Data Engineering Courses

Module 1: Python for Data Science

⏳ 2 weeks

• Topics: Python basics, data structures, functions, NumPy, Pandas

• Hands-on: Analyze data sets and perform data cleaning with Python libraries

Module 2: SQL & Data Modeling

⏳ 2 weeks

• Topics: SQL queries, joins, subqueries, normalization, ER modeling

• Hands-on: Design normalized databases and run analytical queries

Module 3: Big Data Hadoop & Spark

⏳ 3 weeks

• Topics: HDFS, MapReduce, YARN, Spark RDDs, DataFrames, Spark SQL

• Hands-on: Process large datasets and build pipelines using PySpark

Module 4: Apache Kafka

⏳ 1.5 weeks

• Topics: Kafka architecture, producers, consumers, topics, streaming use cases

• Hands-on: Implement real-time data pipelines using Kafka

Module 5: Data Warehousing with AWS Redshift & GCP BigQuery

⏳ 2 weeks

• Topics: OLAP, schema design, data loading, querying, optimization

• Hands-on: Load and analyze data using Redshift and BigQuery

Module 6: Cloud Platforms – AWS & Azure

⏳ 2 weeks

• Topics: EC2, S3, Lambda, Glue, Azure Data Lake, Azure Data Factory

• Hands-on: Build serverless ETL pipelines on AWS and Azure

Module 7: ETL & Data Pipelines with Airflow

⏳ 1.5 weeks

• Topics: DAGs, task scheduling, dependency management, logging

• Hands-on: Automate workflows and monitor ETL pipelines using Airflow

Module 8: Capstone Project

⏳ 2 weeks

• Topics: Real-world data engineering challenges integrating multiple tools

• Hands-on: Design, build, and deploy a full data pipeline from ingestion to visualization

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