Why is Big Data on AWS Gaining Popularity amongst Businesses?

Big data has become more important because of its use case in most of the professional fields and the results it brings to the table. Big data, in general, is referred to the huge amount of information that has been gathered and further analyzed to gain intel on it. It largely emphasizes on proper utilization of a collection of data rather than the amount of data that has collected. The collected information can be both structured or unstructured data, mostly containing day to day business information. The analyzed data can be used in many ways such as to create personalized medicine on the basis of patient information, analyze transactions on a daily basis, and many more.

Amazon Web Services provides a wide variety of services that can be useful for big data analysis and much more. It takes upon the difficulty of provisioning, availability, durability, recovery, and backing up of the information to make the process simpler. AWS simplifies the complex process of collecting the data, storing it, analyzing and further visualizing it for reference. The process of collecting data in AWS from an on-premise application is done with the use of AWS Storage Gateway. Along with that AWS direct connect is used to establish a dedicated connection between an on-premise system and AWS. Similarly, AWS snowball is used to physically migrate huge amounts of data to AWS. On the other hand, Amazon Kinesis Data Firehose and Amazon Kinesis Video Streams are used for capturing real-time streaming data.

AWS S3 (Simple Storage Service) is used to store the collected data and is designed to be highly scalable and durable at the same time. The stored data can be further used for machine learning, artificial intelligence, and big data analysis. AWS S3 can be used as a data lake on AWS that can be further analyzed and processed accordingly. Furthermore, AWS glue is a service that makes the data in S3 available when it is required. It can extract the data, transform and then load it for further processing, at the same time making it a queryable data lake.

Amazon Athena is a serverless infrastructure that can be used to analyze the data in Amazon S3 with the help of SQL queries. It is done by defining a standard schema in the database and thereby running the required queries. The payment method of Amazon Athena is done on a per-query basis. Amazon EMR is used for processing data in a large amount that is available at a data lake. It makes the process of data analysis simpler and cost-efficient at the same time, it supports multiple open source projects such as Hadoop, presto, Spark, etc. In the EMR, every project is updated after 30 days of a release so that the results are up to date without any extra efforts. Apart from those, Amazon Redshift, Amazon Kinesis, Amazon Elasticsearch Service, and Amazon QuickSight are AWS services that can be used for real-time analysis and for post-processing visualization.

Big data can be used in various ways on AWS such as an on-demand basis, clickstream analysis, event-driven, application, and data warehouse. Big Data can be analyzed on an on-demand basis where entire analytics applications are build to power users’ business. A “Hadoop Cluster” can be scaled from zero to thousands of servers within a minute instead of hours or days; it can also be turned off easily when you’re done. This means you can process big data workloads in less cost and time. The input data is collected from various locations and uploaded in AWS S3 as a result of creating a data lake for the analyzer to feed on. Then Amazon EMR is used to arrange and collect all of the data stored which is further uploaded to Amazon redshift. Further, this data in Amazon redshift is used in the personalized business application for any organization. In the case of clickstream analysis, the data is sent to Kinesis Stream that stores the data. This stored data can be used in a custom-built application that can process the same and show the results to the end-user who has been using their streaming service. Generally, in order to get simply started with AWS, you can make a Hadoop cluster with the help of Amazon EMR and run a custom-built application.