Advanced Iot Bootcamp Advanced Data Engineering Bootcamp Advanced Data Engineering Bootcamp Advanced Data Engineering Bootcamp Advanced Data Engineering Bootcamp

Data Engineering Industry Training Program

Build scalable, production-grade data pipelines used by modern enterprises.

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1.5 Month
Online + Offline
Beginner friendly
No prior experience required
Industry focused
Learn what industry needs
Internship Certificate
Credentials that boost careers

What you'll Build

  • Python-Based ETL Pipelines
    Load, clean, validate, and transform raw datasets into analytics-ready outputs.
  • SQL Analytics & Transformation Layers
    Reporting tables and views built using joins, aggregations, and validation logic.
  • Distributed Spark Data Pipelines
    Optimized PySpark workflows handling larger-than-memory datasets.
  • End-to-End Cloud Data Engineering Capstone
    Python → SQL → Spark → Cloud pipeline with documentation and final presentation.
Week 1: Data Engineering Foundations
  • Data Engineering vs Data Science vs Business Analytics
  • Responsibilities of a Data Engineer and modern data stacks
  • Batch vs streaming data concepts
  • Data lifecycle and architecture layers (ingestion → analytics)
  • OLTP vs OLAP fundamentals
Week 2: SQL for Data Engineering
  • Relational databases, schemas, data types, keys & constraints
  • SQL transformations: joins, aggregations, CASE logic
  • Designing views vs tables
  • Incremental logic concepts and data validation queries
  • Building trustworthy analytics pipelines using SQL
Week 3: Python for ETL & Data Quality
  • Role of Python in data engineering pipelines
  • Working with CSV, JSON, and columnar formats
  • Data cleaning: null handling, duplicates, type casting
  • Business validation rules and quality checks
  • Writing reusable ETL code with logging and error handling
Week 4: Data Modeling & Warehousing
  • Data modeling fundamentals for analytics
  • Fact & dimension tables, grain definition
  • Star and snowflake schemas
  • Slowly Changing Dimensions (SCD) concepts
  • Warehouse loading strategies and BI-ready layers
Week 5: Distributed Data Processing with Spark
  • Why distributed computing is needed
  • Spark architecture: driver, executors, lazy evaluation
  • Spark transformations and actions
  • Performance basics: partitions, caching, Parquet files
Week 6: Cloud Pipelines & Orchestration
  • Cloud data engineering fundamentals
  • Working with cloud data warehouses (BigQuery)
  • Pipeline orchestration concepts (scheduling, dependencies, retries) li>
  • End-to-end pipeline execution, documentation, and presentation

Learning Outcomes

Strong End-to-End Data Engineering Foundation

Clear understanding of how production data pipelines are designed and operated.

High Confidence in SQL & ETL Design

Ability to build maintainable, scalable SQL-based transformation layers.

Practical Python & Spark Skills

Hands-on experience writing ETL pipelines and distributed data workflows.

Cloud & Modern Stack Exposure

Experience working with BigQuery, cloud storage, and orchestration concepts.

Portfolio-Ready Capstone Project

One complete Data Engineering project ready for GitHub, resumes, and interviews.

Career Readiness for Data Engineering Roles

Understanding of DE job roles, industry expectations, and next growth paths.

Certificate

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Prepare for a Career in
Data Engineering

  • Learn industry-demanded Data Engineering with Experts mentors
  • Build real-world projects using SQL and Python
  • Develop strong problem-solving & deployment experience
  • Earn an certificate + portfolio to boost your career