Enroll now in our AWS Glue Course to gain hands-on experience through live interactive sessions, real-world data projects, and personalized mentorship. The course is fully aligned with the latest cloud and data engineering practices,
enabling you to confidently design, orchestrate, and deliver scalable data pipelines with precision and efficiency.
Prerequisites
- Understanding of cloud computing concepts and AWS ecosystem basics
- Basic knowledge of databases, tables, and data formats
- Basic programming knowledge in Python or SQL
- Understanding of Extract, Transform, Load (ETL) processes
What Will You Learn
1. AWS Glue Fundamentals
- Understand the architecture, components, and use cases of AWS Glue.
- Learn how Glue simplifies ETL processes and data integration in the cloud.
2. Working with the AWS Glue Data Catalog
- Create and manage databases and tables.
- Use crawlers to automatically discover and catalog data.
- Implement custom classifiers for structured and semi-structured data.
3. Building ETL Jobs with AWS Glue Studio
- Design, develop, and schedule ETL pipelines.
- Apply transformations like filtering, joining, and aggregating data.
- Debug, monitor, and optimize Glue jobs for better performance.
4. Data Transformation and Processing
- Work with DynamicFrames and Spark DataFrames for advanced data processing.
- Handle complex data formats, nested JSON, and large datasets.
- Implement best practices for efficient ETL workflows.
5. AWS Glue DataBrew
- Learn to prepare, clean, and normalize data without writing code.
- Create recipes and run data preparation projects interactively.
- Automate recurring data curation tasks with scheduled DataBrew jobs.
6. Workflows and Automation
- Orchestrate multiple ETL jobs using Glue Workflows.
- Configure triggers: scheduled, conditional, or event-based.
- Implement error handling and retry strategies for robust pipelines.
7. Integration with AWS Services
- Connect Glue with S3, Redshift, RDS, and other AWS services.
- Understand security best practices using IAM and KMS.
- Leverage real-time streaming ETL for dynamic data processing.