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Functioning of Data Lakes Built on Amazon S3

 Amazon S3 (Simple Storage Service) is a cloud-based and optimized data storage service, storing data in its native format regardless of whether it is unstructured, semi-structured, or structured. The durability of data in S3 is 99.999999999 (11 9s) and data in any volume is stored in a fully safe and secured environment.

Many competencies can be used when an S3 data lake is built on Amazon S3. The critical ones are media data processing applications, Artificial Intelligence (AI), Machine Learning (ML), big data analytics, and high-performance computing (HPC). When all these are linked up, businesses get access to critical data and business intelligence and analytics from unstructured data sets as well as the S3 data lake.

There are several benefits of Amazon S3 data lake.

Computing and storage facilities are in different silos in S3 data lake and data in any format can be stored here. Compare this with traditional systems where computing and storage were closely interlinked, making estimating the costs of maintaining each facility almost impossible. 

Users on S3 data lake get access to Amazon S3 serverless computing. Here codes can be run without having to provision or manage servers. Data processing, querying, and implementation can be carried out both on serverless and non-cluster Amazon Web Service platforms. These include Amazon Athena, Amazon Rekognition, Amazon Redshift Spectrum, and AWS Glue.

Finally, the APUs of the S3 data lake issupported by several third-party vendors like Amazon Hadoop that can be easily used on the S3 data lake.

These are the advanced features and capabilities of the S3 data lake that make it stand out among the traditional data lakes.


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