Data Engineering

Build reliable data pipelines, warehouses and ETL workflows in the cloud.

1 month (4 weeks)
20 topics
Final exam

Book a trainer

Your learning path

4 weeks from start to certificate

1
Week 1
Data engineering basics
2
Week 2
ETL pipelines
3
Week 3
Cloud data platforms
4
Week 4
Capstone pipeline
Finish
Exam & certificate

Every week

How each week works

Learn

Live class with your trainer

Practice

Guided hands-on exercises

Build

Mini project for your portfolio

Review

Feedback and Q&A

Concept lab

See how the core concepts connect

Follow the path from foundations to real workplace application. Every concept feeds the next project.

Week 1

Introduction to Data Engineering

Week 1

Data Storage Fundamentals

Week 2

Introduction to ETL/ELT

Week 2

Data Extraction Techniques

Week 3

Introduction to Cloud Data Ecosystems

Week 3

Cloud Data Storage (AWS Focus)

Week 4

Pipeline Design and Architecture

Week 4

Implementing the Data Ingestion Layer

Topics per week

20 topics · 80 lessons

Week 15

Week 25

Week 35

Week 45

Course curriculum

Week 1 · 5 topics · 20 lessons

Data engineering basics

By the end of this week: Learners can explain fundamental data engineering concepts, differentiate data architectures, and apply basic data modeling principles.

1.1Introduction to Data Engineering
- Role of a Data Engineer
- Data Lifecycle Stages
- Data Engineering vs. Data Science
- Common Data Engineering Stacks
1.2Data Storage Fundamentals
- File Formats: CSV, JSON, Parquet
- Relational Databases: PostgreSQL
- NoSQL Databases: MongoDB
- Object Storage: Amazon S3
1.3Data Modeling Principles
- Conceptual, Logical, Physical Models
- Star Schema vs. Snowflake Schema
- Data Normalization (1NF, 2NF, 3NF)
- Dimension and Fact Tables
1.4SQL for Data Engineers
- Advanced SQL Queries: Joins, Window Functions
- DDL: CREATE, ALTER, DROP
- DML: INSERT, UPDATE, DELETE
- Database Indexing Strategies
1.5Version Control for Data Assets
- Introduction to Git and GitHub
- Branching and Merging Workflows
- Version Control for SQL Scripts
- Managing Data Schema Changes

Week 1
Data engineering basics

Introduction to Data Engineering

Data Storage Fundamentals

Data Modeling Principles

SQL for Data Engineers

Version Control for Data Assets

Hands-on project

SQL Data Modeling for an E-commerce Database

Week 2 · 5 topics · 20 lessons

ETL pipelines

By the end of this week: Learners can design, build, and orchestrate basic ETL/ELT pipelines using various data processing tools and scheduling platforms.

2.1Introduction to ETL/ELT
- ETL vs. ELT Paradigms
- Batch vs. Stream Processing
- Data Ingestion Strategies
- Common ETL Challenges
2.2Data Extraction Techniques
- API Data Extraction (RESTful APIs)
- Database Change Data Capture (CDC)
- Web Scraping with Beautiful Soup
- File System Monitoring
2.3Data Transformation with Python
- Pandas for Data Manipulation
- Data Cleaning and Validation
- Aggregation and Reshaping Data
- Handling Missing Values
2.4Data Loading Strategies
- Idempotent Loads
- SCD Type 1 and Type 2
- Bulk Loading into Data Warehouses
- Upserts and Merges
2.5Orchestration with Apache Airflow
- Airflow Concepts: DAGs, Operators, Tasks
- Writing Airflow DAGs (Python)
- Scheduling and Monitoring Workflows
- Airflow Task Dependencies

Week 2
ETL pipelines

Introduction to ETL/ELT

Data Extraction Techniques

Data Transformation with Python

Data Loading Strategies

Orchestration with Apache Airflow

Hands-on project

Building an ELT Pipeline with Python and Airflow

Week 3 · 5 topics · 20 lessons

Cloud data platforms

By the end of this week: Learners can leverage cloud-based services for scalable data storage, processing, and warehousing, understanding cost implications.

3.1Introduction to Cloud Data Ecosystems
- Benefits of Cloud for Data
- AWS, Azure, GCP Overview
- Serverless Data Services
- Cloud Cost Management
3.2Cloud Data Storage (AWS Focus)
- Amazon S3: Object Storage
- S3 Data Lake Design
- AWS Glue Data Catalog
- IAM for S3 Access Control
3.3Cloud Data Warehousing (AWS Redshift)
- Redshift Architecture and Concepts
- Loading Data into Redshift
- Redshift Spectrum for S3 Data
- Performance Tuning in Redshift
3.4Cloud Data Processing (AWS Glue/EMR)
- AWS Glue: ETL Service
- Apache Spark on AWS EMR
- Serverless Queries with Amazon Athena
- Data Streaming with Kinesis
3.5Data Governance and Security in Cloud
- Data Encryption at Rest and Transit
- Identity and Access Management (IAM)
- Compliance Standards (HIPAA, GDPR)
- Data Lineage and Metadata Management

Week 3
Cloud data platforms

Introduction to Cloud Data Ecosystems

Cloud Data Storage (AWS Focus)

Cloud Data Warehousing (AWS Redshift)

Cloud Data Processing (AWS Glue/EMR)

Data Governance and Security in Cloud

Hands-on project

Cloud Data Lake and Warehouse Setup on AWS

Week 4 · 5 topics · 20 lessons

Capstone pipeline

By the end of this week: Learners can design, implement, and deploy a complete end-to-end data pipeline solution, including monitoring and testing.

4.1Pipeline Design and Architecture
- Requirements Gathering and Analysis
- Designing Scalable Architectures
- Data Governance Integration
- Choosing the Right Tools
4.2Implementing the Data Ingestion Layer
- Automated Data Source Connections
- Handling Incremental Data Loads
- Error Handling during Extraction
- Data Validation at Ingestion
4.3Building the Transformation Logic
- Complex Data Cleaning Rules
- Advanced Data Aggregations
- Data Quality Checks (Great Expectations)
- Using dbt for Transformations
4.4Deployment, Monitoring, and Alerting
- Containerization with Docker
- CI/CD for Data Pipelines
- Logging and Monitoring (CloudWatch)
- Alerting for Pipeline Failures
4.5Data Pipeline Testing and Maintenance
- Unit and Integration Testing
- Data Reconciliation
- Performance Optimization
- Documentation and Runbooks

Week 4
Capstone pipeline

Pipeline Design and Architecture

Implementing the Data Ingestion Layer

Building the Transformation Logic

Deployment, Monitoring, and Alerting

Data Pipeline Testing and Maintenance

Hands-on project

End-to-End Data Pipeline for Sales Analytics

Final exam & certificate

Finish the 4 weeks, then take the online exam. Score 70% or more to pass.

Job-ready syllabus

Learn it. Build it. Explain it. Use it.

The syllabus goes beyond watching lessons. You practice the tasks employers expect, produce evidence of your skills, and prepare to discuss your work clearly.

Workplace skills

Apply Introduction to Data Engineering, Data Storage Fundamentals, Data Modeling Principles, SQL for Data Engineers in guided business scenarios.

Portfolio proof

Complete 4 practical projects, including SQL Data Modeling for an E-commerce Database.

Professional practice

Present your work, respond to feedback, document decisions, and improve the final result.

Interview readiness

Review common role questions and explain your process, tools, trade-offs, and project outcomes.

Your completion pack

Weekly projects, trainer feedback, final assessment, and a certificate you can add to your résumé and LinkedIn profile.

Why learn from us?

Six reasons students pick M - IT Solutions

One-month programs built around live teaching, real projects and support that actually shows up.

Live online classes

Learn with a real instructor in real time — not pre-recorded videos you watch alone.

Hands-on projects

Every week ends with practical work you can show employers on day one.

1-on-1 trainer time

Stuck or want to go deeper? Book private sessions with a trainer whenever you need.

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Lessons, progress tracking and exams all live in your account, on any device.

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