Data Engineering Course — Duration-wise Syllabus

2030 Outlook — Data Engineering

Career Risk (2030): Very Low

Very Low

career risk

Where it's headed

Data Engineering is considered one of the best technology careers for the next decade. As companies generate huge amounts of data, they need engineers to build pipelines, process data, create data lakes and support AI systems.

Demand today

Strong, sustained demand across Python, SQL, Apache Spark, Kafka, AWS, Azure and Databricks — the core stack behind every modern data platform.

Our recommendation

If you want a technology career with a long runway, this is it. Build depth in Python, Spark and one cloud platform first, then broaden.

Python SQL Apache Spark Kafka AWS Azure Databricks Airflow

Duration-wise curriculum

12 Weeks syllabus, module by module.

Click a module to expand it. Every module ends with a hands-on exercise so the tool stack sticks, not just the theory.

Python fundamentals: data structures, functions, OOP basics
File handling, working with JSON/CSV/Parquet
Writing modular, production-style scripts
Virtual environments and dependency management

Advanced SQL: window functions, CTEs, query optimization
Relational schema design and normalization
Indexing and performance tuning basics
Intro to NoSQL data modeling

Spark architecture: driver, executors, DAGs
PySpark DataFrames and transformations
Partitioning, caching and performance tuning
Batch processing pipelines with Spark

Kafka architecture: topics, partitions, brokers
Producers and consumers in Python
Building a simple streaming pipeline
Stream vs batch processing trade-offs

S3 as a data lake foundation
AWS Glue for cataloging and ETL jobs
Redshift for warehouse-style analytics
IAM, cost control and security basics for data pipelines

Azure Data Factory pipeline design
Azure Synapse Analytics fundamentals
Integrating ADF with Data Lake Storage
Monitoring and cost management in Azure

Databricks notebooks and cluster management
Delta Lake: ACID transactions on data lakes
Building medallion (bronze/silver/gold) architectures
Job scheduling within Databricks

Data lake design patterns
Apache Airflow: DAGs, operators, scheduling
Building an end-to-end orchestrated pipeline
Monitoring, retries and pipeline observability

Build a full pipeline: ingestion → processing → warehouse → dashboard
Deploy and schedule it with Airflow
Portfolio & GitHub project review
Mock interviews on system design and pipeline architecture

Who should join

ETL testers and data analysts moving into engineering roles
Software developers pivoting into data-heavy roles
Backend engineers who want to specialize in data infrastructure
Anyone targeting long-term, high-demand technology careers

Prerequisites

Basic programming exposure is helpful (Python is taught from the ground up)
Working knowledge of SQL fundamentals
Comfort with command-line basics; cloud accounts are set up during the program

Career outcomes

Roles this program prepares you for.

This is our most in-demand program — graduates typically move directly into pipeline-building roles.

ROLE 01

Data Engineer

Design, build and maintain pipelines that move and transform data at scale.

ROLE 02

Big Data Engineer

Specialize in Spark and Kafka-based processing for high-volume systems.

ROLE 03

Cloud Data Engineer

Own data infrastructure on AWS, Azure or Databricks for a product team.

Data Engineering