Functioning and Concepts of Amazon S3 Data Lake

 Amazon S3 (Simple Storage Service) is a cloud storage platform. Data is stored here in buckets with each file having an object and metadata that can be stored in a bucket by loading to S3. Once done, permissions can be set on that metadata or the object. Only authorized personnel can take decisions as to where the buckets and their contents will be placed on an Amazon S3 storage repository.

Users can access many competencies when building an S3 data lake like big data analytics, high-performing computing (HPC), machine learning (ML), artificial intelligence (AI), and media data processing applications. All these help businesses gain critical insights into unstructured data sets. With HPC and ML applications, users can quickly start files and process massive amounts of media workloads from the S3 data lake with Amazon FSx for Luster. Users also have the flexibility to use the Amazon Partner Network for HPC, ML, and AI applications through the S3 data lake.

S3 data lake has separate computing and storage facilities. In traditional systems, the two have always been closely interlinked, making it very difficult to accurately estimate costs of IT infrastructure maintenance. On the other hand, on the S3 data lake, all data types can be stored in their native formats and AWS analytics tools used to process data when Amazon Elastic Compute Cloud (EC2) launches virtual servers. 

Finally, Amazon S3 data lake has user-friendly APIs supported by multiple third-party vendors like Apache Hadoop and other analytics tools suppliers. Users get to work with the tool of their choice to carry out data analytics on Amazon S3.

This is how Amazon S3 data lake helps businesses run IT infrastructure seamlessly and cost-effectively.  


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