Databricks Learning Roadmap

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Here’s a step-by-step roadmap to master Databricks and become an expert, covering both fundamentals and advanced concepts.

Databricks Learning Roadmap (2025 Edition)

1. Fundamentals & Core Concepts

  • What is Databricks? (Overview, Use Cases, Cloud Providers)
  • Databricks Workspace & UI (Notebooks, Repos, Jobs)
  • Databricks Clusters (Types, Autoscaling, Configuration)
  • Databricks File System (DBFS)
  • Data Lake vs Data Warehouse Concepts

2. Data Engineering with Databricks

  • Working with DataFrames (Spark SQL, PySpark, Scala, SQL, R)
  • Reading/Writing Data (CSV, Parquet, Delta, JSON, Avro, JDBC)
  • Data Ingestion & Connectivity (Connecting to Cloud Storage, Databases, APIs)
  • Data Cleaning & Transformation (ETL with Spark)
  • Delta Lake (ACID Transactions, Time Travel, Schema Enforcement)
  • Partitioning & Performance Optimization
  • Orchestrating ETL Pipelines (Databricks Workflows, Jobs, Task Dependencies)
  • Managing Metadata (Unity Catalog, Hive Metastore)

3. Data Science & Machine Learning

  • Exploratory Data Analysis (EDA) in Notebooks
  • Feature Engineering with Spark MLlib
  • Model Training (MLlib, MLflow Integration, AutoML)
  • Hyperparameter Tuning & Experiment Tracking
  • Model Deployment (Batch & Real-time Inference)
  • Model Management (MLflow Registry)
  • Collaborative Development (Version Control, Repos, Branches)

4. Advanced Analytics & SQL

  • Advanced Spark SQL (Joins, Windows, Aggregations)
  • Building Data Models (Star/Snowflake Schema)
  • Analytical Functions & BI Dashboards
  • Databricks SQL (Lakehouse, Serverless SQL, Query History)
  • Visualizations (Databricks Visuals, Integrating with Power BI/Tableau)

5. Streaming & Real-Time Analytics

  • Structured Streaming in Databricks (Batch vs Streaming)
  • Real-time ETL and Processing Pipelines (Kafka, Kinesis, Event Hubs)
  • Windowed Aggregations, Watermarks, Late Data Handling
  • Streaming to Data Lake/Dashboard

6. Administration, Security & Governance

  • Cluster & Job Administration (Monitoring, Logging, Debugging)
  • Access Controls (RBAC, Unity Catalog, Table ACLs)
  • Data Lineage, Auditing, and Compliance
  • Secrets Management (Key Vault, Secret Scopes)
  • Cost Management & Optimization (Cluster Sizing, Spot Instances)

7. Automation, CI/CD, & DevOps

  • Automating Workflows (Jobs API, Databricks CLI)
  • CI/CD for Notebooks & Workflows (Repos, GitHub Actions, Azure DevOps, Jenkins)
  • Infrastructure as Code (Databricks Terraform Provider)
  • Monitoring & Alerting

8. Integration & Interoperability

  • Integrating with BI Tools (Power BI, Tableau, Looker)
  • Connecting External ML Frameworks (TensorFlow, scikit-learn, XGBoost)
  • REST API Usage (Jobs, Clusters, Workspace Management)
  • Data Sharing & Collaboration (Delta Sharing, External Tables)

9. Specialization Areas (Optional/Advanced)

  • Lakehouse Architecture Deep Dive
  • Data Governance at Scale (Data Mesh, Multi-cloud)
  • GenAI/LLM on Databricks (Databricks Mosaic, AI Functions)
  • Performance Tuning & Troubleshooting at Scale
  • Migrating Legacy Workloads (from Hadoop, Data Warehouses)
  • Industry Solutions (Healthcare, Finance, IoT, etc.)

Learning Tips:

  • Follow the official Databricks Academy: Free and paid courses.
  • Hands-on practice: Use the Community Edition or trial cloud accounts.
  • Read the docs: Databricks documentation is excellent and up-to-date.
  • Build projects: End-to-end data pipelines, ML models, or dashboards.
  • Certifications: Consider Databricks’ Data Engineer, Data Analyst, or ML Associate/Professional certs.

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