Exploring the Functions of the Amazon S3 Data Lake

 Amazon S3 (Simple Storage Service) is a cloud-based data storage service that stores unstructured, semi-structured, and structured data in their native formats. Data durability of S3 is at an amazing 99.999999999 (11 9s) with data protected in a highly optimized and secure environment. Data files having metadata and objects are stored in buckets. For uploading metadata or files, the object has to be uploaded to Amazon S3 after which permissions can be granted on the metadata or the related objects in the buckets.

Several competencies are used for building an S3 data lake on Amazon S3. These include media data processing applications, Artificial Intelligence (AI), Machine Learning (ML), big data analytics, and high-performance computing (HPC). All these together help businesses get access to vital business intelligence and analytics from the S3 data lake and unstructured data sets.

A benefit of S3 data lake is that computing and storage facilities are in different silos. Hence, all data types can be stored at affordable costs in native formats. This is against the previous systems where the two were so closely interlinked that it was impossible to estimate costs of data processing and storage separately as a part of infrastructure maintenance.

With S3 data lake, it is easy to access the other services of Amazon S3 like serverless computing where codes can be run without managing or provisioning servers. Amazon Web Service platforms like Amazon Athena, Amazon Rekognition, Amazon Redshift Spectrum, and AWS Glue can be used for data processing, querying, and implementation. Additionally, payment for services is only for the quantum of resources used without any flat or upfront fees. 

These are some of the advanced features of the S3 data lake.


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