Oracle Extends Reach of Big Data Warehouse

Mike Vizard
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Big Data: Not Just for Big Business Anymore

The rise of Big Data presents IT organizations with a broad swath of management challenges that all can be traced back to the need to create a next-generation data warehouse to store all that data in a way that allows it to bring value to the business.

In the case of Oracle, that means deploying Big Data appliances that can run either Hadoop, preferably based on a distribution from Cloudera or the Oracle NoSQL database. Oracle has now extended that platform approach to Big Data to include an Oracle Big Data Appliance X4-2 offering that makes use of the latest generation of Intel Xeon processors and 4TB magnetic disk drives.

According to George Lumpkin, vice president product management for Oracle data, Oracle views Hadoop and other forms of NoSQL as a natural extension of the data warehouse. Those data warehouses, notes Lumpkin, already contain customer information in a structured format that often stretches back across multiple decades. Hadoop and other technologies now allow those data warehouses to be extended to include all types of unstructured data, including clickstreams and all manner of machine data.

The goal, says Lumpkin, should not be to throw out all those existing investments, but rather extend them. As such, Oracle now offers a wide range of options in support of multiple types of Big Data formats. Together, Lumpkin says these technologies provide the foundation for the next-generation of data warehousing technology in the enterprise.

To put a finer point on that statement Oracle, also announced that along with Cloudera it has become a co-founder of Sentry, a project to deliver fine-grained authorization to data stored in Apache Hadoop that is similar to what enterprise IT organizations apply when deploying relational databases.

Data management has always been Oracle’s strongest suit. The challenge now is figuring out what percentage of all the data an organization collects can be housed in Big Data lakes using technologies that are a lot less expensive than the traditional relational database. That doesn’t mean the relational database disappears from the data warehouse, but it does mean that going forward, IT organizations must balance the strengths and weaknesses of one platform versus another as they look to combine various data elements to present the organization with the right mix of data at the right time.

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