Gartner’s magic quadrant analysis for Business Intelligence and Analytics platforms have mainly three criteria to assess platforms which I think are spot-on. They focus on enable (data with minimal technical know-how), produce (efficiency of data analytics and reports building) and consume (from various platforms, etc.). The theme is clear as businesses do not have the patience to wait for large implementation projects or development cycles. They want to know key drivers, critical insights, hidden opportunities and decision-making data points on various devices right away.
With the emergence and popularity of Big Data platforms, businesses are able to gain insights from data that was useless and too large to process in past. Platforms like Hadoop and Spark enable organizations to process structured and unstructured data at amazing speed and scale to gain quick insights. But major drawback of Big Data platforms is that organizations need to rely on specialized resources to build custom process to analyze data sets for their unique business needs. In most cases people who are most frequent consumers of data like Business Analysts, Data Scientists, Testers, Managers, Executives and business users, are not technical enough to work with Big Data platform directly. Some organizations take route of training they staff on higher level Big Data scripting languages like PIG and Hive with minimal success. Today’s users are accustomed to rich user interface with drag & drop and widget like applications on mobile devices.
Oracle’s Big Data Discovery platform addresses this problem with a nice twist to it. With partnership with Cloudera and offerings like No-Sql In-Memory database, Oracle have made it clear that they continue to be innovative data company. With release of Big Data Discovery platform, they are bringing data “enable, produce and consume” to non-technical business focused people. Oracle BDD enables organizations to have Big Data experts focus on exponential challenges without having to worry about mundane tasks of loading and extracting data for business users. Also BDD non-technical staff to perform quick analysis, transformation, charting, graphing and reporting on Big Data through a web-based interface that works on any platform.
Business Case for Oracle Big Data Discovery – Allows Hadoop experts to focus on complex data processing rather than mundane data export tasks – Quick dashboards, charts, graphs and maps for decision making, communication and operations – Power of Big Data in hands of business analysts, QA, business users, data scientists and executives
– High-speed data transformations without programming – Blazing fast search and sampling from Big Data stored in Hadoop Cluster – Big Data is not always “Batch Data”. BDD uses pre-indexed queries and Spark transformations providing real-time analytics capabilities unlike batch processing methods like MapReduce. – Self-service data upload to Hadoop, Hive and BDD – Command Line Interface for advanced users Technology
Three major components make up Oracle BDD.
BDD Studio: BDD Studio is a web application that needs to be deployed on Weblogic server. Studio provides end user interface on any
browser for analysis of data in Hadoop cluster. Studio also comes fitted with HDFS and Hive client for advanced users to analyze data from command line interface.
DGraph & Gateway: DGraph engine is already famous for high-speed indexing and searching of unstructured data. DGraph automatically indexes Hive tables from Hadoop cluster using HCatalog. BDD implements a Gateway component to communicate with DGraph for fast access to indexed data.
Data Processing: Data processing components are deployed to all nodes participating in Hadoop cluster. Data processors enables BDD studio and DGraph to communicate with Hadoop cluster using HCatalog and Spark transformation requests.
Read the blog: Data Quality Powered by Big Data