Welcome back to TechTalks with Manoj — the show where we get past the buzzwords and dig into what’s actually powering modern cloud-native architectures.
Today, we’re talking about a platform that quietly glues together data engineering, machine learning, SQL analytics, and even generative AI — all under one hood. Yep, we’re diving into Azure Databricks.
This isn’t just another Spark wrapper or a BI tool with dashboards. It’s a unified engine that lets you build pipelines, train models, query with SQL, stream live data, and fine-tune LLMs — all in the same ecosystem.
If you’ve ever bounced between Synapse, Spark clusters, ML tools, and governance messes — Databricks might just be the control plane you didn’t know you needed.
Here’s what we’re breaking down today:
What makes Azure Databricks more than “just Spark” — and how it evolved with Microsoft
Key concepts like workspaces, clusters, notebooks, and jobs — the real building blocks
The Big 5 workloads: data engineering, ML, SQL/BI, streaming, and generative AI
How Delta Lake, Auto Loader, and Unity Catalog simplify even complex pipelines
The data governance story — with Unity Catalog and Microsoft Purview working together
Real-world examples — from bronze-silver-gold dataflows to LLM-powered RAG pipelines
Cost control tips, cluster tuning insights, and scaling patterns you can actually use
Whether you’re a data engineer dealing with broken pipelines or an architect trying to unify governance, compute, and AI under one strategy — this episode will help you connect the dots.
Let’s jump in.
