Amazon S3 (Simple Storage Service) is an optimized data storage service based in the Cloud where data in its native form – unstructured, semi-structured, or structured data can be stored. Data, regardless of the volume, can be stored in a fully safe environment with data durability at a high of 99.999999999 (11 9s)
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What is Amazon S3 and the concept of an S3 data lake?
In Amazon S3, data is stored in buckets with files containing metadata and objects. For uploading a file or metadata that has to be stored in a bucket, you have to upload an object to Amazon S3. After this step is completed, permissions can be set on the object or the related metadata that are stored in the containers (buckets) for holding the objects. Access to the buckets can be restricted to selected personnel only who in turn can access logs and objects to decide where they will be stored on Amazon S3.
When an S3 data lake is built, several competencies may be used. These include Machine Learning (ML), Artificial Intelligence (AI), big data analytics, media data processing applications, and high-performance computing (HPC). All these together will help you get vital and incisive business intelligence and analytics from unstructured data sets and the b
Large volumes of media workloads can be processed with Amazon FSx for Luster from the S3 data lakethrough file systems for HPC and ML applications. The S3 data lakecan also be used for specific analytics like ML, AI, and HPC applications from the Amazon Partner Network (APN).
It is for all these reasons and the capabilities offered by the S3 data lakethat large business entities like Expedia, Airbnb, GE, FINRA, and Netflix have made this storage platform their preferred option for a data lake.
What are the leading advantages of the Amazon S3 data lake?
There are several advanced and cutting-edge features of the Amazon S3 data lake.
- Traditional data warehousing systems had computing and storage facilities that were so closely interlinked that it was almost impossible to understand and optimize the costs of data processing and infrastructure maintenance. On the other hand, the S3 data lakehas separate silos for computing and storage and you can store all data types cost-effectively in their native formats.
Virtual servers can be launched with the Amazon Elastic Cloud Compute (EC2) while data processing can be done with the analytics tool of Amazon Web Service (AWS). An EC2 instance can be used also to optimize the precise ratios to be allocated for bandwidth, memory, and CPU to improve the performance of the S3 data lake.
- S3 data lakeoffers data processing, querying, and implementation across serverless and non-cluster AWS platforms such as Amazon Athena, Amazon Rekognition, Amazon Redshift Spectrum, and AWS Glue. Users also get the services of Amazon S3 for serverless computing where they can run codes without the need for managing or provisioning servers. You only have to pay for the computing and storage resources used without a flat one-time fee or recurring charges.
- With the centralized data architecture of Amazon S3, a multi-tenant environment can be seamlessly built to bring your data analytics tools to a common data set. This is a huge improvement over traditional systems and their quality of data governance and costs where data copies had to be circulated across multiple data processing platforms.
- The APIs of the Amazon S3 data lakeare supported by several third-party vendors and are very user-friendly with the most common being Apache Hadoop and other analytics tools suppliers. Users can therefore use the tool they are very comfortable with on Amazon S3 data lake.
These advanced features and cutting-edge capabilities make Amazon S3 data lakethe most-used service for the modern business environment.
What are the AWS services to be used across the Amazon S3 data lake?
Large numbers of AWS analytics applications, AI/ML services, and high-performing file systems can be accessed by users of the S3 data lake. Hence, it is possible to run unlimited workloads and intricate queries without the need for extra data processing capabilities or transfers to other data stores.
Some of the AWS services that can be used with the S3 data lakeare as follows:
- Creating a fully-secured data lake quickly in days only with the AWS Lake Formation. All that you have to do is decide where the data should be located and the policies to be applied for data access and security. AWS Lake Formation then combines the specified data collected from various sources and moves it to the Amazon S3 data lake.
- After the location of the data in an S3 data lakeis defined, it can be used in various diversified use cases from the analysis of petabyte-scale data sets to querying of metadata of a single object. All these can be done without resource and time-intensive ETL activities.
- With the S3 data lake, users can discover insights from the data sets in their native formats, analyze images and videos stored in S3, and create recommendation machines. These can be done with AWS services such as Amazon Rekognition, Amazon Personalize, Amazon Comprehend, and Amazon Forecast.
It is therefore seen that the S3 data lakehas complete infrastructure support from all ancillary Amazon Services.
Finally, a word of caution – though Amazon Redshift and Amazon S3 are often used interchangeably, there are a lot of differences between the two. Redshift is a data warehouse for structured data only while S3 ingests data in their native format in any form.