Suppose you are looking for the most optimized object storage service ideal for unstructured, semi-structured, and structured data and storage services to build a data lake. In that case, Amazon S3 (Simple Storage Service) is the platform for you. With S3 you can scale a data lake regardless of its size in a fully safe environment where data durability is 99.999999999 (11 9s). It is also preferred for its cost-effective capabilities.

When you build a data lake on Amazon S3, that is S3 data lake, you get access to a host of competencies. These include running artificial intelligence (AI), machine learning (ML), big data analytics, high-performance computing (HPC), and media data processing applications that will enable you to gain critical business insights into unstructured data sets. Further, with Amazon FSx for Lustre, it is possible to launch file systems for ML and HPC applications and process large volumes of media workloads directly from the S3 data lake.



The S3 data lake also allows you to use your selected and preferred analytics, HPC, AI, and ML applications from APN (Amazon Partner Network). Since Amazon S3 supports several cutting-edge features, storage administrators, data scientists, and IT managers can manage objects at scale, audit activities across the S3 data lake, and strictly enforce access policies.

Today, Amazon S3 is the chosen option for tens of thousands of data lakes that are common household brands and names such as Airbnb, Expedia, Netflix, GE, and FINRA. These prominent business entities use the S3 data lake to discover incisive business insights and securely scale their operational needs.

Amazon S3 vs. Amazon Redshift

Here, it is necessary to distinguish between Amazon S3 and Amazon Redshift as both are generally talked about in the same breath even when there is a distinct difference. Amazon S3 is an object storage platform, whereas Amazon Redshift is a data warehouse, and organizations often run both simultaneously. The two are not a part of any “either-or” debate.

The main plank of Amazon S3 vs Redshift rests on permitting unstructured vs structured data. Since Redshift is a data warehouse, any data ingested must be structured. It is an ecosystem that is created for business intelligence tools and common SQL-based clients that use the standard ODBC and JDBC connections. Amazon S3, on the other hand, can ingest data of any size or structure without the need for the purpose of the data to be stated upfront. Hence, there is space for key data discovery and exploration that leads to more analytic opportunities.

Primary features of Amazon S3 data lake

Some of the main features of the Amazon S3 data lake can be summed up as follows.

  • Separate silos for data storage and computing: S3 data lake is a huge improvement over traditional warehousing solutions where computing and storage facilities were so intricately bonded that it was almost impossible to optimize data processing infrastructure and costs. Now, on the S3 data lake, you can store all data types in their native formats very cost-effectively. Amazon Elastic Compute Cloud (EC2) can be used to launch virtual servers with data being processed by the AWS analytics tools. An EC2 instance can also be used to maximize the ideal ratios of memory, bandwidth, and CPU to improve data lake performance.
  • Implementation across serverless and non-cluster AWS platforms: On S3 data lake, data processing and querying can be carried out with Amazon Redshift Spectrum, Amazon Athena, AWS Glue, and Amazon Rekognition. Amazon S3 also offers serverless computing that allows codes to be run without requiring provisioning and managing servers. As a user, you have to pay only for the storage and computing resources used without any-one time flat or upfront fee.
  • Centralized data architecture: It is very easy to use Amazon S3 for building a multi-tenant environment, enabling you to bring your data analytics tools to a common data set. This improves the quality of data governance and costs over the traditional systems where you had to circulate multiple data copies across many processing platforms.
  • Uniform APIs: Amazon S3 data lake APIs are very user-friendly and supported by multiple third-party software vendors. Common among them are Apache Hadoop and other analytics tools suppliers. You can thus use the tool you are comfortable with to perform data analytics on Amazon S3.
Because of these cutting-edge and advanced features, Amazon S3 data lake is a much-in-demand service in the modern-day business environment.

Access to AWS services with Amazon S3 data lake

The Amazon S3 data lake allows you to access several high-performing file systems, AI/ML services, and AWS analytics applications. You can, therefore, execute multiple intricate queries and run unlimited workloads across the S3 data lake without relying on additional storage resources and data processing facilities from other data stores.

A few AWS services that can be used with the S3 data lake are as follows.
  • AWS Lake Formation: After defining where the data resides and what policies to follow about data access and security, an optimized S3 data lake can be quickly created.
  • AWS applications without data movement: Once the data resides in the S3 data lake, use cases include analyzing petabyte-sized data sets and metadata querying of a single object without extensive ETL activities.
  • Launching machine learning jobs: You can use Amazon Comprehend, Amazon Forecast, Amazon Personalize, and Amazon Rekognition to discover insights from structured data stored in an S3 data lake.
This is how Amazon S3 data lake has helped businesses to maximize their operating efficiencies.