Azure Databricks ML & Data Engineering Learning Roadmap
A comprehensive, hands-on curriculum covering distributed data systems, ML lifecycle management, and production-grade data & ML engineering
Project-Based Learning Approach
This roadmap follows a project-based, incremental complexity approach aligned with real-world ML & Data Engineering responsibilities
Hands-on First
Every task includes practical implementation with real datasets
Production Mindset
Focus on scalability, reliability, monitoring, and best practices
End-to-End Ownership
Build complete pipelines from data ingestion to model deployment
Job-Ready Skills
Tasks mirror actual responsibilities in the role
7 Progressive Learning Phases
Master every aspect of ML and Data Engineering on Azure Databricks through hands-on projects
Databricks & Spark Foundations
Core distributed computing concepts and Databricks workspace fundamentals
Task 1.1: Environment Setup & Exploration
Set up Azure Databricks workspace, understand cluster configuration, and explore the Databricks UI. Create your first notebook and run basic PySpark commands.
Technologies:
✓ Deliverables:
- Configured Databricks workspace with running cluster
- Notebook demonstrating SparkContext, SparkSession usage
- Documentation of cluster configuration rationale
Task 1.2: DataFrame Basics & Distributed Processing
Learn PySpark DataFrame API fundamentals. Work with a real dataset (e.g., NYC Taxi trips ~5GB) to understand lazy evaluation, transformations, actions, and partitioning.
💡 Pro Tips:
- • Use .cache() strategically on frequently accessed DataFrames
- • Monitor Spark UI to understand job execution and identify bottlenecks
- • Start with smaller data samples for testing before scaling
Technologies:
Task 1.3: Advanced DataFrame Operations & SQL
Deep dive into complex transformations, window functions, joins, and Spark SQL. Build a data analysis pipeline combining DataFrame API and SQL.
Technologies:
Data Engineering Fundamentals
ETL/ELT pipeline development, data modeling, and storage optimization
Task 2.1: Build Your First ETL Pipeline
Design and implement a complete ETL pipeline: Extract data from multiple sources (CSV, JSON, APIs), Transform with business logic, Load into optimized storage format (Delta Lake).
💡 Pro Tips:
- • Use Delta Lake's ACID properties to ensure data consistency
- • Partition by commonly filtered columns (date, region, etc.)
- • Implement idempotency to allow safe pipeline reruns
Technologies:
Task 2.2: Implement Medallion Architecture (Bronze-Silver-Gold)
Build a production-grade data lakehouse using medallion architecture. Implement Bronze (raw), Silver (cleaned), and Gold (aggregated) layers with incremental processing.
Technologies:
Task 2.3: Advanced Delta Lake Features
Master Delta Lake capabilities: ACID transactions, time travel, merge operations, Z-ordering, and vacuum. Implement SCD Type 2 (Slowly Changing Dimensions).
Task 2.4: Workflow Orchestration with Databricks Jobs
Automate data pipelines using Databricks Jobs. Create multi-task workflows with dependencies, error handling, and notifications.
ML Engineering Foundations
Feature engineering, model training, and ML lifecycle basics with MLflow
Task 3.1: Feature Engineering at Scale
Build scalable feature engineering pipelines using PySpark. Create features from raw data: aggregations, time-based features, encoding, and feature transformations for ML.
💡 Pro Tips:
- • Use window functions for time-based aggregations instead of joins
- • Persist intermediate feature sets to avoid recomputation
- • Track feature creation logic for reproducibility
Task 3.2: Build ML Pipeline with Spark MLlib
Create a distributed ML training pipeline using Spark MLlib. Train regression/classification models at scale with cross-validation and hyperparameter tuning.
Task 3.3: MLflow Tracking & Experimentation
Implement MLflow for experiment tracking, parameter logging, and metric comparison. Track multiple training runs and compare model performance systematically.
Task 3.4: MLflow Model Registry & Versioning
Master MLflow Model Registry for centralized model management. Implement model versioning, stage transitions (Staging → Production), and model lineage tracking.
Production ML Systems
Model deployment, monitoring, retraining automation, and serving infrastructure
Task 4.1: Batch Inference Pipeline
Build a production batch inference system using Spark for distributed scoring. Load models from MLflow Registry, apply to large datasets, and store predictions in Delta Lake.
💡 Pro Tips:
- • Use pandas_udf for vectorized predictions (much faster than row-level UDF)
- • Broadcast small lookup tables to avoid shuffles
- • Partition predictions table by date for efficient querying
Task 4.2: Model Monitoring & Drift Detection
Implement comprehensive model monitoring: data drift detection, model performance tracking, and alerting. Build dashboards to visualize model health over time.
Task 4.3: Automated Retraining Pipeline
Build end-to-end automated retraining workflow triggered by drift or scheduled intervals. Include data validation, feature engineering, training, evaluation, and conditional deployment.
Task 4.4: Feature Store Implementation
Implement Databricks Feature Store for centralized feature management. Create feature tables, automate feature computation, and enable feature reuse across models.
Streaming Data Processing
Real-time data pipelines with Structured Streaming and Kafka integration
Task 5.1: Structured Streaming Basics
Learn Spark Structured Streaming fundamentals. Build a simple streaming pipeline that reads from file source, performs transformations, and writes to Delta Lake.
Task 5.2: Kafka Integration & Real-Time ETL
Integrate with Apache Kafka for real-time data ingestion. Build end-to-end streaming ETL: consume from Kafka, transform, enrich, and load into Delta Lake with exactly-once semantics.
💡 Pro Tips:
- • Use foreachBatch for complex custom logic in streaming
- • Configure checkpointing for fault tolerance
- • Monitor lag to ensure stream keeps up with source
Task 5.3: Real-Time Feature Engineering & ML Scoring
Build real-time ML inference pipeline on streaming data. Compute features on-the-fly, apply ML models from MLflow, and store predictions in near real-time.
Advanced Data Engineering
Performance optimization, data quality frameworks, and governance
Task 6.1: Performance Tuning & Optimization
Master Spark performance optimization techniques. Profile slow queries, identify bottlenecks, and apply advanced optimization strategies (AQE, broadcast joins, partition tuning).
Task 6.2: Data Quality Framework Implementation
Build comprehensive data quality framework with automated validation, profiling, and anomaly detection. Integrate with Delta Lake and set up quality dashboards.
Task 6.3: Data Lineage & Observability
Implement data lineage tracking to map data flow from source to consumption. Build observability into pipelines with logging, metrics, and alerting.
Task 6.4: Data Governance with Unity Catalog
Implement data governance using Unity Catalog. Set up access controls, PII detection and masking, data classification, and audit logging.
Capstone Projects
End-to-end production-grade systems combining all learned skills
Capstone 1: Customer Churn Prediction Platform
Build complete customer churn prediction system: batch and streaming data ingestion, medallion architecture, feature engineering at scale, ML training with MLflow, automated retraining, batch and real-time inference, monitoring, and dashboards.
System Components:
- • Multi-source data ingestion (batch: S3/ADLS, streaming: Kafka)
- • Bronze-Silver-Gold lakehouse with data quality checks
- • Feature Store with 100+ engineered features
- • ML training pipeline with hyperparameter tuning
- • Batch and real-time inference pipelines
- • Model monitoring with drift detection
Capstone 2: Real-Time Recommendation Engine
Build production recommendation system with real-time user event processing, collaborative filtering model training, online feature serving, and sub-second inference.
Capstone 3: Time-Series Forecasting Platform
Build enterprise forecasting system for demand prediction: multi-level hierarchical forecasting, distributed training across thousands of time series, probabilistic predictions, and forecast reconciliation.
Additional Resources
Everything you need to succeed in your learning journey
Official Documentation
Sample Datasets
- NYC Taxi Trips (Batch processing practice)
- Criteo Click Logs (ML training)
- Kaggle Competitions (Real-world scenarios)
- Azure Open Datasets (Production-like data)
Community & Learning
- Databricks Academy (Free courses)
- Databricks Community Edition
- Stack Overflow (Q&A)
- GitHub (Sample projects and code)
Certification Path
- Data Engineer Associate
- Data Engineer Professional
- Machine Learning Associate
- Machine Learning Professional
Ready to Start Your Journey?
Join thousands of engineers mastering ML and Data Engineering on Azure Databricks