Data Engineering
( 24 Modules )

Module #1
Introduction to Data Engineering
Overview of data engineering, importance, and role in data science

Module #2
Data Engineering Fundamentals
Data processing, data storage, and data architecture basics

Module #3
Data Ingestion Patterns
Designing and implementing data ingestion pipelines

Module #4
Data Storage Options
Relational databases, NoSQL databases, and data warehousing

Module #5
Big Data Storage Solutions
HDFS, HBase, and other big data storage options

Module #6
Data Processing Fundamentals
Batch processing, stream processing, and real-time processing

Module #7
Apache Spark Fundamentals
Introduction to Apache Spark, Spark Core, and Spark SQL

Module #8
Batch Processing with Apache Spark
Batch processing use cases and implementations with Spark

Module #9
Stream Processing with Apache Spark
Stream processing use cases and implementations with Spark

Module #10
Real-Time Processing with Apache Flink
Introduction to Apache Flink and real-time processing use cases

Module #11
Data Pipelines and Workflow Management
Designing and implementing data pipelines with Apache Airflow and Apache NiFi

Module #12
Data Quality and Data Governance
Data quality metrics, data governance, and data lineage

Module #13
Data Security and Access Control
Data encryption, access control, and security best practices

Module #14
Cloud-Based Data Engineering
Cloud-based data engineering with AWS, GCP, and Azure

Module #15
Containerization and Orchestration
Containerization with Docker and orchestration with Kubernetes

Module #16
Monitoring and Logging in Data Engineering
Monitoring and logging best practices in data engineering

Module #17
Testing and Validation in Data Engineering
Testing and validation strategies for data pipelines and systems

Module #18
Data Engineering for Machine Learning
Data engineering for machine learning models and AI applications

Module #19
Real-World Data Engineering Use Cases
Case studies and real-world examples of data engineering applications

Module #20
Data Engineering Tools and Technologies
Survey of data engineering tools and technologies

Module #21
Data Engineering Best Practices
Best practices for data engineering design, development, and deployment

Module #22
Data Engineering at Scale
Scalability and performance considerations for large-scale data engineering systems

Module #23
Data Engineering for Data Science
Collaboration between data engineers and data scientists

Module #24
Course Wrap-Up & Conclusion
Planning next steps in Data Engineering career

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