3 Keys to Improving Your Analytic Results with Hadoop
Apache Hadoop is at the core of the Big Data revolution. Vendors such as Cloudera, MapR, and Hortonworks have taken this open source software that enables distributed parallel processing of huge amounts of data, and included important data management and support capabilities. However, many Hadoop analytic solutions are designed for a limited number of specialists within organizations, or just focus on Hadoop as a data source.
Alteryx provides a unique approach to accessing, cleansing and prepping Big Data with a simple, intuitive, drag and drop, tool-based interface that doesn’t require an analyst to write SQL or HQL (Hive Query language.) Alteryx enables analysts of all skill levels the ability to read data directly from Hadoop, enrich the data, and integrate it with any other data source. Once the exact dataset needed for analysis is completed, Alteryx enables analysts to perform deep analytics, write that data back to the Hadoop platform and perform all of this within the same GUI. Self-service analytics is the core of what Alteryx is about, and that includes redefining Big Data platform such as Hadoop from specialized platforms requiring specialized skills, into everyday tool for line-of-business analysts.
Key Alteryx Capabilities for Hadoop Analytics: