Data Engineering & Analytics Master Program
Pipelines, warehousing, and analytics engineering fundamentals.
A complete, hands-on curriculum that takes you from SQL and Python fundamentals through data modeling, cloud data warehousing, ETL/ELT pipelines, orchestration with Airflow, analytics engineering with dbt, big data processing with Spark, streaming pipelines, and business intelligence — finishing with production-grade data platform capstone projects.
Phase 1: Data Foundations
Module 1: Data Engineering Fundamentals
- Role of a data engineer
- Data lifecycle & architecture patterns
- OLTP vs. OLAP
- Data lakes vs. data warehouses
- Batch vs. streaming data
Hands-on labs
- Map a sample company's data architecture
Module 2: SQL for Data Engineering
- Advanced SQL queries & joins
- Window functions & CTEs
- Query optimization & indexing
- Data modeling basics (star & snowflake schema)
Hands-on labs
- Write advanced analytical SQL queries on a sample dataset
Module 3: Python for Data Engineering
- Python fundamentals for data work
- Pandas & NumPy basics
- Working with APIs & files (CSV, JSON, Parquet)
- Data cleaning & transformation
Hands-on labs
- Build a Python ETL script to clean and transform raw data
Phase 2: Data Modeling & Warehousing
Module 4: Data Modeling
- Dimensional modeling concepts
- Fact & dimension tables
- Slowly changing dimensions
- Normalization vs. denormalization
Hands-on labs
- Design a star schema for a sales analytics use case
Module 5: Cloud Data Warehousing
- Amazon Redshift architecture
- Snowflake fundamentals
- Data warehouse performance tuning
- Partitioning & clustering strategies
Hands-on labs
- Load and query a dataset in Amazon Redshift
Phase 3: Data Pipelines & ETL/ELT
Module 6: Building ETL/ELT Pipelines
- ETL vs. ELT patterns
- Extracting data from APIs & databases
- Data transformation strategies
- Loading strategies & incremental loads
Hands-on labs
- Build an end-to-end ETL pipeline with Python
Module 7: Orchestration with Apache Airflow
- Airflow architecture (DAGs, operators, scheduler)
- Building and scheduling DAGs
- Dependency management
- Monitoring & alerting pipelines
Hands-on labs
- Orchestrate a multi-step data pipeline with Airflow
Module 8: Analytics Engineering with dbt
- Introduction to dbt & the transformation layer
- Models, tests, and documentation
- Version-controlled analytics workflows
- Data quality testing
Hands-on labs
- Build and test a dbt transformation project
Phase 4: Big Data & Streaming
Module 9: Big Data Processing with Spark
- Apache Spark architecture
- PySpark DataFrames & transformations
- Distributed data processing concepts
- Performance tuning basics
Hands-on labs
- Process a large dataset with PySpark
Module 10: Streaming Data Pipelines
- Streaming vs. batch processing
- Apache Kafka fundamentals
- AWS Kinesis basics
- Real-time data ingestion patterns
Hands-on labs
- Build a simple real-time data ingestion pipeline
Phase 5: Cloud Data Platforms & Governance
Module 11: AWS Data Services
- Amazon S3 as a data lake
- AWS Glue for ETL & cataloging
- Amazon Athena for serverless querying
- AWS Lake Formation basics
Hands-on labs
- Build a serverless data lake pipeline with S3, Glue, and Athena
Module 12: Data Quality & Governance
- Data quality frameworks
- Data cataloging & metadata management
- Data lineage tracking
- Security & access control for data platforms
Hands-on labs
- Implement data quality checks in a pipeline
Module 13: Data Pipeline Monitoring & Reliability
- Pipeline observability
- Alerting & failure handling
- Cost optimization for data platforms
- CI/CD for data pipelines
Hands-on labs
- Add monitoring and alerting to an existing pipeline
Phase 6: Analytics, BI & Capstones
Module 14: Business Intelligence & Visualization
- BI tool fundamentals (Power BI/Tableau)
- Building interactive dashboards
- Data storytelling principles
- Connecting BI tools to warehouses
Hands-on labs
- Build an interactive sales dashboard in Power BI/Tableau
Module 15: Analytics for Decision-Making
- KPI design & metrics frameworks
- A/B testing fundamentals
- Descriptive vs. predictive analytics basics
- Communicating insights to stakeholders
Module 16: Capstone Projects
- Apply your knowledge by building comprehensive, production-grade data platforms
Capstone Projects
Apply everything you've learned by building comprehensive, production-grade systems.
Tools & Technologies
Languages
Python, SQL
Data Warehousing
Amazon Redshift, Snowflake
Pipeline & Orchestration
Apache Airflow, dbt
Big Data Processing
Apache Spark, PySpark
Streaming
Apache Kafka, AWS Kinesis
Visualization & BI
Power BI, Tableau, Looker Studio
Cloud & Storage
AWS S3, AWS Glue, AWS Athena