcast and found it really interesting to understand more about presto/trino, especially three things:

I particularly like how Justin discusses the decoupling aspect in the sense that trino is kind of an analytical database without storage. No need to copy data around, stuff can happen at query time. Of course, the guys at firebolt will have some things to say about whether that’s actually feasible or not, but I like the general direction.

I also like the project because it empowers teams to have an abstraction layer that essentially allows doing piece-by-piece migrations of data warehouses or data lakes; A concept I find very important. After all, I think you should be able to exchange all of the major components of your tech stack every 6–12 months.

Mihail Eric, a machine learning researcher, did some digging into the data behind current “data” job openings and came up with pretty stunning numbers, 70% more job openings in the “data engineering” space compared to the “data scientist” space.

Yeah, that is it! All I wanted to share, just the number this time. Just let it sink in.

Stuff that might be interesting for anyone at the front line of the data world, inside a data company, inspired by much positive feedback from my article on commercial open source software data companies.

I had a really fun time discussing lots of different topics in the data space and explaining some ideas about the four “dragons of data” on the data stack show, which is featured by RudderStack. The four dragons are the “DAKS”:

- Decentralization of data

- Amount of data

- Kinds of data structures

- The Data Snowflake Problem

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