The Functioning of Data Lake Built on Amazon S3

 The Simple Storage Service (S3) of Amazon is a cloud-based data storage service that stores data in its native format – unstructured, semi-structured, or structured. Here, metadata and objects in data files are stored in buckets.

For uploading files or metadata, the objects have to be first moved to Amazon S3. After this step is completed, permissions can be set on the metadata or the related objects stored in the buckets (containers) and selected personnel are given access to them. They are given the authority to decide where the logs and objects will be stored on Amazon S3.

Various competencies can be 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 used in tandem give organizations access to critical business intelligence and analytics as well as unstructured data sets from S3 data lake.
There are several benefits of the Amazon S3 data lake.

The most advantageous for users is the separate computing and storage facilities thereby making it possible to accurately estimate the costs of data processing and storage and infrastructure maintenance separately.

Further, by being on Amazon S3, the users of S3 data lakeget access to the services of Amazon S3 for serverless computing where codes can be run without having to manage or provision servers. All tasks related to data processing, querying, and implementation can be done on both serverless and non-cluster Amazon Web Service platforms like Amazon Athena, Amazon Rekognition, Amazon Redshift Spectrum, and AWS Glue.

These are some of the reasons why Amazon S3 data lake is the preferred service for modern business environment.     

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