Data Eng.

Data Engineering – Complete Course Content (Beginner to Advanced)

Introduction to Data Engineering

• Role of Data Engineer
• Data Engineering vs Data Science vs Data Analytics
• Modern data ecosystem overview
• ETL vs ELT
• Batch vs Streaming data
• Data lake, Data warehouse, Data lakehouse concepts

Linux & Shell Basics (For Data Engineers)

• Basic Linux commands
• File system navigation
• File manipulation
• Shell scripting basics
• Automating simple workflows

Python for Data Engineering

Core Python

• Variables, data types, loops, functions
• File handling
• Exception handling

Python for Data Pipelines

• Working with CSV, JSON, Excel
• Reading/writing large datasets
• Pandas for data processing
• Boto3 / APIs (basic introduction for pipelines)

SQL for Data Engineering

• SQL fundamentals
• Joins, aggregations, subqueries
• CTEs
• Window functions
• Query optimization basics
• Writing analytical SQL queries
• Stored procedures & functions (basics)

Data Modeling

• OLTP vs OLAP
• Star schema & Snowflake schema
• Fact & dimension tables
• Normalization & denormalization
• Slowly Changing Dimensions (SCD Type 1, 2)

ETL / ELT & Pipeline Concepts

• What is a pipeline?
• Data ingestion techniques
• Data transformation layers
• Batch pipelines
• Incremental loads & change data capture (CDC)
• Error handling & logging
• Orchestration concepts

Big Data & Distributed Systems

• Hadoop ecosystem overview
• HDFS concepts
• MapReduce basics
• YARN & distributed computation
• Why Spark replaced Hadoop MapReduce

Apache Spark (Core Skill for Data Engineers)

Spark Core

• RDD concepts
• Transformations & actions
• Lazy evaluation
• Partitioning

Spark SQL

• DataFrames
• Dataset API
• SQL queries
• Optimizer & Catalyst
• UDFs

Spark for ETL

• Reading/writing CSV, JSON, Parquet
• Working with large datasets
• Writing pipelines in PySpark

Cloud Data Engineering

Azure / AWS / GCP Overview

• IAM concepts
• Storage services
• Compute services
• Serverless basics

Cloud Storage

• AWS S3 / Azure Blob / GCP Storage

Compute & Processing

• AWS EMR / Azure Databricks / GCP Dataproc
• Serverless compute (AWS Lambda / Azure Functions)

Cloud Data Warehouse

• Snowflake
• AWS Redshift
• Azure Synapse
• Google BigQuery

Modern Data Pipelines Tools

• Airflow (workflow orchestration)
• ADF / AWS Glue
• Databricks workflows
• Kafka basics (streaming ingestion)
• Event hubs / Pub-sub overview

Data Quality & Governance

• Data validation techniques
• Data quality checks (DQ rules)
• Logging & monitoring
• Metadata management
• Lineage basics (Collibra/Apache Atlas overview)

DevOps for Data Engineering

• Git & GitHub
• CI/CD concepts
• Docker basics
• Deploying ETL pipelines
• Environment management

Mini Projects + Capstone

• ETL pipeline using SQL + Python
• PySpark batch processing pipeline
• Data ingestion from API to cloud storage
• End-to-end pipeline using Airflow/ADF
• Data warehouse modeling project
• Resume building for Data Engineers
• Interview preparation + coding round