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And we're all just pretending it's going to work out fine A data scientist told me something that perfectly captures our industry's delusion: "I spend most of my time wrangling data. I can't trust my data engineers because they don'
We have a problem at Matterbeam. Not a technical problem. A trust problem. Every company we talk to has heard our pitch before, or at least they think they have. "We'll solve your data pipeline mess." "No more broken integrations." "Data available everywhere,
A behind-the-scenes look at building AI infrastructure with Promoboxx We've been having a lot of conversations lately that start the same way: "We want to experiment with AI, but our data infrastructure is slowing us down. We're wasting all our time on $#&%!* pipelines instead
There’s a moment we kept seeing at ODSC East last week. Someone would be walking by, scanning booths like we all do at conferences—half-looking for free stuff, half-trying to avoid eye contact—and then they’d stop. Dead in their tracks. Not for a t-shirt. Not for a
Data problems in companies are not due to people's lack of skills, but from the wrong fundamental approach that doesn't align with how businesses actually operate and evolve.
Let's trace the evolution of the "data lake" concept The term "data lake" was coined by James Dixon, then CTO of Pentaho, in 2010. Dixon used the metaphor of a lake to contrast with the more structured "data mart" (which he compared
The developments of generative AI in the past few years have been amazing. There are promises of AI agents revolutionizing everything from customer service to software development. Now they're coming for your data infrastructure. The pitch is seductive: AI agents will automatically handle data integration, quality, and governance.
You’re Doing ETL Wrong – Here’s a Better Way If you’re still managing your ETL/ELT pipelines like it’s 2015, it’s time to rethink your approach. The recent pricing change by Fivetran is just another glaring example of why the current trend in data integration is
The lakehouse idea springs from a common pain point: warehouses excel at handling structured data and delivering strong analytics performance, but they falter when faced with unstructured data and scalability challenges. On the other hand, lakes shine with unstructured data and flexibility but struggle with governance, consistency, and transactional integrity.
