Customers can bring data into the data lake from multiple sources through pre-defined templates. Users can automatically classify and prepare the data and centrally define granular data access policies to govern access by the different groups within an organization. They can then analyze the data using their choice of AWS analytics and machine learning services, including Amazon Redshift, Amazon Athena, and AWS Glue, with Amazon EMR, Amazon QuickSight, and Amazon SageMaker integration set for the following months.
No additional charges will be required to use AWS Lake Formation – instead customers pay only for the underlying AWS services used.
“Our customers tell us that Amazon S3 is the ideal place to house their data lakes, which is why AWS hosts more data lakes than anyone else – with tens of thousands and growing every day. They’ve also told us that they want it to be easier and faster to set up and manage their data lakes,” says Raju Gulabani, Vice President, Databases, Analytics, and Machine Learning, AWS. “That’s why we built AWS Lake Formation, so customers can spend more time learning from their data and innovating, rather than wrestling that data into functioning data lakes. AWS Lake Formation is available today and we’re excited to see how customers use it as one of the building blocks for growing and transforming their businesses and customer experiences.”
Learn more about AWS Lake Formation from the Accenture customer success story, outlined in the AWS press release:
Accenture is a leading global professional services company, providing a broad range of services and solutions in strategy, consulting, digital, technology, and operations. “I focus on helping clients in their ‘Data on Cloud’ journey. Specific to that, we have seen that organizations are dealing with a lack of trusted data when they need to perform analytics on data coming from multiple sources,” said Namrata Maheshwary, Senior Architect for the Data Business Group, Accenture. “Data cleansing is a critical step in data analytics and can greatly impact the business outcome and decision making. The new features in AWS Lake Formation have been hugely beneficial to address the challenge of data veracity and securing access to the data lake. We found it tremendously useful to make use of the advanced machine learning techniques for data preparation to find matching records, clean, and deduplicate data from different data sources. This will help reduce the time, effort, and cost, while improving the quality and accuracy of the data in a customer’s data lakes.”