Exalytics in-memory analytics software and industry-changing hardware was engineered to work together to provide extremely fast solutions for BI, modeling, forecasting, and planning.
Your sales and marketing teams can identify new customers and new revenue opportunities, and you can lower your operational costs and reduce your overall risk with this preconfigured, pretested, preoptimized engineered system from Oracle. With Oracle Exalytics In-Memory Machine you get:
Oracle’s Big Data Appliance includes Intel’s new processors and the latest release of CDH (Cloudera’s Distribution, including Apache Hadoop) and Cloudera Manager, as well as Oracle Enterprise Manager Plug-In for Oracle Big Data Database Appliance. Oracle Big Data Connectors have been enhanced to enable greater database SQL access to Hadoop from Oracle Database.
“An influx of raw data sets is flooding every enterprise. However, before businesses can take advantage of the potential opportunity, they face a significant challenge in organizing these diverse datasources,” says Cetin Ozbutun, vice president of data warehousing and big data technologies at Oracle. “The latest updates further improve the abilities of our customers to optimize big data workloads and integrate them with their data warehouses to easily analyze all data throughout the enterprise.”
Big Data Database: appliance and connectors
Oracle Big Data Appliance is an open, multi-purpose engineered system for Hadoop and NoSQL processing. It runs a diverse set of workloads – from Hadoop-only workloads (MapReduce 2, Spark, Hive etc.) to interactive, all-encompassing interactive SQL queries using Oracle Big Data database SQL. Big Data Appliance provides an open environment for innovation while maintaining tight integration and enterprise-level support. Organizations can deploy external software to support new functionality – such as graph analytics, natural language processing and fraud detection. Support for non-Oracle components is delivered by their respective support channels and not by Oracle.
In addition to providing Oracle Big Data database SQL and the full Cloudera software platform, Big Data Appliance utilizes Oracle Big Data Connectors to simplify data integration and analytics. Big Data Connectors provide high-speed access to data in Hadoop from Oracle Exadata and Oracle Database – with data transfer rates on the order of 15 TB/hour. Big Data Connectors also enable integrated, highly scalable analytics – providing native access to Hadoop data and parallel processing using Oracle R Distribution. Finally, Oracle XQuery for Hadoop facilitates standard XQuery operations to process and transform documents in various formats (JSON, XML, Avro and others), executing in parallel across the Hadoop cluster.
Today the term big data draws a lot of attention, but behind the hype there’s a simple story. For decades, companies have been making business decisions based on transactional data stored in relational databases. Beyond that critical data, however, is a potential treasure trove of non-traditional, less structured data: weblogs, social media, email, sensors, and photographs that can be mined for useful information. Decreases in the cost of both storage and compute power have made it feasible to collect this data – which would have been thrown away only a few years ago. As a result, more and more companies are looking to include non-traditional yet potentially very valuable data with their traditional enterprise data in their business intelligence analysis.
To derive real business value from big data, you need the right tools to capture and organize a wide variety of data types from different sources, and to be able to easily analyze it within the context of all your enterprise data. Oracle offers the broadest and most integrated portfolio of products to help you acquire and organize these diverse data types and analyze them alongside your existing data to find new insights and capitalize
on hidden relationships.
Big data typically refers to the following types of data:
Traditional enterprise data – includes customer information from CRM systems, transactional ERP data, web store transactions, general ledger data.
Machine-generated /sensor data – includes Call Detail Records (“CDR”), weblogs, smart meters, manufacturing sensors, equipment logs (often referred to as digital exhaust), trading systems data.
Social data – includes customer feedback streams, micro-blogging sites like Twitter, social media platforms like Facebook
Big data Growth The McKinsey Global Institute estimates that data volume is growing 40% per year, and will grow 44x between 2009 and 2020.