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Building a Data Lake on Amazon Simple Storage Service

Amazon Simple Storage Service (S3) is a cloud-based data storage service that stores data in its native format. Data durability of S3 is always at a high of 99.999999999 (11 9s), and the data regardless of the volume is stored in a fully secured and safe ecosystem. In Amazon S3, data files that contain metadata and objects are stored in buckets for uploading. For metadata and files, the object is to be uploaded to S3. After this step, permissions can be granted on the metadata or related objects stored in the buckets.

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


There are several benefits of the S3 data lake.

The first is different computing and storage silos that were not there in traditional databases. Hence, the costs of each facility in relation to data processing, storage, and infrastructure maintenance can now be accurately calculated. Further, in S3 data lake, all types of data in native format can be stored including unstructured, semi-structured, and structured data, all at very affordable costs.

Again, users on S3 data lake can process, query, and implement data on both serverless and non-cluster Amazon Web Service platforms like Amazon Athena, Amazon Rekognition, Amazon Redshift Spectrum, and AWS Glue. Most importantly, payment is in proportion to the storage and computing facilities used without any flat fees.   

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