Data Engineering Syllabus

 Data Engineer syllabus typically covers foundational programming, databases, big data technologies, cloud computing, and data pipeline orchestration. Here's a structured syllabus:

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1. Fundamentals of Data Engineering

• Introduction to Data Engineering

• Roles & Responsibilities of a Data Engineer

• Data Engineering vs. Data Science vs. Data Analytics

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2. Programming for Data Engineering

• Python (Pandas, NumPy, PySpark)

• SQL (Joins, Aggregations, Window Functions)

• Shell Scripting & Bash Commands

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3. Database Management Systems

• Relational Databases (PostgreSQL, MySQL)

• NoSQL Databases (MongoDB, Cassandra)

• Data Modeling & Normalization

• Indexing & Query Optimization

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4. Data Warehousing

• Data Warehouse Concepts (OLAP vs. OLTP)

• ETL vs. ELT Processes

• Popular Data Warehouses (Snowflake, Amazon Redshift, Google BigQuery)

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5. Big Data & Distributed Computing

• Hadoop Ecosystem (HDFS, MapReduce, YARN)

• Apache Spark (RDDs, DataFrames, SparkSQL)

• Apache Kafka (Streaming Data Processing)

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6. Cloud Computing for Data Engineering

• AWS (S3, Lambda, Glue, Redshift)

• Google Cloud (BigQuery, Dataflow)

• Azure Data Services

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7. Data Pipeline Orchestration

• Apache Airflow

• Prefect / Luigi

• Workflow Scheduling & Automation

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8. Data APIs & Integration

• REST & GraphQL APIs

• Data Ingestion with APIs

• Web Scraping for Data Engineering

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9. Data Governance & Security

• Data Quality & Validation

• Data Encryption & Access Control

• GDPR, HIPAA, and Data Compliance

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10. Real-World Projects

• Building an ETL Pipeline

• Data Warehousing with Cloud Technologies

• Streaming Data Processing with Kafka & Spark

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This syllabus covers beginner to advanced topics, making it a solid roadmap for aspiring data engineers.