Organizations and small businesses around the world today prefer Amazon Simple Storage Service (S3) to build data lakes as it is a high-performing storage service that is optimized for both unstructured and structured data. A data lake can be built cost-effectively and quickly scaled to any required size.
Further, the Amazon S3 data lake functions in a highly-secured environment with data durability at an amazing 11 9s (99.999999999). Being based in the cloud, Amazon S3 offers advanced availability and performance for storing and retrieving data at any time from any location in unlimited volumes.
The Basic Concepts of Amazon S3
Before going into the intricacies of the S3 data lake, it is necessary to understand the functioning and the basic concepts of Amazon S3.
In this cloud storage platform, data is stored in buckets with each file comprising an object and metadata. This file or metadata can be stored in a bucket by loading an object to S3. After this is done, it is possible to set permissions on that metadata or the object.
Access to the buckets that hold the objects can be restricted to authorized personnel. Only they can check logs and objects and take decisions on where the buckets and their contents will be located in the Amazon S3 storage repository.
When an S3 data lake is built, users can access several competencies. These include big data analytics, high-performing computing (HPC), machine learning (ML), artificial intelligence (AI), and media data processing applications, all of which help organizations get critical insights into unstructured data sets.
Users can easily start files for HPC and ML applications and process huge volumes of media workloads from S3 data lake with Amazon FSx for Luster. There is also the flexibility to use the Amazon Partner Network (APN) for HPC, ML, and AI applications through the S3 data lake.
Because of its cutting-edge features, Amazon S3 is very popular among leading business entities around the world like Expedia, Airbnb, GE, FINRA, and Netflix for building data lakes.
Amazon S3 and Amazon Redshift
Amazon S3 and Amazon Redshift are often denoted in the same frame though there is a sharp difference between the two. Hence, before proceeding with S3 data lake, it is relevant to clear the air about them.
Amazon S3 is an object storage system while Amazon Redshift is a data warehouse. It is not unusual to find organizations simultaneously running both of them.
Amazon S3 can ingest data of any structure or size and the reason for the data need not be stated upfront. Therefore, there is ample opportunity for key data discovery and exploration, leading to more analytics possibilities. Amazon Redshift, on the other hand, being a data warehouse, can only ingest structured data. It has been primarily created for business intelligence tools and those who use the standard JDBC and ODBC connections.
The Main Features of Amazon S3 Data Lake
There are several cutting-edge and advanced features of the S3 data lake.
- Operable across non-cluster and Serverless AWS platforms: Data processing and querying on S3 data lake can be done with Amazon Athena, Amazon Rekognition, Amazon Redshift Spectrum, and AWS Glue. Users are offered serverless computing, where codes can be run without provisioning and managing servers. Payment for these services is only to the extent of storage and computing resources used without any one-time flat fee or upfront charges.
- Separate storage and computing facilities: In traditional data storage systems, computing and storage systems were so intricately linked that it was very difficult to maximize the costs of data infrastructure maintenance. S3 data lake is a quantum improvement as all data types can be stored at affordable rates in their native formats. AWS analytics tools areused to process data when Amazon Elastic Compute Cloud (EC2) launchès virtual servers. Further S3 data lakeperformance can be improved with an EC2 instance to optimize the ideal ratios of bandwidth, memory, and CPU.
- Unified data architecture: Amazon S3 is used for building a multi-tenant ecosystem to bring data analytics tools to a common data set. It leads to an improvement in data governance quality and costs in comparison to earlier systems where multiple data copies had to be circulated across various processing platforms.
- Uniform APIs: The APIs of Amazon S3 data lake is user-friendly and supported by multiple third-party vendors like Apache Hadoop and other analytics tools suppliers. Users get to work with the tool of choice to carry out data analytics on Amazon S3.
Because of these friendly and versatile features, S3 data lake is the preferred choice of most businesses today.
Access to AWS Services across S3 Data Lake
Those using the S3 data lake have access to a wide range of high-performing file systems, AWS analytics applications, and AI or ML services. Thus, unlimited workloads and intricate queries can be executed across the S3 data lake without additional data processing capabilities or transferring to other data stores.
The AWS services that can be used with the S3 data lake are as follows.
- AWS Lake Formation: With this AWS service, a data lake can be created within days as all that is required is to define the location of the data and the access and security policies to be followed. Lake Formation auto collates this data and moves it to the S3 data lake.
- Machine learning: Discover insights from structured datasets and analyze images and videos stored in S3 with AWS services like Amazon Forecast, Amazon Personalize, and Amazon Comprehend.
Amazon S3 data lake helps businesses run IT infrastructure seamlessly and cost-effectively.