Overview
This course provides a solid foundation in data engineering concepts, focusing on understanding data principles, building data pipelines, and working with data storage and processing systems. You will gain hands-on experience with techniques and tools that enable efficient data management and scalable architectures, preparing you to handle real-world data engineering challenges.
Objectives
By the end of this course, participants will be able to:
Prerequisites
- Basic understanding of data concepts and system design.
- Familiarity with software development or programming (e.g., Java, Scala, or Python).
- Fundamental knowledge of distributed systems is beneficial.
Course Outline
Module 1: Introduction to Data Engineeringkeerti2024-12-17T10:44:28+05:30
- Overview of Data Engineering concepts
- Data lifecycle and key challenges
- Batch vs. real-time data processing
Module 2: Data Ingestion and Collectionkeerti2024-12-17T10:45:43+05:30
- Techniques for ingesting data from multiple sources
- Working with APIs, files, and message brokers
- Tools and frameworks for data ingestion
Module 3: Data Storage Solutionskeerti2024-12-17T10:46:24+05:30
- Understanding different storage paradigms: Relational vs. Non-relational databases
- File systems, data lakes, and cloud-based storage
- Selecting appropriate storage based on use cases
Module 4: Data Transformation and Processingkeerti2024-12-17T10:47:04+05:30
- Batch processing fundamentals
- Real-time stream processing principles
- Data processing tools and frameworks
Module 5: Building Scalable Data Pipelineskeerti2024-12-17T10:47:45+05:30
- Principles of building robust and scalable pipelines
- Optimizing performance and resource management
- Monitoring and maintaining data pipelines
Module 6: Ensuring Data Reliability and Qualitykeerti2024-12-17T10:48:25+05:30
- Data validation, error handling, and recovery strategies
- Best practices for ensuring data quality
- Data governance and security principles
Module 7: Real-World Applications of Data Engineeringkeerti2024-12-17T10:49:07+05:30
- Case studies on scalable architectures
- Integrating data engineering tools into existing systems
- Common challenges and solutions in production environments
