Data Engineering

Data Engineering focuses on building and managing the systems and pipelines that enable data collection, storage, and processing at scale. This course equips learners with skills in databases, big data tools, cloud platforms, and ETL (Extract, Transform, Load) processes — preparing them for careers in data engineering and big data management.

Duration: Flexible

Fees: Contact us for details

Syllabus Highlights

Introduction to Data Engineering & Data Ecosystem
Databases (SQL & NoSQL)
SQL (MySQL / PostgreSQL / Oracle)
NoSQL (MongoDB, Cassandra)
Data Warehousing Concepts
ETL Processes (Extract, Transform, Load)
Big Data Fundamentals (Hadoop & HDFS)
Distributed Data Processing with Apache Spark
Data Pipelines with Apache Airflow / Luigi
Cloud Platforms for Data Engineering (AWS, Azure, GCP basics)
Data Lakes & Data Warehouses (Snowflake, Redshift, BigQuery)
Batch Processing vs Real-Time Processing (Kafka, Spark Streaming)
Data Security, Governance & Compliance
Introduction to Machine Learning Data Pipelines

Projects

Building an ETL Pipeline for Sales Data
Data Warehouse for a Retail Business
Real-Time Streaming Pipeline with Kafka & Spark
Cloud-Based Data Lake Implementation
End-to-End Data Pipeline Project (Big Project)

Outcomes

Strong foundation in SQL, NoSQL & Big Data tools
Ability to design & build scalable data pipelines
Hands-on experience with cloud-based data platforms
Readiness for roles like Data Engineer, Big Data Engineer & Cloud Data Specialist