Amazon AI Superpowers, DataOps with Chris, TrinoDB; ThDPTh #56
I’m Sven, I collect “Data Points” to help understand & shape the future, one powered by data.
If you only have 30 seconds to spare, here is what I would consider actionable insights for investors, data leaders, and data company founders.
DataOps is still new. DataOps, making data teams more productive by applying concepts from agile, software engineering, and manufacturing has been around for years, but it’s slow to catch on. I have no idea why.
Data network effects will eat up the fashion space. Amazon leverages its AI superpowers to start taking over the physical in-store fashion space. Another example of how the competition will look like in a couple of decades, solely based on number-crunching capabilities.
What: Amazon will open up an Amazon fashion store later this year in the LA area. It will reimagine the fashion shopping experience with AI, especially focused on recommendations.
My Perspective: This is a creepy and yet very effective example of how I believe the world will turn out in a couple of decades. Amazon is leveraging is super-powers in data-crunching into a competitive advantage in a different field.
Data will be at the heart of everything in the future, so data crunching will be the superpower. Fashion stores might simply be ill-equipped to develop this superpower, so in the future, it might just as well be the Google’s and Amazons of this world that own fashion, develop drugs, and give medical advice.
These are data network effects at their best, and creepiest if you spin that idea a bit further.
What: This is a very interesting talk from Christopher Bergh, one of the key people in the DataOps space and CEO of datakitchen.io.
My perspective: I love his introduction “deliver the wrong data to a 5,000 sales team and get the call — do this again, and you’re out…”. That’s the life of most data people, and exactly what DataOps is there to solve.
“Chris, I thought this should take 2 hours, not 2 weeks” — exactly the feeling I always have, “it’s just adding this one attribute, how can that be like weeks worth of doing stuff??”
Chris is using the river metaphor to explain, that data people are mostly fixed on solving the downstream problems, but rarely go upstream where everything is about “how do I get more flow out of my team?”.
I do remember my experience when I turned to DevOps and learned about all these things we do to treat infrastructure as code. So the obvious question is: We’re doing this to software, and infrastructure, why not in the data space? And why not to data?
A few more highlights:
“Analytics customers often don’t know what they want. If you give them something they can feedback, you get a better work product in the end.”
“A developer should be able to tell from his desktop whether a change he’s made is good or not. Whether it’s going to break production.”
However, there is also one image I do not like. Chris is always using the assembly line metaphor using cars. He is pointing out, that in DataOps we are really dealing with two assembly lines, one for the new data coming in, and one for our new innovations. But I think he is failing to make the point that currently only one of them, the “innovation pipeline” is behaving like a car assembly line. The other one is much closer to a “bulk process” like cleaning & sorting sand where you focus on statistics and not deterministic steps.
I still recommend you watch it, I really like his style and the experience he backing it up with.
What: It’s a simple introduction to trino explaining one of the key benefits of this cool query engine, to join across multiple data sources.
My perspective: I am slowly getting into trino & this world, and I enjoyed the basic explanation of things in the article.
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