Databricks — Complete Learning Path
Welcome to the Databricks learning path on Full Stack Learners. This course is designed to take you from your first understanding of the Databricks Lakehouse Platform to building, optimizing, governing, and operating production-grade data and AI solutions.
You do not need prior Databricks experience. The course introduces the platform progressively, explains the underlying Apache Spark and Delta Lake concepts, and then moves into enterprise engineering and architecture.
What you will learn
- Understand the Databricks Lakehouse Platform and its architecture.
- Develop data solutions using Apache Spark, PySpark, and Databricks SQL.
- Build reliable batch and streaming pipelines with Delta Lake and Lakeflow.
- Govern data and control access with Unity Catalog.
- Integrate Databricks with AWS, Azure, GCP, SAP, and common data platforms.
- Optimize workloads for performance, reliability, and cost.
- Deliver production projects using CI/CD, infrastructure as code, and operational practices.
- Understand the machine-learning and generative-AI capabilities in the Databricks ecosystem.
Learning roadmap
The roadmap is organized into 27 modules. Follow the sequence for a complete learning journey, or jump to a topic when you already have the prerequisites.
| # | Module | Focus |
|---|---|---|
| 00 | Learning Path & Prerequisites | Tools, prerequisites, and how to use the course |
| 01 | Databricks Foundations | Platform concepts, workspace, compute, and terminology |
| 02 | Platform Architecture | Control plane, data plane, storage, compute, and security |
| 03 | Apache Spark Fundamentals | Execution model, jobs, stages, tasks, partitions, and shuffle |
| 04 | PySpark | DataFrames, transformations, joins, windows, and advanced APIs |
| 05 | SQL in Databricks | SQL warehouses, queries, views, and query optimization |
| 06 | Delta Lake | Transaction log, ACID, schema evolution, MERGE, and maintenance |
| 07 | Unity Catalog | Metastore, catalogs, schemas, objects, permissions, and lineage |
| 08 | Data Engineering | Batch, incremental processing, CDC, SCD, and medallion architecture |
| 09 | Auto Loader | Incremental file ingestion and schema management |
| 10 | Structured Streaming | Streaming semantics, checkpoints, watermarks, and Kafka |
| 11 | Lakeflow | Declarative pipelines, Connect, Jobs, and orchestration |
| 12 | Databricks Workflows / Jobs | Tasks, dependencies, schedules, retries, and monitoring |
| 13 | Compute | Compute choices, serverless, Photon, policies, and cost |
| 14 | Performance Engineering | Spark tuning, query plans, data layout, and production optimization |
| 15 | Data Governance & Security | Identity, access, audit, masking, filters, and compliance |
| 16 | Databricks + Cloud | AWS, Azure, GCP, storage, networking, and cloud patterns |
| 17 | Databricks + AWS | S3, IAM, Glue, Redshift, Lambda, EventBridge, and Kinesis |
| 18 | Data Integration | JDBC, APIs, Kafka, SAP, Salesforce, and integration platforms |
| 19 | dbt + Databricks | Models, tests, incremental models, snapshots, and deployment |
| 20 | ML / AI on Databricks | MLflow, model serving, Vector Search, RAG, and Mosaic AI |
| 21 | MLOps | Model lifecycle, deployment, CI/CD, and monitoring |
| 22 | DevOps / CI-CD | CLI, REST APIs, Asset Bundles, Git, and Terraform |
| 23 | Advanced Architecture | Enterprise lakehouse, data mesh, resilience, and governance |
| 24 | Real-World Projects | End-to-end batch, streaming, CDC, SAP, BI, and GenAI projects |
| 25 | Interview Preparation | Fundamentals, coding, troubleshooting, and architecture scenarios |
| 26 | Certification | Certification pathways and practice |
Recommended learning sequence
Foundations
↓
Spark + PySpark + SQL
↓
Delta Lake + Unity Catalog
↓
Data Engineering + Auto Loader + Streaming
↓
Lakeflow + Jobs + Compute
↓
Performance + Governance + Cloud
↓
Integration + dbt + DevOps
↓
AI / ML + MLOps
↓
Enterprise Architecture + Projects
↓
Interview + Certification
Who is this course for?
- Beginners entering data engineering and analytics engineering.
- SQL developers who want to learn distributed data processing.
- Data engineers moving from traditional ETL tools to a Lakehouse platform.
- Cloud engineers working with AWS, Azure, or Google Cloud.
- BI and data-warehouse professionals modernizing enterprise platforms.
- Experienced engineers preparing for senior, lead, or architect responsibilities.
Prerequisites
You can start with no Databricks experience. The following foundations will make the course easier:
| Skill | Expected level | Why it helps |
|---|---|---|
| SQL | Basic | Querying and transforming data |
| Python | Basic | PySpark programming |
| Data concepts | Basic | Tables, schemas, keys, and ETL |
| Cloud concepts | Helpful | Storage, identity, and networking |
| Apache Spark | Not required | Covered in this course |
How each tutorial is structured
Where relevant, tutorials will include:
- Concept and business problem.
- Architecture and execution flow.
- Step-by-step explanation.
- SQL or PySpark examples.
- Hands-on exercise with expected output.
- Performance, reliability, and security considerations.
- Common mistakes and troubleshooting.
- Production patterns and interview questions.
Platform and terminology note
Databricks evolves continuously. Product names, interfaces, supported runtimes, and feature availability can differ by cloud and workspace configuration. Each tutorial should identify relevant prerequisites and distinguish general Apache Spark behavior from Databricks-specific features. Verify operational details against the official documentation before applying them in production.
Start learning
Begin with 01. Databricks Foundations to understand what Databricks is, why the Lakehouse architecture matters, and how the platform fits into a modern data ecosystem.
Learn progressively. Build practically. Design for production.