If you have to bank on an optimized storage platform to build a data lake with structured and unstructured data and a chosen storage service, Amazon Simple Storage Service (S3) should be your preferred option. With S3, you can build and scale a data lake regardless of its scale or size in a cost-effective and secure environment where data protection assured is 99.999999999 (11 9s) of durability.
But first, what is a data lake?
It is a centralized storehouse where data of any size, both structured and unstructured can be stored. The advantage here is that it is not required to process data and structure and format it first before storing it. This stored data can be utilized to run a wide range of applications and analytics including dashboards and visualizations, big data processing, real-ime analytics, and machine learning. In today’s business environment where massive amounts of data are generated and have to be stored, such attributes help to make informed, accurate, and critical operational decisions.
What are the main features of Data Lake?
First, any volume of data present in real-time can be imported with data lakes. Data collated from multiple sources can be shifted to a data lake in the original structure. Hence, a lot of time can be saved in the schema, defining data structures, and transformations.
Data lakes also facilitate the storage of both relational and non-relational data. The first is data collected from operational databases and active line of business while the second type is the data taken from mobile apps, social media, and IoT. The two can be differentiated when you are indexing, cataloging or crawling through the data.
Finally, data scientists, data developers, and business analysts can access tools and frameworks according to their requirements with data lakes. It also includes commercial offerings from data warehouse and business intelligence vendors as well as open-source frameworks like Presto and Apache. Data lakes also provide opportunities to run analytics at any time without having to shift data to a different system first.
Coming to Amazon S3 Data Lake, there are several benefits to using it as the main storage platform. It has infinite scalability and hence is ideal for a data lake. It is possible to increase storage capacities from petabytes of content with 11 9s levels of durability, paying only for the storage capacities utilized. Other features of S3 are a scalable performance, access control functionalities, and native encryptions.
Here are some of the reasons for using S3 Data Lake.
- A data lake built on Amazon S3 can be used by native AWS services to run Artificial Intelligence (AI), Machine Learning (ML), high-performance computing (HPC), big data analytics, and media data processing applications. All these combined offer valuable insights into unstructured data sets.
- The Data Lake has the flexibility to use a preferred analytic such as AI, ML, HPC applications from APN (Amazon Partner Network).
- Large volumes of media workloads can be processed directly from the data lake and file systems for HPC and ML applications launched for ML and HPC applications with Amazon FSx for Lustre.
- Since Amazon S3 Data Lake supports a wide range of features, storage administrators, IT managers, and data scientists can enforce policies, manage objects at scale, and audit activities across data lakes.
Because of these factors, there are many advantages of using Amazon S3 to build a data lake. Here are a few of them.
- Any business owner will always give top priority to data durability and data safety. Amazon S3 ensures both these aspects. For durability, the 11 9s feature makes sure that if 10,000,000 objects are stored in S3, the probability of losing even a single object is one in 10,000 years. So far as data safety is concerned, any object uploaded on S3 across multiple systems is automatically copied, uploaded and stored, thereby preventing any form of failures, errors, and threats.
- Since Amazon S3 Data Lake automatically stores copies across a minimum of three AZs (Availability Zones) it insulates against the possibility of failure of an entire AWS Availability Zone. AZs are also located separately to provide fault tolerance. There is a wide range of data management features in S3 with broad flexibility to operate at an object level. This helps to manage scale, configure access, audit data across an S3 Data Lake, and optimize cost efficiencies.
- When you build a data lake with Amazon S3, you get data protection through an infrastructure that is best suited for the most data-sensitive organizations. You can take advantage of the non-requirement of elaborate procurement cycles to quickly scale up storage capacity. Applications can be run on AWS native services for analytics like HPS, ML, AI, and media data processing.
- It is easy to build a multi-tenant environment with Amazon S3 where many users can bring their own data analytics tools to a common set of data. This advances both cost and data governance and is an improvement over traditional solutions that require multiple copies of data to be distributed across multiple processing platforms.
- In conventional data warehouse solutions like Hadoop, compute and storage are tightly coupled, making it complex to optimize costs and data processing workflows. On the other hand, with Amazon S3 Data Lake, all data types can be stored cost-effectively in their native formats. Once this is done, as many or as few virtual servers as required can be launched using Amazon Elastic Cloud Compute (EC2) and AWS analytics tools can be used to process data. EC2 can be optimized to provide precise ratios of CPU, memory, and bandwidth for best performance.
- Amazon S3 can be used with Amazon Athena, Amazon Redshift Spectrum, Amazon Rekognition, and AWS Glue to query and process data. It also integrates with AWS Lambda server-less computing to run code without managing or provisioning servers. With all these capabilities, businesses need to pay for the actual amounts of data processed only or for computing times used.
- Finally, Amazon S3 RESTful APIs are simple and easy to use. They are supported by most major third-party independent software vendors like Apache Hadoop and other analytics tool vendors. This feature helps customers to make use of tools that they are conversant with and knowledgeable about and thereby perform analytics optimally on data in S3.
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