What is a Data Lake?
A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data, and run different types of analytics — from dashboards and visualizations to big data processing, real-time analytics, and machine learning to guide better decisions.
More info: https://aws.amazon.com/big-data/datalakes-and-analytics/what-is-a-data-lake/
Why do you need a data lake?
Organizations that successfully generate business value from their data, will outperform their peers. An Aberdeen survey saw organizations who implemented a Data Lake outperforming similar companies by 9% in organic revenue growth. These leaders were able to do new types of analytics like machine learning over new sources like log files, data from click-streams, social media, and internet connected devices stored in the data lake. This helped them to identify, and act upon opportunities for business growth faster by attracting and retaining customers, boosting productivity, proactively maintaining devices, and making informed decisions.
Data Lakes and Analytics on AWS
Customers leverage AWS Analytics as the fastest way to get answers from all of their data to all their users. The breadth and depth of analytics services on AWS makes it easy to spin up the right resources to run whatever analysis is most appropriate for a specific need. Deep integration between all the layers of the AWS analytics stack gives builders the tools to quickly analyze data using any approach.
More info: https://aws.amazon.com/big-data/datalakes-and-analytics/
Building Big Data Storage Solutions (Data Lakes) for Maximum Flexibility
Organizations are collecting and analyzing increasing amounts of data making it difficult for traditional on-premises solutions for data storage, data management, and analytics to keep pace. Amazon S3 and S3 Glacier provide an ideal storage solution for data lakes. They provide options such as a breadth and depth of integration with traditional big data analytics tools as well as innovative query-in-place analytics tools that help you eliminate costly and complex extract, transform, and load processes. This guide explains each of these options and provides best practices for building your Amazon S3-based data lake.
More info: https://docs.aws.amazon.com/whitepapers/latest/building-data-lakes/building-data-lake-aws.html
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