Apache Hadoop is the leading Open Source framework scalable for processing huge datasets in distributed systems. It enables users to store and process huge volumes of data and analyzes unstructured and complex data. 

Hadoop is designed to manipulate, store, manage and analyze Big Data. Hadoop™ MapReduce coupled with HDFS (Hadoop Distributed File System) enables storing large volumes of data. Hadoop also addresses highly variable information formats, data velocity and data variance.

Leverage Hadoop for the Enterprise



Our Hadoop consultants solve enterprise's data management challenges - whether using Hadoop as a data hub, data warehouse, staging environment or analytic sandbox.

Hadoop Design & Development

Our Big Data Practice team has expertise in Hadoop Ecosystem like HBase, Pig, Flume, Hive, Sqoop, Oozie, and Zookeeper to deliver scalable Apache Hadoop based solutions.


We develop Hadoop based solutions that can integrate with enterprise applications like Liferay, Drupal, Talend, Alfresco, CRM, ERP, Marketing Automation and more.

Hadoop Support and Maintenance

Leverage our 24x7 support service and Cloudera’s Distribution (including Apache Hadoop) partnership benefits to keep your Hadoop deployment running.

Our Hadoop Implementation Examples

Collection & analysis of structured and unstructured data to improve customer engagement

An integrated data warehousing platform with Talend (ETL), Hadoop and IBM Cognos facilitating customer targeting, lead generation, campaign performance, customer profiling, site performance, and intelligent content recommendation. Key features include:

  • Detailed data discovery to ensure that the data sourced is meaningful & adds value
  • Talend ETL integration for flexibility & agility
  • Definition/Execution of roadmap to ensure success of the data warehouse including data validation

360-degree view into employee internet data plan usage patterns

The Hadoop based Log Processing & Analysis solution built using Apache Flume– distributed system for aggregating streaming data, HDFS – Primary Hadoop Storage system, MapReduce – Parallel storage to process large amount of data in parallel, Sqoop – Efficient transfer of huge data between Hadoop & structured data stores, Pentaho – Open Source data integration tool to aggregate and manage large unstructured employee's internet usage patterns logs. Key benefits of the solution include:

  • Optimum bandwidth utilization with faster response time.
  • Rich user interface with accessibility through mobile devices and tablets
  • Cost advantage through non dependence on high end storage networks

Analyzing call data records for a Telecom company with Dashboards on usage of services.

An application that process ~500GB of data every hour with ~5 node Hadoop Cluster, Multi node InfiniDB cluster holding ~250GB of aggregated data, and UI queries with responsiveness between 10-15 secs. The processed data fed in to a dashboard to analyze usage.The objective is to optimize network bandwidth management & policy configuration. Key statistics of Hadoop based Big Data Analytics platform includes:

Source emits 250,000 records/sec, 900M records/hour

  • Each record ~500 bytes
  • Raw data of ~3TB retained in the Hadoop cluster for 6 hours
  • ~10TB of data maintained in the cluster

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