Market research and client engagements show that big data plays a central role in helping enterprises reimagine their businesses.
Hadoop is fast becoming the platform of choice for big data analytics. For years, thousands of organizations have relied on Hadoop clusters and associated building blocks to build and run the data platforms to process peta bytes of data every day. Despite all the challenges and efforts needed to set up a Hadoop ecosystem within an organization, Hadoop-based data platforms have become an integral part of the data landscape.
In today’s context, Hadoop-based data platforms can be made many times easier to run and manage on the cloud. Cloud-based deployments allow users to spin up and scale Hadoop clusters in near real time and at less cost.
TCS partners with clients to run their big data system landscape on AWS cloud to achieve scale, enhance agility, innovate and launch new services faster.
AWS offers Amazon Elastic MapReduce (EMR), a Hadoop-based managed service that takes care of all the mundane tasks needed to spin up and run these clusters.
AWS EMR supports the entire software stack, which includes Apache Spark, HBase, HCatalog, Hive, Flink Presto, Ganglia, Oozie, Pig, MXNet and Sqoop. It greatly simplifies the setup of clusters as all these packages are automatically installed at the time of cluster creation. AWS EMR carries a customized version of Hive, which can connect to and query DynamoDB.
TCS has demonstrated delivery excellence in AWS EMR services and helped many clients to migrate from on-premise Hadoop to EMR. TCS has also developed many solutions, migration frameworks, tools and accelerators in moving from on-premise to AWS EMR.
TCS’ Data and Analytics Solutions on AWS would help to: