Why Your Solar Forecasting Model Doesn't Trust Every ZeroA data quality pattern for sensor dropout at IoT scale, using Lakeflow Declarative Pipelines (formerly DLT)A solar panel producing zero output at 2pm on a clear day is a maintenance ticket. A s...
Part 5 - the final post in my enterprise data platform series - is up. This one is about what actually happened when we connected BI tools to the Views layer.We had two tools: Tableau for the finance team's analytical workbooks, and Databricks Dashbo...
For years, enterprise data architecture has followed a familiar pattern.An application writes customer orders, account updates, inventory changes, or transactions into an operational database.Then data engineering takes over.We capture changes throug...
IntroductionDifferent organizations use different names for these artifacts: business requirements, mapping specifications, source-to-target mappings, data contracts, transformation rules, or semantic definitions. The name matters less than the goal:...
Part 4 of my enterprise data platform series is up - this one cover why we added a fifth layer to the standard medallion architecture.We connected BI tools to the Gold layer and immediately hit four problems Gold alone couldn't solve:Schema breaks wh...
Nobody told the analytics team they couldn't query the raw customer table. So, they did.Full names, email addresses, phone numbers - exported to a CSV for "a quick look." No alert fired. No one flagged it. We found out three weeks later during a comp...
Part 3 of my series on building an enterprise data platform on Databricks is up - this one cover Gold layer design.The short version: Gold isn't just aggregated Silver. Silver maps to your source system. Gold maps to the business questions your consu...
Part 2 of my series on building an enterprise data platform on Databricks — this one's about Silver.Part 1 covered why we ran two ingestion paths in parallel (GoldenGate CDC + JDBC batch) and kept them as separate bronze tables. If you missed it:http...
Part 1 of a 5-part series on building an enterprise data platform on Databricks.When migrating a large retail conglomerate's SAP HANA platform to Databricks, we needed both historicalcompleteness and near-real-time freshness from day one.That require...
Hi Everyone! This is my official submission for DAIS 2026 Community Virtual Contest!Deal2Delivery: How I Built an End-to-End AI Sales Intelligence Platform on DatabricksEvery sales team has the same nightmare: a deal closes, and then nobody knows if ...
In Part 1, we covered why multi-table transactions matter. Now let's build one. We'll create the tables from the claim wrap-up scenario, load sample P&C insurance data, and walk through what happens when the wrap-up succeeds, when it fails, and when...
Most Databricks streaming failures don't look dramatic.No cluster termination. No red wall of errors. The UI says RUNNING — and your customers start reporting nonsense.I wrote about the incident that changed how we think about streaming jobs on share...
Completely agree, production war stories are worth more than any documentation. I’ve eaten enough teeth on production data lake issues to write my own chapter on what can go wrong, whether that’s deploying Databricks in financial institutions or bein...
Databricks is a popular unified data analytics platform known for its powerful data processing capabilities and seamless integration with Apache Spark. However, managing and optimizing costs in Databricks can be challenging, especially when it comes ...
@Second Reply You’re right just printing out selected_pool isn’t enough to actually leverage dynamic cluster sizing at runtime. In practice, the value of selected_pool would feed directly into your Databricks cluster creation API or workflow automati...
Tips and Techniques for Ingesting Large JSON files with PySparkIntroductionSuppose you’ve ever struggled or grappled with consuming massive JSON files with PySpark. In that case, you are aware that insufficient data can always creep in and silently d...
The Apache Spark™ 4.0 introduces a new feature for SQL developers and data engineers: SQL Scripting. As such, this feature enhances the power and extends the flexibility of Spark SQL, enabling users to write procedural code within SQL queries, with t...
