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
Frequently Asked Questions
Who is the Data Engineering & Analytics program for?
It's designed for people targeting roles like Data Engineer, Analytics Engineer, BI Developer. The curriculum is structured "Beginner to Advanced," so it works whether you're starting out or already have some hands-on background.
Do I need prior experience to join?
No advanced experience is required to start. Helpful (not mandatory) prerequisites: Basic computer knowledge; Basic SQL familiarity (helpful but not mandatory); Comfort with logical/analytical thinking.
How is the program delivered, and how long does it take?
It's delivered as live + hands-on labs, cohort-based, spanning 16 modules across 6 phases. You'll work through hands-on labs alongside the curriculum, not just video lectures.
What certifications does this program prepare me for?
The curriculum is built around: Data Engineering Certificate (Internal) — Primary Focus; AWS Certified Data Analytics – Specialty (Optional); Google Professional Data Engineer (Optional).
How much does the Data Engineering & Analytics program cost, and how do I enroll?
Pricing depends on the current batch and any active offers. Send an enquiry or message us on WhatsApp and we'll share the latest fee, upcoming batch dates, and enrollment steps.