Introduction to Data Engineering
( 24 Modules )

Module #1
Introduction to Data Engineering
Overview of data engineering, its importance, and career paths

Module #2
Data Engineering Fundamentals
Key concepts, data pipelines, and data processing

Module #3
Data Storage Options
Relational databases, NoSQL databases, and data warehousing

Module #4
Big Data and Distributed Systems
Introduction to big data, Hadoop, and distributed systems

Module #5
Data Ingestion and Integration
Data ingestion methods, APIs, and data integration patterns

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

Module #7
Apache Spark Introduction
Overview of Apache Spark, its ecosystem, and use cases

Module #8
Spark Core and DataFrames
Working with Spark Core, DataFrames, and datasets

Module #9
Spark Streaming and Structured Streaming
Real-time data processing with Spark Streaming and Structured Streaming

Module #10
Data Warehousing and Analytics
Introduction to data warehousing, star and snowflake schemas

Module #11
Data Visualization and Reporting
Data visualization tools, reporting, and business intelligence

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

Module #13
Security and Access Control
Data security, access control, and compliance

Module #14
Cloud Computing for Data Engineering
Cloud computing services, AWS, GCP, and Azure for data engineering

Module #15
Building Data Pipelines
Designing and building data pipelines with Apache Beam and Apache NiFi

Module #16
Monitoring and Observability
Monitoring data pipelines, logging, and observability

Module #17
Data Engineering Best Practices
Design patterns, testing, and deployment strategies

Module #18
Case Studies and Industry Applications
Real-world applications of data engineering in various industries

Module #19
Data Engineering Tools and Technologies
Survey of data engineering tools, Kafka, Flink, and more

Module #20
Machine Learning and AI for Data Engineers
Introduction to machine learning and AI for data engineers

Module #21
Collaboration and Communication
Soft skills for data engineers, collaboration, and communication

Module #22
Career Development and Growth
Career paths, growth opportunities, and staying current in data engineering

Module #23
Project Development and Implementation
Guided project development and implementation

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

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