Amazon Simple Storage Service (S3) is an optimized data storage service used to build a data lake that can store unstructured, semi-structured, and structured data. With S3, data of any volume can be scaled easily in a fully safe environment with data durability of an incredible 99.999999999 (11 9s).
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The Key Concepts of Amazon S3
Before studying the S3 data lake and its various features and benefits, it is necessary to know some of the key concepts of this data storage service.
Data is stored in the form of buckets in the Amazon S3. A file contains an object and metadata. You have to upload a file or metadata that needs to be stored in a bucket by loading an object in Amazon S3. Once this step is done you can set permissions on the related metadata or an object.
Buckets are containers holding the objects. You can restrict access to them to only specific authorized persons who can access logs and objects and decide the location where the buckets and their contents will be stored on Amazon S3.
When you build an S3 data lake, there are several competencies that you can access like running artificial intelligence (AI), machine learning (ML) high-performance computing (HPC), big data analytics, and media data processing applications. All these help you to get vital business insights into unstructured data sets.
You can also initiate file systems for ML and HPC applications as well as process large volumes of media workloads with Amazon FSx for Luster from the S3 data lake. You also have the flexibility to use specific analytics, HPC, ML, and AI applications from the Amazon Partner Network (APN) through the S3 data lake.
Amazon S3 is today hugely popular among business entities and tens of thousands of data lakes are used by some of the leading brands like Airbnb, Expedia, Netflix, GE, and FINRA.
Amazon Redshift and Amazon S3
There is a distinct difference between Amazon Redshift and Amazon S3 even though these two are treated as the same in conversational pieces. While Amazon Redshift is a data warehouse, Amazon S3 is an object storage service and many enterprises run them simultaneously.
The main difference between the two is how structured and unstructured data are treated. Amazon Redshift is a data warehouse and hence accepts only structured data. It is specifically meant for SQL-based clients and for use by business intelligence tools that use the standard ODBC and JBDC connectivity.
On the other hand, Amazon S3 accepts data of any size and structure and its purpose need not be mentioned upfront. Hence on S3, there is a lot of scope for data exploration and discovery, leading to more data analytic scenarios.
Main Features of the Amazon S3 Data Lake
Some of the more important features of the S3 data lake are as follows.
- The S3 data lake has separate storage and computing facilities as distinct from traditional warehousing solutions where the two were very closely linked. The advantage of the modern S3 data lake is that all data types can be stored cost-effectively in their native formats. For example, Amazon Elastic Compute Cloud (EC2) is used to launch virtual servers with data processing by the AWS analytics tools. You can also optimize the precise ratios of memory and bandwidth to improve S3 data lake performance by using the EC2 instance.
- Implementation on non-cluster and serverless AWS platforms is easy on S3 data lake as it can do data processing and query with Amazon Redshift Spectrum, Amazon Athena, AWS Glue, and Amazon Rekognition. S3 also enables serverless computing, allowing codes to be run without provisioning and managing servers. You have to pay only for the resources used for computing and storage and no flat or one-time fee is charged by S3.
- Because of the centralized environment, you can build an S3 data lake in a multi-tenant business ecosystem by bringing your data analytics tools to a common data set. The quality of data governance is therefore improved and costs lowered as compared to older systems where multiple data copies had to be circulated across multiple data platforms.
- S3 data lake APIs are uniform and standardized and are supported by several third-party software vendors like Apache Hadoop and other analytics tools suppliers.
These advanced features and cutting-edge capabilities make Amazon S3 data lake the much-preferred option of businesses for their data lake requirements.
S3 data lake for accessing AWS Services
An S3 data lake provides access to a host of file systems such as AWS analytics applications and AI/ML services. Hence, it is easy to execute multiple queries and run unlimited workloads simultaneously across the S3 data lake without requiring more data processing facilities and storage resources from other data stores.
The following are the AWS Services that can be used with the S3 data lake.
The first is AWS applications without data movement. Once the data is located in the S3 data lake, analyzing petabyte-sized data sets and querying of metadata of a single object with a lot of ETL activities is possible.
Next, AWS lake formation and an optimized S3 data lake can be quickly created once it is decided where the data should reside and what data access and security policies to follow.
Finally, machine learning activities can be launched with Amazon Comprehend, Amazon Forecast, Amazon Personalize, and Amazon Rekognition to get new insights from data stored in the Amazon S3 data lake.
This is how Amazon S3 data lake has helped businesses to maximize their operating efficiencies.
