Performing data engineering with Amazon Web Services combines AWS's scalable infrastructure with robust data processing tools, enabling efficient data pipelines and analytics workflows. This comprehensive guide to AWS data engineering will teach you all you need to know about data lake management, pipeline orchestration, and serving layer construction.
Through clear explanations and hands-on exercises, you’ll master essential AWS services like Glue, EMR, Redshift, Athena, and QuickSight. Additionally, you’ll explore data governance, DevOps, CI/CD, and Infrastructure as Code. As you progress, you’ll also gain insights into Tableau Server and Cloud.
By the end of this book, you’ll be well-versed in AWS data engineering and have gained proficiency in key AWS services, mastered data processing techniques, and developed the skills necessary to tackle large-scale data challenges with confidence.

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Spis treści

Data Engineering with AWS Cookbook. A recipe-based approach to help you tackle data engineering problems with AWS services eBook -- spis treści

• 1. Managing Data Lake Storage
• 2. Sharing Your Data Across Environments and Accounts
• 3. Ingesting and Transforming Your Data with AWS Glue
• 4. A Deep Dive into AWS Orchestration Frameworks
• 5. Running Big Data Workloads with Amazon EMR
• 6. Governing Your Platform
• 7. Data Quality Management
• 8. DevOps – Defining IaC and Building CI/CD Pipelines
• 9. Monitoring Data Lake Cloud Infrastructure
• 10. Building a Serving Layer with AWS Analytics Services
• 11. Migrating to AWS – Steps, Strategies, and Best Practices for Modernizing Your Analytics and Big Data Workloads
• 12. Harnessing the Power of AWS for Seamless Data Warehouse Migration
• 13. Strategizing Hadoop Migrations – Cost, Data, and Workflow Modernization with AWS