


A self-service tool, Datameer-Analytics is designed primarily for non-technical users and decision makers to leverage a Hadoop cluster for their reporting and analytics needs, without having to rely on IT. Datameer-Analytics makes a big data implementation exceedingly easy as it can efficiently simplify the complexity of gleaning insights from the data, which requires multiple people knowing different technologies working as a single unit. What makes Datameer-Analytics one of a kind is its ease of use. Customers can install the solution, which wraps itself around their big data cluster to create a simple handle, enabling them to manipulate the data directly through the solution instead of modifying their complex big data framework. For instance, in case of reporting changes, which requires 3-4 weeks’ time for IT personnel, Datameer- Analytics follows a no ETL or schema-on-read approach to enable instantaneous changes to reports without relying on technical personnel to make changes to the underlying code or database. As Joe McKinney, the CIO of Infonex Technologies puts it— “Our goal is to help clients maximize the value of analytics investment by enabling anyone in their organization to unlock insights through big data visualization—not just the data scientists.”
Bringing Clarity to Data Lake
Datameer-Analytics comprises efficient data curation modules that cleanse the data thoroughly from the data lake, allowing customers to access clean and better-quality data.
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Our smart data analytics platform can transform mounds of complex data from any data source into actionable insights
The solution also comes with 40+ pre-built connectors databases, leveraging which customers can seamlessly extract or ingest data from multiple disparate sources, saving significant time. Further, the solution is future-proof with built-in machine learning (ML) and artificial intelligence (AI) components. Datameer-Analytics can also be integrated with other self-service analytics and visualization tools like Tableau, Qlik, and many more. Most importantly, one can avail all the aforementioned benefits of Datameer-Analytics at just one-third of the cost of other big data solutions in the market.
In an implementation highlight, Infonex assisted a genomic research center in obtaining valuable insights from the massive volume of genomic data at their disposal. Overwhelmed with several petabytes of data, the client was performing analytics on samples of data, which often led to missing out on other significant data in the datasets. Moreover, it took them 28 hours to process the data samples for just one report. With Datameer- Analytics, Infonex reduced the processing time from 28 hours to 40 minutes and also ensured that every single byte of data was analyzed to derive valuable and actionable insights.
Proven Custom Data Solutions Provider
Infonex has also been a trusted data solutions provider to several Fortune 500 companies and mid-sized companies. The company offers a wide spectrum of custom data solutions— data migration, data modeling, data mining, data extraction, data curation, data analytics, etc. Data is the foundation of sound decision making and organizations have hundreds of thousands of data points available to them. We have helped transform mountains of such data to meaningful insights using various custom solutions catered to specific needs of each customer, says McKinney. The company takes pride in recently having successfully completed a complex project that involved migrating legacy Epic Clarity data to an Oracle Data Warehouse.
Infonex has carved a niche in the Big Data industry with the success of Datameer-Analytics. The company will also continue to invest in its employees and maintain a competent workforce. “Our Big data expertise coupled with our AI and ML capabilities will propel us forward,” concludes Ebrahim.
Company
Infonex Technologies
Headquarters
Santa Clara, CA
Management
Afzal Ebrahim, CEO
and Joe McKinney, CIO
Description
Offers Datameer-Analytics—an ingenious end-to-end big data solution— purpose-built to abstract the complexity of Hadoop
