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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
A conversation with Galen Schreck, fractional CTO and enterprise architect, about the real challenges of working with Salesforce data and what needs to change. There’s a conversation that happens in almost every company using Salesforce: Sales team: “We need all our customer engagement data in Salesforce so we can
"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
You know what you're supposed to do. We've heard the same refrains for a decade or more. Conference keynotes. Blog posts. LinkedIn thought leadership. Build a data culture. Invest in data literacy. Improve data quality at the source. Get executive buy-in. Implement strong governance. Focus on
There's a version of a stat that gets thrown around a lot. Data teams spend 80% of their time on data preparation or cleaning. Eighty percent. We've just... accepted this? Like it's some law of nature? As if the universe decreed that for every
