7 posts
> Building point-in-time correct, production-grade feature pipelines — from raw Kafka events to online feature serving in milliseconds, using Spark Structured S...
> A deep dive into how Spark transforms your SQL into a physical execution plan — and how Databricks layers Adaptive Query Execution and the Photon vectorized e...
Master Google's Gemma 4 open-weight model. Our guide covers architectural innovations, knowledge distillation, and real-world Python deployment strategies.
If you've been building with AI agents, you've probably hit the same wall I did: your agent needs to do things — query databases, call APIs, check systems — but...
Azure Databricks doesn't live in isolation. In most enterprise Azure environments it sits downstream of Azure Data Factory, which orchestrates ingestion from da...
Batch pipelines are predictable. They run, they finish, you check the results. Streaming pipelines are alive — they never stop, failures compound, and a small m...
If you've ever spent more time debugging broken Spark jobs than actually building pipelines, Databricks Delta Live Tables (DLT) is worth your attention. Instead...