What is Databricks?
"Master the Databricks Lakehouse Platform — from infrastructure as code and governance to production-grade orchestration and deployment."
Databricks is a unified, cloud-based Lakehouse Platform built by the original creators of Apache Spark. It brings data engineering, data science, machine learning, and business intelligence together into one collaborative workspace, running on top of managed Spark compute and an open storage layer called Delta Lake.
Where a raw Spark cluster only gives you the execution engine, Databricks wraps it with everything a production data team actually needs day to day:
- Workspace & Notebooks — a shared, version-controlled environment for writing and running PySpark, SQL, Scala, or R code collaboratively.
- Clusters — fully managed, auto-scaling Spark compute, provisioned and torn down on demand without manual cluster administration.
- Delta Lake — an open-source storage format that adds ACID transactions, schema enforcement, and time travel on top of plain Parquet files in cloud object storage.
- Unity Catalog — a single, cloud-agnostic governance layer for every table, file, model, and notebook, replacing fragmented per-system access controls.
- Workflows (Lakeflow Jobs) — a built-in orchestrator for scheduling and monitoring multi-step data, analytics, and ML pipelines.
- Asset Bundles — an infrastructure-as-code way to define jobs, pipelines, and ML resources as source files, enabling proper CI/CD for Databricks projects.
This module walks through each of these building blocks — from defining infrastructure as code to governing data with Unity Catalog and orchestrating production pipelines — using real-world enterprise scenarios throughout.
What You'll Master
Infrastructure as Code
Defining jobs, pipelines & ML resources as Databricks Asset Bundles for repeatable, version-controlled deployments.
CI/CD & Deployment
Modern continuous integration & delivery practices for shipping data and AI projects reliably.
Orchestration with Workflows
Building, running & monitoring reliable multi-step pipelines using Lakeflow Jobs (Databricks Workflows).
Unity Catalog Governance
A single, cloud-agnostic control plane for managing access to tables, files, models, and more.
Learning Path & Course Syllabus
Infrastructure-as-code for defining jobs, pipelines, and ML resources as source files alongside your project code.
Modern software engineering practices — CI/CD, version control, and IaC — for reliable Databricks deployments.
The fully managed orchestration service built into the Lakehouse Platform for running & monitoring pipelines.
The industry-first unified governance solution for every data and AI asset in the lakehouse, on any cloud.
Technical questions and detailed answers on Databricks architecture, administration, and development best practices.
Practical production scenarios: the business problem, design considerations, architecture, and implementation.
An enterprise event-driven pipeline combining Azure Blob Storage, Event Hubs, Data Factory, and Databricks.
Scaling incremental file ingestion from Azure ADLS Gen2 with Databricks Auto Loader (cloudFiles).
What's Included in This Module
| Component | Coverage Details |
|---|---|
| Core Topics | Asset Bundles (IaC), CI/CD Deployment, Workflows Orchestration, Unity Catalog Governance |
| Practical Exercises | 2 End-to-End Production Pipeline Case Studies (Event-Driven Ingestion, Incremental Auto Loader) |
| Assessments | Databricks Q&A + Real-World Scenario Walkthroughs |