Amazon Simple Storage Service (S3) is today the universally preferred choice by organizations around the world to build a data lake because it is the most high-performing storage service for both unstructured and structured data.
You can cost-effectively build and scale a data lake of any size with data protected in a highly secure environment with an amazing 11 9s (99.999999999) of durability. S3 offers cutting-edge data availability, scalability, and performance that can be used to store and retrieve data at any time, from anywhere, in unlimited volumes.
The Primary Concepts of Amazon S3
Before going to the S3 data lake, it is necessary to understand some basic concepts of S3. Amazon S3 stores data in the form of objects in buckets. An object comprises a file or any metadata that defines the file. For storing an object in Amazon S3, you have to upload the file that you want to store in a bucket. By doing so, you can set permissions on the object or the related metadata.
Buckets are containers that hold the objects and you can limit access to them to only specified personnel, view access logs and objects, and choose the location where Amazon will hold the buckets and their content.
Why Have an Amazon S3 Data Lake?
There are several reasons why you should have a data lake built on S3.
- Capability to use native AWS services to run big data analytics, AI (Artificial Intelligence), metadata processing, and high-performance computing applications. These help give better insights into the unstructured data sets.
- Capability to launch file systems for ML and HPC applications, ensuring processing of large media workloads from the data lake directly.
- The options to use preferred analytics AI, HPC, and ML applications from the APN (Amazon Partner Network.
All these factors empower IT managers, data scientists, and storage administrators to strictly enforce policies of access, manage objects at scale, and audit activities across their S3 data lake. Amazon S3 hosts millions of data lakes for well-known brands enabling them to securely scale up or down with their needs and discover new business insights around the clock.
Key features of Amazon S3 data lake
Some of the key features of the AmazonS3 data lake are as follows.
- Separating storage from computing and data processing: In traditional data warehousing solutions like Hadoop, the storage and computing capabilities are tightly interlinked, making it very difficult to maximize costs and data processing infrastructure. On the other hand, you can store all data types cost-effectively in their native formats with an S3 data lake. Virtual servers can then be launched as per your need using Amazon Elastic Compute Cloud (EC2) and AWS analytics tools may be used to process your data. EC2 instances can also be optimized to get the ideal ratios of CPU, memory, and bandwidth for the best data lake performance.
- Centralized data architecture: A multi-tenant ecosystem can be easily built with Amazon S3 so that users can bring their own data analytics tools to a common set of data. This improves data governance and costs when compared with traditional solutions that required the circulation of multiple data copies across several processing platforms.
- Incorporation across non-cluster and serverless AWS services: Querying and data processing can be done with Amazon Athena, Amazon Redshift Spectrum, Amazon Rekognition, and AWS Glue. Amazon S3 also combines with serverless computing so that code can be run without provisioning or managing servers. Users have to pay only for the computing and the storage resources used and no flat fee or upfront charges have to be paid.
- Standardized APIs: Amazon S3 data lake REST APIs are simple to use and supported by many independent third-party software vendors like Apache Hadoop and analytics tool vendors. Customers can, therefore, bring the tools that they regularly use and are comfortable with to perform analytics on data in Amazon S3.
These are the benefits that make the Amazon S3 data lake, the preferred data lake choice of modern businesses.
Using AWS services across Amazon S3 data lake
Customers of the S3 data lake can access a large number of AWS analytics applications, high-performing file systems, and AI/ML services. Thus, you can run unlimited workloads and execute intricate queries across the S3 data lakewithout requiring additional data processing capabilities or transfers to other data stores.
These are some of the AWS services that can be used with the S3 data lake.
- Using AWS Lake Formation: A fully secured data lake can be created in days and not months with AWS Lake Formation. All that is required is to define where the data resides and what policies regarding data access and security have to be applied. Lake Formation then collates data from various sources and moves it to the Amazon S3 data lake.
- Running AWS applications without data movement: Once the data resides in an S3 data lake, it can be used for a range of use cases. This includes analyzing petabyte-scale data sets to metadata querying of a single object that can be done without resource and time-intensive ETL jobs.
- Launch machine learning jobs: You can discover insights from your structured datasets, create recommendation machines, and analyze images and videos stored in S3. It can be done with AWS AI services such as Amazon Comprehend, Amazon Forecast, Amazon Personalize, and Amazon Rekognition.
Amazon S3 data lake has enabled businesses to finetune their IT infrastructure at very affordable costs.