Data Engineering with Python and Cloud
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Course Overview
Course Modules Module 1: Data Engineering Fundamentals • Data Engineering Role • Data Pipelines • ETL vs ELT • Data Warehouses vs Data Lakes • Batch vs Streaming Processing • Modern Data Stack Module 2: SQL & Database Design • Advanced SQL Queries • Database Normalization • Indexing & Optimization • PostgreSQL & MySQL • NoSQL Databases • Schema Design Module 3: Data Pipeline Development • Apache Airflow • DAG Design • Pipeline Orchestration • Data Quality Checks • Error Handling • Monitoring & Alerting Module 4: Big Data Technologies • Apache Spark • PySpark • Hadoop Ecosystem • Distributed Computing • Data Partitioning • Performance Tuning Module 5: Cloud Data Platforms • AWS Redshift & S3 • Google BigQuery • Azure Synapse • Snowflake • Cloud Storage Solutions • Cost Optimization Module 6: Capstone Project • Pipeline Architecture Design • Data Ingestion • Transformation Logic • Cloud Deployment • Testing & Validation • Documentation Capstone Project Design and build a scalable data pipeline that ingests, transforms, and loads data into a cloud data warehouse.
Learning Outcomes
Design and implement robust ETL/ELT pipelines.
Work with big data tools like Spark and Hadoop.
Deploy and manage data workflows on cloud platforms.
Ensure data quality, reliability, and observability.
Architect modern cloud-based data infrastructure.
Prerequisites
• Proficiency in Python programming • Understanding of SQL and relational databases • Basic knowledge of cloud computing (preferred)
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