Big Data, IT Expert SystemBig Data and Hadoop

Analyzing large data sets will become a key basis of competition. ITEXPS helps companies bring together data from any source and manage it for business value.

Book an appointment Call 847-350-9034

Big DataHadoopData integration

What is Hadoop?

It is approximated that 80% of all data that exists is unstructured, meaning it is raw and unorganized. Hadoop has become a popular choice to process, organize and store huge volumes of semi-structured and unstructured data, making it suitable for data mining and business analytics.Hadoop is not a single product but a collection of applications that make up a software framework designed to run distributed applications across clusters of commodity server hardware. It breaks big programming tasks into smaller jobs (mapping) spread across several computers, then consolidates the results into a single result set (reducing).

ITEXPS focus

ITEXPS Software automates Hadoop installation and management for the enterprise.

Four areas of need

Security, high availability, performance and manageability for the business enterprise.Contact us →

Independent management layer

Not a Hadoop distribution; it sits on top of any distribution to simplify installation and cluster management.Contact us →

Big Data training

See the Big Data Hadoop training module and other programs. Big Data Hadoop module All training programs Education grant program

Want to know more about Big Data?

For more information, call 847-350-9034 or book an appointment with ITEXPS.Book an appointment Call 847-350-9034 Education grant program

What is Hadoop?

It is approximated that 80% of all data that exists is unstructured, meaning it is raw and unorganized. Hadoop has become a popular choice to process, organize and store huge volumes of semi-structured and unstructured data, making it suitable for data mining and business analytics.Hadoop is not a single product but a collection of applications that make up a software framework designed to run distributed applications across clusters of commodity server hardware. It breaks big programming tasks into smaller jobs (mapping) spread across several computers, then consolidates the results into a single result set (reducing).