Amazon S3 (Simple Storage Service) is based in the cloud and is an ideal platform to store data in its native format – unstructured, semi-structured, or structured – and build a data lake. You can scale a data lake regardless of its size in an environment that is fully safe and secure, highly cost-effective, and with data durability at an amazing 11 9s (99.999999999).
Primary Concepts of the Amazon Simple Storage Service
It is necessary to understand the basic concept and functioning of Amazon S3 before going to the features of the S3 data lake.
Data is stored in buckets on this cloud storage platform. Each file in it contains an object and metadata. These are stored in a bucket by loading an object to S3. Once this step is completed, you can set permissions on that object or the metadata.
Only personnel who are authorized can get access to the buckets that hold the objects. They are allowed to verify logs and objects and decide where the buckets and the objects will be placed in the Amazon S3 repository.
You get access to several competencies when building an S3 data lake all of which will help you to get detailed and critical insights into the data sets of your organization. These include artificial intelligence (AI), machine learning (ML), big data analytics, high-performing computing (HPC), and media data processing applications. You can, therefore, quickly and seamlessly initiate files for ML and HPC applications and process media workloads of large volumes from the S3 data lake with Amazon FSx for Luster.
Users also have the options and the flexibility to use APN (Amazon Partner Network) through the S3 data lake for AI, ML, and HPC applications. It is because of these optimized capabilities that S3 data lake is the preferred platform of major enterprises around the world like FINRA, Expedia, Netflix, GE, and Airbnb to name a few leading ones.
Cutting-edge Features of the Amazon S3 data lake
The main features of the Amazon S3 data lake are as follows.
- S3 data lakehas separate computing and storage repositories which is a great help for modern-day data-driven organizations. In traditional systems, the two were closely linked, making it very difficult to accurately estimate the costs of data processing and infrastructure maintenance. In contrast to this setup, the S3 data lake allows data storage in respective native formats cost-effectively.
This is possible as virtual servers can be launched with data processing being taken care of by AWS analytics tools like the Amazon Elastic Compute Cloud (EC2). This EC2 instance can also be used to optimize the ratios of memory, CPU, and bandwidth to improve the functioning of the S3 data lake.
- S3 data lakehas a centralized data structure that makes it very easy for Amazon S3 to build a multi-tenant ecosystem that helps to bring your data analytics tools to a common data set. This ensures that costs are reduced when compared to old systems where multiple copies of data had to be circulated across various data processing platforms.
- S3 data lakecan be deployed across non-cluster and serverless AWS platforms as querying and data processing can be done with AWS Glue, Amazon Athena, Amazon Rekognition, and Amazon Redshift Spectrum.Further, because Amazon S3 provides serverless computing, codes can be run without provisioning and having to manage servers. You have to pay only for the computing and the storage resources used without any flat one-time fees or upfront charges.
- S3 data lakeis very user-friendly as it is supported by a host of third-party software vendors because of its uniform APIs. The most common and popular vendor on the list is Apache Hadoop. The advantage here is that you can carry out data analytics and processing on Amazon S3 with the tool that you are most comfortable with.
All these features are exclusive to this cloud storage repository, making Amazon S3 data lake the mostpreferred service in the current business environment.
AWS services used with Amazon S3 data lake
When you are on Amazon S3 data lake, you get access to several high-performing systems, AWS analytics applications, AI/ML services, and more. The main benefit here is that multiple users can run multiple intricate queries and unlimited workloads simultaneously without any drop in performance or having to draw additional data processing facilities or storage resources from other data stores.
Some of the AWS services that complement S3 data lakeare as follows.
- AWS applications that do not require any data movement. Extensive ETL activities are not required when analyzing petabyte-sized data sets and metadata querying of a single object, once the data is located in the S3 data lake.
- A maximized S3 data lakecan be quickly and seamlessly created with AWS Lake Formation after specifying the location of the data and the policies to be followed regarding data access and security.
- You can launch machine learning jobs and use Amazon Comprehend, Amazon Forecast, Amazon Personalize, and Amazon Rekognition to discover and explore data analytics from structured datasets in the S3 data lake.
Data ingestion is generally the most challenging task for IT teams when using Big Data Analytics on S3 data lake. Data administrators often have to manage ingestion for thousands of sources, many of which require individual agents and coding, to make sure of a data pipeline that is full of analytics-ready data. Hence, enterprises need a Big Data Tool to ease data ingestion and speed it up so that the full potential of S3 data lakecan be realized without putting pressure on IT teams.