Data Engineering Course Syllabus
Introduction to Data Engineering
- Overview of Data Engineering: Definition and role of a data engineer, Differences between data engineering, data science, and data analytics
- Data Ecosystem: Types of data (structured, semi-structured, unstructured), Data lifecycle (collection, storage, processing, analysis, visualization)
Data Modeling and Database Systems
- Relational Databases: Introduction to SQL, Database design principles (normalization, denormalization), SQL queries and optimization
- NoSQL Databases: Types of NoSQL databases (document, key-value, column-family, graph), Use cases and data models for NoSQL
- Data Warehousing: Introduction to data warehousing concepts, ETL vs. ELT processes, Data warehouse architectures (Kimball, Inmon)
Data Ingestion and ETL Processes
- Data Ingestion Techniques: Batch vs. streaming data ingestion, Tools and frameworks for data ingestion (Apache Kafka, Apache NiFi)
- ETL Tools: Introduction to ETL tools (Apache Airflow, Talend, Informatica), Designing and implementing ETL pipelines, Error handling and logging in ETL processes
Big Data Technologies
- Introduction to Big Data: Characteristics of big data (volume, velocity, variety), Use cases for big data
- Apache Hadoop Ecosystem: HDFS (Hadoop Distributed File System), MapReduce programming model, Tools: Hive, Pig, HBase
- Apache Spark: Introduction to Spark and its components, Spark Core and Spark SQL, Streaming with Spark Streaming, Machine Learning with MLlib
Data Storage and Management
- Data Lakes: Definition and architecture of data lakes, Tools and technologies for data lakes (Amazon S3, Azure Data Lake)
- Data Governance and Quality: Data quality frameworks, Data lineage and metadata management, Tools for data governance (Apache Atlas, Collibra)
Cloud Data Engineering
- Introduction to Cloud Computing: Overview of cloud platforms (AWS, Azure, Google Cloud)
- Data Engineering on Cloud Platforms: Data storage services (Amazon S3, Google Cloud Storage), Managed databases (Amazon RDS, Azure SQL Database), Data pipelines in the cloud (AWS Glue, Google Dataflow)
Data Visualization and Reporting
- Data Visualization Principles: Importance of data visualization, Best practices for creating effective visualizations
- Tools for Data Visualization: Overview of visualization tools (Tableau, Power BI, Looker), Building dashboards and reports, Integrating visualizations with data sources
Capstone Project
- Project Design and Implementation: Identifying a real-world data engineering problem, Designing and implementing a data pipeline, Presenting findings and insights from the project
Best Practices and Future Trends
- Data Engineering Best Practices: Version control for data pipelines, Testing and monitoring ETL processes
- Future Trends in Data Engineering: Emerging technologies (AI/ML in data engineering), Trends in data privacy and security
Data Engineering Syllabus
1. Introduction to Data Engineering
2. Data Modeling and Database Systems
3. Data Ingestion and ETL Processes
4. Big Data Technologies
5. Data Storage and Management
6. Cloud Data Engineering
7. Data Visualization and Reporting
8. Capstone Project
9. Best Practices and Future Trends
