I recently published a piece on Lakeflow Connect and wanted to share it here since this community is where the conversation actually happens.The post covers something most of us have lived through, the hidden cost of maintaining ingestion pipelines. ...
Building an Incremental Customer Data Migration Workflow in DatabricksBy Naveen AyallaIntroductionIn many enterprise environments, customer data is spread across legacy systems that were originally designed for operational processing rather than mode...
Great write-up, Naveen. Very practical and clear.I really like how you focused not just on migration, but on building a reliable incremental workflow with proper duplicate handling and governance. That’s where real value comes from.Also, connecting D...
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...
In Spark, data skew can be the silent killer of performance. One wide partition pulling in 90% of the data?But even with AQE (Adaptive Query Execution) turned on in Databricks, skewness isn't always automatically identified— and here’s why.What Is co...
@mark_ott , this question seems right up your alley. Care to comment?
Here is how to trained a lightweight Convolutional Neuronal Network (CNN) to detect pneumonia from chest X-rays pictures on Azure Databricks. I promise no LLMs, no hype, just real-world deep learning:1. Built it with TensorFlow & Keras on Databricks2...
Data Engineering has come a long way. From the days of manual ETL scripts to the modern world of automated, AI-driven data pipelines, the evolution has been nothing short of fascinating. As a data engineer working across various platforms, I’ve seen ...
