The Concept of an S3 Data Lake

Amazon Simple Storage Service (S3) is a cloud-based platform where data in its native format can be stored, regardless of whether it is in an unstructured, semi-structured, or structured format. Data on S3 is stored in a fully safe environment with data durability at 99.999999999 (11 9s).

Data is stored in buckets in Amazon S3 with files containing metadata and objects. An object has to be uploaded to Amazon S3 when a file or metadata has to be stored in a bucket. Once this step is executed, permissions can be set on the object or the metadata stored in the buckets that hold the objects. Select staff can be granted access permission to the buckets and only they can decide where the logs and the objects will be stored on Amazon S3.

Several competencies such as Artificial Intelligence (AI), big data analytics, Machine Learning (ML), media data processing applications, and high-performance computing (HPC) may be used for building an S3 data lake. This data lake can be used to obtain vital and incisive business intelligence and analytics from unstructured data sets.

From the S3 data lake, massive volumes of media workloads can be processed with Amazon FSx for Luster through file systems for HPC and ML applications. Through the Amazon Partner Network (APN), this data lake can also be used for carrying out specific analytics ML, AI, and HPC applications.

Critical Features of the Amazon S3 Data Lake

There are several important features of the S3 data lake. These are as follows.

• Separate storage and computing facilities exist on the S3 data lake whereas, in traditional warehousing solutions, these two are closely linked making it very difficult to individually evaluate the maintenance costs of each.  

• All data types can be stored cost-effectively in their native formats in the S3 data lake. For instance, Amazon Elastic Compute Cloud (EC2) can be used to launch virtual servers with data processing by the AWS analytics tools.

• It is also possible to calculate the ratios of memory and bandwidth precisely to improve S3 data lake performance by using the EC2 instance.

• The S3 data lake can take up data processing and query with Amazon Redshift Spectrum, Amazon Athena, AWS Glue, and Amazon Rekognition. S3 also ensures serverless computing facilitating codes to be run without provisioning and managing servers. S3 does not charge any flat or one-time fees and users have to pay only for the quantum of computing and storage resources used.

• S3 data lake can be built in a multi-tenant business ecosystem by bringing your data analytics tools to a common data set thereby improving the quality of data governance and lowering maintenance costs. This is distinct from older systems where multiple data copies had to be circulated across multiple data platforms. 

These capabilities make Amazon S3 data lake the preferred option for data lake requirements.