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Data Engineering with AWS - Helion

Data Engineering with AWS
ebook
Autor: Gareth Eagar
Tytuł oryginału: Data Engineering with AWS
ISBN: 9781800569041
stron: 482, Format: ebook
Data wydania: 2021-12-29
Księgarnia: Helion

Cena książki: 149,00 zł

Dodaj do koszyka Data Engineering with AWS

Written by a Senior Data Architect with over twenty-five years of experience in the business, Data Engineering for AWS is a book whose sole aim is to make you proficient in using the AWS ecosystem. Using a thorough and hands-on approach to data, this book will give aspiring and new data engineers a solid theoretical and practical foundation to succeed with AWS.

As you progress, you'll be taken through the services and the skills you need to architect and implement data pipelines on AWS. You'll begin by reviewing important data engineering concepts and some of the core AWS services that form a part of the data engineer's toolkit. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how the transformed data is used by various data consumers. You'll also learn about populating data marts and data warehouses along with how a data lakehouse fits into the picture. Later, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. In the final chapters, you'll understand how the power of machine learning and artificial intelligence can be used to draw new insights from data.

By the end of this AWS book, you'll be able to carry out data engineering tasks and implement a data pipeline on AWS independently.

Dodaj do koszyka Data Engineering with AWS

 

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Dodaj do koszyka Data Engineering with AWS

Spis treści

Data Engineering with AWS. Learn how to design and build cloud-based data transformation pipelines using AWS eBook -- spis treści

  • 1. An Introduction to Data Engineering
  • 2. Data Management Architectures for Analytics
  • 3. The AWS Data Engineer's Toolkit
  • 4. Data Cataloging, Security and Governance
  • 5. Architecting Data Engineering Pipelines
  • 6. Ingesting Batch and Streaming Data
  • 7. Transforming Data to Optimize for Analytics
  • 8. Identifying and Enabling Data Consumers
  • 9. Loading Data into a Data Mart
  • 10. Orchestrating the Data Pipeline
  • 11. Ad Hoc Queries with Amazon Athena
  • 12. Visualizing Data with Amazon QuickSight
  • 13. Enabling Artificial Intelligence and Machine Learning
  • 14. Wrapping Up the First Part of Your Learning Journey

Dodaj do koszyka Data Engineering with AWS

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