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Jul 26 2014

Oracle Big Data SQL: Diverse Data Sources – One Management System

As more and more companies base their business decisions on Big Data analysis, new problems arise in the data center. One of the key issues IT departments need to solve is integrating data from numerous sources – including Hadoop, relational, and NoSQL databases – into one big data management system. Oracle's recently released Big Data SQL software package is supposed to serve as a core element in such a system.

According to Oracle, the trouble with existing solutions such as Hadoop and NoSQL is that while they address specific big data problems, each of their database structures can administrate only certain data types. As a result, users are forced to run various isolated data silos, which in turn prohibits the comprehensive access and analysis needed to generate mission-critical insights. Until now, the only way to overcome these obstacles was to copy and move data between platforms and then either analyze them with MapReduce-powered tools or to construct individual queries for each platform and stitch together the respective outcomes – hardly an option for managers that boast their ability to make real-time or near-real-time decisions.

Oracle tackles this challenge with Big Data SQL, a software package that extends its big data portfolio and is scheduled for release in fall 2014. The basic concept behind it: use SQL to run queries across all data stores in an enterprise, regardless of their underlying architecture. If the database giant is to be believed, this means IT departments can now solve several problems at once. Number one, the software breaks down the walls between the data stores and gathers relevant information from all sources within the enterprise following more or less complex inquiries, thus helping to avoid the intricate and time-consuming process described above. Number two, enterprises that want to capitalize on Big Data can tone down their sometimes hectic search for programmers with Hadoop or NoSQL experience and further rely on their SQL-skilled staff instead. And number three, potential customers will be able to save time and money.

To achieve this, Big Data SQL relies on two high-end technologies first introduced with Oracle's Exadata machines, expanded external tables and Smart Scan on Hadoop:

  • Using new external table types, data in Hadoop and NoSQL is exposed to users of Oracle database products. These tables, once defined, automatically discover Hive metadata including data location and data parsing requirements. This enables SQL queries to access the data in its existing format leveraging native parsing constructs.
  • Oracle's unique Smart Scan capability brings the proven storage processing innovations of Oracle Exadata to Big Data environments. Here, performance losses are typically the result of excess data movement. Instead of sending all scanned data to the compute resources, Smart Scan on Hadoop radically minimizes data movement to the compute nodes by applying data-local scans, column projection, predicate evaluation, and complex function evaluation at the storage level.

With Big Data SQL, Oracle hopes to satisfy the needs of heavily data-reliant enterprises which, by its own account, are reverting to the 'classic' database language to comb their files for actionable info. This sounds like a good idea indeed; however, for now one small problem remains: in the first edition, Oracle Big Data SQL will only run on the Oracle Big Data Appliance and yield the best results in conjunction with the aforementioned Exadata servers. And these hardware purchases take away some of the expected cost advantage.

 
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