Data Engineering

Master the full data engineering lifecycle—ingest, store, process, orchestrate, and monitor data—so you can build robust, scalable data pipelines that power analytics and ML.

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Data engineering

Soft skills

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Why Data Engineering Matters

In today’s data-driven world, businesses need reliable pipelines to move, clean, transform, and serve data. Good data engineering ensures that insights are timely, trusted, and cost-effective. With data infrastructure being central to modern applications, demand for engineers who can build and maintain it is growing rapidly.

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Flexible learning: fully online and instructor-led offline sessions

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Personalized learning paths based on AI-driven skill diagnostics

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Hands-on labs and real-world capstone projects

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Dedicated mentorship and expert code reviews

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100% placement support including interview prep, role matching, and career guidance

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PROGRAM OVERVIEW

Over ~ 7 months (or suitable period), learners move through core modules: Foundations, Ingestion & Storage, Processing & Transformation, Workflow Orchestration & MLOps, Monitoring & Deployment. The program combines live Instructor-led sessions, labs, real-world projects, and mentorship.

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Data Engineering Training Program | Full-Width Curriculum

The Complete Data Engineering Training Program

Phase 1: Foundations

🎯 Goal: Build the essential base—programming, data fundamentals, and fundamentals of systems.

Module 1: Programming & Data Structures

Python core (data types, loops, functions, modules)
Advanced data structures (lists, dicts, sets, trees/maps)
Basic scripting & version control (Git)

Module 2: Database Fundamentals & Data Modelling

Relational databases: SQL querying, normalization, indexing
NoSQL primers (key-value, document, columnar stores)
Schema design for analytics vs transactional use-cases

Phase 2: Ingestion & Storage

🎯 Goal: Learn how to bring data in and store it efficiently.

Module 3: Data Ingestion Architectures

Batch ingestion (Airflow, cron jobs)
Streaming ingestion (Kafka / Pulsar basics)
Connectors / ETL/ELT tools (Airbyte, Fivetran)

Module 4: Storage Systems & Warehousing

Data lakes vs warehouses vs lakehouses
Tools/platforms: S3, Azure Blob, GCS; Snowflake, BigQuery, Redshift, Databricks
Partitioning, formats (Parquet, ORC, Avro)

Phase 3: Processing & Transformation

🎯 Goal: Transform raw data into analytics-ready and ML-ready data.

Module 5: Batch Data Processing

Using Spark or PySpark
Transformations, aggregations, joins, enrichments

Module 6: Streaming & Real-time Processing

Stream processing (stateless vs stateful)
Frameworks: Spark Streaming, Flink, Kafka Streams

Module 7: Data Transformation Tools & Best Practices

dbt for modular, testable transformations
Versioning, testing, docs

Phase 4: Workflow Orchestration & Deployment

🎯 Goal: Make pipelines reliable, repeatable, and production-ready.

Module 8: Orchestration & Scheduling

Airflow, Prefect, Dagster (DAG design, triggers, retries, monitoring)

Module 9: Containerization & Infrastructure as Code

Docker, Kubernetes basics
Terraform, CloudFormation

Module 10: MLOps & Deployment of Data Pipelines

Deploying pipelines; optimizing for scale
CI/CD for data workflows

Phase 5: Monitoring, Security & Capstone

🎯 Goal: Ensure quality, reliability, security, and wrap it up with a project.

Module 11: Data Quality & Observability

Great Expectations or similar tools
Monitoring pipelines (latency, failures, usage)

Module 12: Security, Governance, & Compliance

Data privacy, encryption, access control
Data lineage, auditability
GDPR & regulations

Module 13: Capstone Project (6–8 weeks)

End-to-end solution: ingestion → storage → transformations → delivery
Include monitoring, security, documentation
Domain options: Finance, Healthcare, Retail, IoT

Module 14: Interview & Career Prep

Common DE interview Qs (SQL, system design, data modeling)
Resume & GitHub portfolio
Mock interviews (technical & behavioral)

Ashutosh Dwivedi

PhD, IIT Kanpur • AI & Cybersecurity

Expert in Artificial Intelligence, Machine Learning, Computer Vision, Data Analytics and Embedded Systems. Co-author of “Digital Communication using MATLAB.”