Building Data Lake on Amazon S3

Amazon S3 (Simple Storage Service) is a cloud-based data storage service that facilitates data storage in native form – unstructured, semi-structured, or structured. Data is stored in a secure environment with data durability as high as 99.999999999 (11 9s).

Data in S3 is stored in buckets with files consisting of metadata and objects. An object has to be uploaded to Amazon S3 for storing a file or metadata in a bucket. Once done, permissions can be set on the object or the related metadata stored in the buckets for holding the objects. Access to the buckets is restricted to those who have the required permissions to access logs and objects for deciding where they will be stored on Amazon S3.

The Concept of Amazon S3 Data Lake

Several competencies are used when building an S3 data lake such as Machine Learning (ML), Artificial Intelligence (AI), big data analytics, media data processing applications, and high-performance computing (HPC). All these together help to get critical business intelligence and analytics from unstructured data sets and the S3 data lake.


It is easy to process large volumes of media workloads with Amazon FSx for Luster from the S3 data lake through file systems for HPC and ML applications. The S3 data lake is also optimized for specific analytics like ML, AI, and HPC applications from the Amazon Partner Network (APN).

Benefits of the Amazon S3 data lake?

The Amazon S3 data lake has several advanced and cutting-edge features.

· In the past, data warehousing systems had very closely integrated computing and storage facilities. This made it almost impossible to individually estimate the costs of data processing and infrastructure maintenance. On the other hand, the S3 data lake has separate silos for computing and storing data cost-effectively in their native formats.

Moreover, in the S3 data lake, virtual servers may be launched with the Amazon Elastic Cloud Compute (EC2) and data processing done with the analytics tool of Amazon Web Service (AWS). To optimize the precise ratios to be allocated for bandwidth, memory, and CPU to improve the performance of the S3 data lake an EC2 instance can also be used

· Data processing, querying, and implementation across serverless and non-cluster AWS platforms are provided by the S3 data lake. These platforms include Amazon Athena, Amazon Recognition, Amazon Redshift Spectrum, and AWS Glue. Users can avail of the services of Amazon S3 for serverless computing to run codes without managing or provisioning servers. Charges are only for computing and storage resources used and not a flat one-time fee.

· The APIs of the Amazon S3 data lake supports several third-party vendors like Apache Hadoop and other analytics tools suppliers. Users can use their preferred tools on the Amazon S3 data lake.

These are some of the features that make Amazon S3 data lake the most-used service in the modern business environment.

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