A comprehensive, hands-on curriculum covering distributed data systems, ML lifecycle management, and production-grade data & ML engineering
This roadmap follows a project-based, incremental complexity approach aligned with real-world ML & Data Engineering responsibilities
Every task includes practical implementation with real datasets
Focus on scalability, reliability, monitoring, and best practices
Build complete pipelines from data ingestion to model deployment
Tasks mirror actual responsibilities in the role
Master every aspect of ML and Data Engineering on Azure Databricks through hands-on projects
Core distributed computing concepts and Databricks workspace fundamentals
Set up Azure Databricks workspace, understand cluster configuration, and explore the Databricks UI. Create your first notebook and run basic PySpark commands.
Learn PySpark DataFrame API fundamentals. Work with a real dataset (e.g., NYC Taxi trips ~5GB) to understand lazy evaluation, transformations, actions, and partitioning.
Deep dive into complex transformations, window functions, joins, and Spark SQL. Build a data analysis pipeline combining DataFrame API and SQL.
ETL/ELT pipeline development, data modeling, and storage optimization
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).
Build a production-grade data lakehouse using medallion architecture. Implement Bronze (raw), Silver (cleaned), and Gold (aggregated) layers with incremental processing.
Master Delta Lake capabilities: ACID transactions, time travel, merge operations, Z-ordering, and vacuum. Implement SCD Type 2 (Slowly Changing Dimensions).
Automate data pipelines using Databricks Jobs. Create multi-task workflows with dependencies, error handling, and notifications.
Feature engineering, model training, and ML lifecycle basics with MLflow
Build scalable feature engineering pipelines using PySpark. Create features from raw data: aggregations, time-based features, encoding, and feature transformations for ML.
Create a distributed ML training pipeline using Spark MLlib. Train regression/classification models at scale with cross-validation and hyperparameter tuning.
Implement MLflow for experiment tracking, parameter logging, and metric comparison. Track multiple training runs and compare model performance systematically.
Master MLflow Model Registry for centralized model management. Implement model versioning, stage transitions (Staging → Production), and model lineage tracking.
Model deployment, monitoring, retraining automation, and serving infrastructure
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.
Implement comprehensive model monitoring: data drift detection, model performance tracking, and alerting. Build dashboards to visualize model health over time.
Build end-to-end automated retraining workflow triggered by drift or scheduled intervals. Include data validation, feature engineering, training, evaluation, and conditional deployment.
Implement Databricks Feature Store for centralized feature management. Create feature tables, automate feature computation, and enable feature reuse across models.
Real-time data pipelines with Structured Streaming and Kafka integration
Learn Spark Structured Streaming fundamentals. Build a simple streaming pipeline that reads from file source, performs transformations, and writes to Delta Lake.
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.
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.
Performance optimization, data quality frameworks, and governance
Master Spark performance optimization techniques. Profile slow queries, identify bottlenecks, and apply advanced optimization strategies (AQE, broadcast joins, partition tuning).
Build comprehensive data quality framework with automated validation, profiling, and anomaly detection. Integrate with Delta Lake and set up quality dashboards.
Implement data lineage tracking to map data flow from source to consumption. Build observability into pipelines with logging, metrics, and alerting.
Implement data governance using Unity Catalog. Set up access controls, PII detection and masking, data classification, and audit logging.
End-to-end production-grade systems combining all learned skills
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.
Build production recommendation system with real-time user event processing, collaborative filtering model training, online feature serving, and sub-second inference.
Build enterprise forecasting system for demand prediction: multi-level hierarchical forecasting, distributed training across thousands of time series, probabilistic predictions, and forecast reconciliation.
Everything you need to succeed in your learning journey
Join thousands of engineers mastering ML and Data Engineering on Azure Databricks