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As enterprises collect increasing amounts of data from various sources, the structure and organization of that data often need to change over time to meet evolving analytical needs. Schema evolution enables adding, deleting, renaming, or modifying columns without needing to rewrite existing data.
Athena provides a simplified, flexible way to analyze petabytes of data where it lives. You can analyze data or build applications from an Amazon Simple Storage Service (Amazon S3) datalake and 30 data sources, including on-premises data sources or other cloud systems using SQL or Python.
2002: Microsoft launches the.NET initiative. Google launches BigQuery, its own data warehousing tool and Microsoft introduces Azure SQL Data Warehouse and Azure DataLake Store. AWS rolls out SageMaker, designed to build, train, test and deploy machine learning (ML) models. 1998: Google comes into existence.
AWS is introducing general availability of fine-grained access control based on AWS Lake Formation for Amazon EMR Serverless on Amazon EMR 7.2. Enterprises can now significantly enhance their data governance and security frameworks. For instructions, refer to Create a datalake administrator.
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