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Data Engineering for AI Systems

Create scalable data pipelines with SQL, Apache Spark, Hadoop, and Airflow.

Tools you’ll work with
Python
SQL
Apache Spark
Hadoop
Kafka
Airflow
+4 more tools
₹30000
₹70000
Save ₹40000
12 weeksBeginner
Instructor
Arun S
MSc AI • 10+ years • Ex - Microsoft
Enroll Now→

Course Overview

Description

25 lessons•57 exercises•4 exams•~12 hours

Are you looking for a well-structured data science fundamentals course?

Do you want to gain a clear understanding of the data science field?

This is the perfect course for you.

If terms like traditional data, big data, business intelligence, and machine learning sound confusing, this course will help you understand both meaning and practical application.

25 lessons•57 exercises•4 exams•12 hours

Course Curriculum

A structured, progressive curriculum designed to build depth, intuition, and real-world proficiency over time.

This section introduces key concepts and builds intuition through structured lessons and exercises.

Introduction to Data Engineering and AI Systems
Data Lifecycle, Data Types and Architecture
Data Collection from APIs, Databases and Streams
Batch vs Real-Time Data Ingestion Techniques
ETL vs ELT Pipelines and Data Integration
Data Cleaning, Transformation and Preprocessing
Case Study: Building Data Pipelines for AI Applications

This section introduces key concepts and builds intuition through structured lessons and exercises.

Introduction to Big Data and Distributed Systems
Hadoop Ecosystem and HDFS
Apache Spark for Batch Data Processing
Stream Processing with Spark Streaming and Kafka
Scalability, Fault Tolerance and Performance
Processing Large Datasets for AI Workloads
End to End Big Data Pipeline Implementation

This section introduces key concepts and builds intuition through structured lessons and exercises.

Python for Data Engineering - Basics and File Handling
Working with Pandas for Data Manipulation
Data Processing with Numpy and PySpark
Building Data Pipelines using Python
Data Import/Export and Integration with Databases

This section introduces key concepts and builds intuition through structured lessons and exercises.

Introduction to Cloud Platforms (AWS, Azure, GCP)
Cloud Storage Services and Data Lakes
Workflow Orchestration with Apache Airflow
Data Warehousing and Data Modeling Concepts
Monitoring, Optimization and Deployment of Pipelines

This section introduces key concepts and builds intuition through structured lessons and exercises.

Introduction to Data Governance and Compliance
Data Quality, Validation and Consistency
Data Security, Privacy and Encryption Basics
Access Control and Data Protection Techniques
Case Study: Securing Data Pipelines in AI Systems

How You’ll Learn

This course is designed to help you move beyond tutorials — toward deep understanding, confident implementation, and long-term career growth.

Live Classes
Quizzes
Assignments
Projects
Certification

Build Skills That Scale With You

This course is designed to help you move beyond tutorials — toward deep understanding, confident implementation, and long-term career growth.

Learn deeply. Apply repeatedly.