Jump-Start Analytics Using Cloud Data Warehousing
The massive adoption of cloud based services and applications has accelerated growth in storage and usage of data in all forms and types - including customer interaction data, social media, devices, log data, stream data, and more. Data is playing a pivotal role in modern business operations, serving as a valuable asset that fuels informed decision-making to drive growth. By harnessing and analyzing this data, businesses can gain a competitive advantage by identifying patterns and correlations to make data-driven decisions and drive innovation. As the volume, velocity, and variety of data continues to grow exponentially, it has become increasingly crucial for businesses to have efficient and scalable data warehousing solutions in the cloud that can handle the demands of today’s data-driven world.
In this eBook, authors Rajesh Francis, Rajiv Gupta, and Milind Oke detail the powerful capabilities of Amazon Redshift and its role in modern data warehousing. Whether you are a data professional, architect, IT leader, or simply someone curious about data management and analytics, this eBook is designed to provide you with comprehensive insights into modern data warehousing patterns and techniques using Amazon Redshift.
With this O'Reilly guide, provided compliments of AWS, you'll learn:
- How modern cloud data warehousing integrated across data sources in an enterprise environment can get you to near real-time and predictive analytics on all your data
- Getting started with Amazon Redshift Serverless and tapping into data across the data lake and databases to conduct high performance analytics and machine learning workflows in SQL
About Amazon Redshift
Tens of thousands of customers use Amazon Redshift everyday to analyze data and meet business outcomes using data. Regardless of skill level, get started in a few clicks with a serverless architecture that drives scale and high performance analytics workloads, keeping costs predictable and reasonable. Power decisions by accessing data across data warehouses, operational databases, data lakes, and even 3rd party data warehouses with a zero-ETL approach. Create and train ML models using familiar SQL. Securely share data across departments or regions. Build reliable, high performance, data-driven systems and applications with as much flexibility and granular authorization controls as required.