Oracle Big Data

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Enterprise Big Data
Predictions 2016
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Is Your Data a Competitive Advantage?

Oracle's big data strategist explains how to create a data capital game plan.

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Increase Trust in Your Data with Oracle Data Governance Solutions
Woman reviewing Oracle Press Release: Learn to Exploit All Your Data for Business Value
Oracle Unveils Oracle Cloud Platform for Big Data
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The Rise of Data Capital

Realize remarkable value from the capture and use of more data, using this wealth of information as the new capital with Oracle Big Data, Hadoop, and analytics.

The Foundation for Data Innovation

Enterprise Big Data

We live in a world increasingly driven by data. How your organization defines its data strategy and approach—including its choice of big data and cloud technologies—will make a critical difference in your ability to compete in the future.

  • Leverage the benefits of big data in the cloud
  • Choose the leading provider to both Fortune 500 firms and top cloud app vendors
  • Extend scalability, reliability, and resiliency across the entire environment
  • Build on Oracle Engineered Systems for the best price for performance
  • Protect investments and skills in the era of big data and cloud

What is Big Data?

Big data describes a holistic information management strategy that includes and integrates many new types of data and data management alongside traditional data.

Image thumbnail of Oracle White Paper: Enterprise Architect's Guide to Big Data-€”Reference Architecture Overview

White paper: Enterprise Architect's Guide to Big Data—Reference Architecture Overview

Big data has also been defined by the four Vs:

  • Volume. The amount of data. While volume indicates more data, it is the granular nature of the data that is unique. Big data requires processing high volumes of low-density, unstructured Hadoop data—that is, data of unknown value, such as Twitter data feeds, click streams on a web page and a mobile app, network traffic, sensor-enabled equipment capturing data at the speed of light, and many more. It is the task of big data to convert such Hadoop data into valuable information. For some organizations, this might be tens of terabytes, for others it may be hundreds of petabytes.
  • Velocity. The fast rate at which data is received and perhaps acted upon. The highest velocity data normally streams directly into memory versus being written to disk. Some Internet of Things (IoT) applications have health and safety ramifications that require real-time evaluation and action. Other internet-enabled smart products operate in real time or near real time. For example, consumer eCommerce applications seek to combine mobile device location and personal preferences to make time-sensitive marketing offers. Operationally, mobile application experiences have large user populations, increased network traffic, and the expectation for immediate response.
  • Variety. New unstructured data types. Unstructured and semi-structured data types, such as text, audio, and video require additional processing to both derive meaning and the supporting metadata. Once understood, unstructured data has many of the same requirements as structured data, such as summarization, lineage, auditability, and privacy. Further complexity arises when data from a known source changes without notice. Frequent or real-time schema changes are an enormous burden for both transaction and analytical environments.
  • Value. Data has intrinsic value—but it must be discovered. There are a range of quantitative and investigative techniques to derive value from data—from discovering a consumer preference or sentiment, to making a relevant offer by location, or for identifying a piece of equipment that is about to fail. The technological breakthrough is that the cost of data storage and compute has exponentially decreased, thus providing an abundance of data from which statistical analysis on the entire data set versus previously only sample. The technological breakthrough makes much more accurate and precise decisions possible. However, finding value also requires new discovery processes involving clever and insightful analysts, business users, and executives. The real big data challenge is a human one, which is learning to ask the right questions, recognizing patterns, making informed assumptions, and predicting behavior.

To learn more about critical considerations in selecting big data technologies, read Oracle's white paper, Enterprise Architect's Guide to Big Data—Reference Architecture Overview (PDF).

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Oracle Big Data Crushes Time-to-Insight for

I can say fairly confidently that Wargaming now has the leading-edge solution in terms of data warehousing, data engineering, and analytics solutions over any other gaming company in the world.

—Craig Fryar, Head of Wargaming Business Intelligence
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StubHub Spots Customer Trends Through Big Data Analytics

Oracle Database and Oracle Advanced Analytics enable predictive analytics, data mining, text mining, statistical analysis, and advanced numerical computations—all inside the database.

—Mike Barber, Senior Manager of Data Science, StubHub
Image supporting Oracle Video: Thomson Reuters Uses Oracle'€™s Big Data Platform
Thomson Reuters Uses Oracle’s Big Data Platform

Oracle Big Data Appliance, Exadata and Exalytics engineered systems provide high performance and a big data delivery of Hadoop, data warehousing, and analytics to improve customer service.

—Rick King, COO for Technology, Thomson Reuters
Image supporting Oracle Video: De Persgroep Attains 360 View with Oracle Big Data Appliance
De Persgroep Attains 360 View with Oracle Big Data Appliance

We want to attain a 360-degree view of our customers and prospects. The big data project helped us to achieve that goal.

—Luc Verbist, CIO De Persgroup

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