A ritual playing out in boardrooms everywhere: the new data strategy presentation. This time it’s different. This time we’ll capture the value. The slides are beautiful. The architecture diagrams are comprehensive. Everyone nods. Nobody believes it. We’ve been here before. At least three times, in fact. The
December 4 at 11 a.m. PT / 2 p.m. ET Six-month data migration timelines. Projects on hold. Costs climbing. What if you could test your migration in parallel with production? Roll back mistakes without losing work? Compress months into weeks? Join us December 4 to see how Matterbeam makes
Matterbeam goes against the data industry's complexity addiction. We built it to let small companies access sophisticated data integration without enterprise budgets. You're not locked into decisions. Time is on your side. Transform and emit data fearlessly as new use cases arise.
Around 2015, I was leading an extraordinary architecture team in an effort to decompose Pluralsight’s monolithic application into distributed services. The system exhibited classic tight coupling symptoms: changes cascaded unpredictably into other components, deployment required coordinated release windows across multiple teams, and specific engineers became single points of failure
"Can I just get the data? Can I just get a dump? Can I please just connect to the database?" If you've worked in data in any organization for more than five minutes, you've heard this plea. Usually it comes from someone who just
Stop chasing tools and focus on business value. You've heard it a thousand times. So has every data team. We nod, agree, and then... buy another tool. Why? Because the tool obsession is a symptom of something deeper. We don't need better discipline. We need a better way.
Data doesn't work in companies, I think everyone feels this on some level. One reason I've heard repeated is that it's a people problem, a lack of data culture and data literacy. Companies spend millions on training programs, hire Chief Data Officers, bring in
The medallion architecture emerged as the data industry's answer to data lake chaos. Organizations had dumped vast amounts of raw data into cheap storage, creating impenetrable data swamps. The medallion's promise was elegant: three progressive layers—Bronze for raw data, Silver for cleaned data, Gold for
I talk to data professionals and they're frequently frustrated. For example spending three months migrating everything to Parquet files in their data lake. Clean, columnar, compressed. Beautiful. But now their real-time service team needs that same data, and now it's painfully slow because, well, scanning columns
