 Название: AWS Lake House Architecture (Early Release)
Автор: Changbin Gong, Raghavarao Sodabathina
Издательство: O’Reilly Media, Inc.
Год: 2021-11-22
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB
To gain deeper and richer insights, your organization needs to analyze relevant data from all sources. That requires moving data easily between your data lake and purpose-built data warehouses. But as these data stores grow, the data they hold only becomes harder to move. This practical book shows you how to overcome this data gravity issue with a new modern data lake house architecture.
Authors Changbin Gong and Raghavarao Sodabathina, solutions architects at Amazon Web Services, show you how to connect your data lake, data warehouse, and other purpose-built services into a coherent whole. With this guide, cloud architects and intermediate and senior developers will learn how an AWS data lake provides a single place where you can run analytics across most of your data.
It is important for a data lake to be scalable because it needs to store and handle large amounts of data. To be scalable here means that the data lakes can have virtually unlimited capacity to store the ingested data. The scalable data lakes pillar of a data lake house architecture is responsible for providing durable, scalable, and cost-effective components to store and manage vast quantities of data. In this data lake house architecture, the data lakes provide a cost-effective storage component that supports various data types including structured, semi-structured, and unstructured data in batch or in steaming. Data lakes make it easier to gain insights from all of the data by providing a single place to access it all. The data lakes natively integrate with the purpose-built data analytics services which can transform and analyze the data in the data lakes to bring insights to the data consumers like data analysts.
Understand key concepts and components of the AWS data lake house architecture
Get practical guidance for building your data lake house architecture in cloud environments
Define data strategy for both structure and unstructured data
Build efficient data lake house architecture based on your specific requirements
Learn how to gain data insights quickly and at scale
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