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Data Engineering with AWS Cookbook

You're reading from   Data Engineering with AWS Cookbook A recipe-based approach to help you tackle data engineering problems with AWS services

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Product type Paperback
Published in Nov 2024
Publisher Packt
ISBN-13 9781805127284
Length 528 pages
Edition 1st Edition
Languages
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Authors (4):
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Viquar Khan Viquar Khan
Author Profile Icon Viquar Khan
Viquar Khan
Gonzalo Herreros González Gonzalo Herreros González
Author Profile Icon Gonzalo Herreros González
Gonzalo Herreros González
Huda Nofal Huda Nofal
Author Profile Icon Huda Nofal
Huda Nofal
Trâm Ngọc Phạm Trâm Ngọc Phạm
Author Profile Icon Trâm Ngọc Phạm
Trâm Ngọc Phạm
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Toc

Table of Contents (16) Chapters Close

Preface 1. Chapter 1: Managing Data Lake Storage 2. Chapter 2: Sharing Your Data Across Environments and Accounts FREE CHAPTER 3. Chapter 3: Ingesting and Transforming Your Data with AWS Glue 4. Chapter 4: A Deep Dive into AWS Orchestration Frameworks 5. Chapter 5: Running Big Data Workloads with Amazon EMR 6. Chapter 6: Governing Your Platform 7. Chapter 7: Data Quality Management 8. Chapter 8: DevOps – Defining IaC and Building CI/CD Pipelines 9. Chapter 9: Monitoring Data Lake Cloud Infrastructure 10. Chapter 10: Building a Serving Layer with AWS Analytics Services 11. Chapter 11: Migrating to AWS – Steps, Strategies, and Best Practices for Modernizing Your Analytics and Big Data Workloads 12. Chapter 12: Harnessing the Power of AWS for Seamless Data Warehouse Migration 13. Chapter 13: Strategizing Hadoop Migrations – Cost, Data, and Workflow Modernization with AWS 14. Index 15. Other Books You May Enjoy

Managing data pipelines with MWAA

MWAA is a fully managed service provided by AWS that simplifies the deployment and operation of Apache Airflow, an open source workflow automation platform. Apache Airflow is widely used for orchestrating complex data workflows, scheduling batch jobs, and managing data pipelines. MWAA takes the power of Apache Airflow and makes it easier to use, maintain, and scale in the AWS cloud environment.

MWAA is commonly used to orchestrate complex data pipelines. Users can define and schedule tasks to transform, process, and move data between various AWS services, databases, and external systems. Another good use case is that MWAA simplifies the management of ETL workflows. Users can easily schedule and automate data extraction, transformation, and loading tasks, ensuring data accuracy and consistency. MWAA also supports batch processing and batch jobs, such as data aggregation, report generation, and data synchronization, which can be efficiently managed...

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