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 and analyzing Big Data by utilizing a Hive (the SQL query layer available for Hadoop) connector. This connector allows data analysts to query Hadoop data using either SQL or HQL (Hive Query language), and then integrate that data with any other data source to build exactly the dataset needed for analysis.
Alteryx also provides direct file system support, with both read and write capabilities to the Hadoop File System (HDFS), and support for Impala. Alteryx, Databricks, and Cloudera, have announced that they will become the primary committers to SparkR, a subset of the overall Spark framework. Alteryx and Databricks accelerating the adoption of SparkR and SparkSQL, in order to help analysts get greater value from Spark as the leading open-source in-memory engine.
Key Alteryx capabilities for Hadoop Analytics: