Data Engineering and Data Warehousing

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Course Content Overview:

Databases & SQL – Learn data modeling, normalization, and querying using tools like MySQL and PostgreSQL.
Data Warehousing – Understand OLAP systems, ETL/ELT workflows, and platforms like Snowflake, Redshift, and BigQuery.
ETL Development – Build automated pipelines using Apache Airflow, AWS Glue, and dbt.
Python for Data Engineering – Use Python, Pandas, and NumPy for data extraction, transformation, and cleaning.
Big Data Technologies – Work with distributed systems like Hadoop, Spark, and Hive for large-scale data processing.
Cloud Data Engineering – Deploy and manage data systems on AWS, Azure, or Google Cloud Platform (GCP).
Real-Time Data Streaming – Implement live data pipelines using Apache Kafka and Spark Streaming.
Data Integration & APIs – Collect and merge data from multiple sources using REST APIs, GraphQL, and web scraping.
Data Governance & Security – Apply privacy, compliance, and access control using tools like the ELK Stack.
Business Intelligence (BI) – Create dashboards and reports with Power BI, Tableau, or Looker.
DevOps for Data Pipelines – Learn CI/CD, Docker, and GitHub Actions for deployment automation.
Capstone Project – Design an end-to-end data pipeline integrating ETL, cloud, big data, and BI tools.