Big Data refers to technologies and initiatives that involve data that is too diverse, fast-changing or massive for conventional technologies, skills and infra- structure to address efficiently. Said differently, the volume, velocity or variety of data is too great.

Why Splunk for Big Data?

Splunk software can collect and index any machine data in real time. Now you can search, explore, navigate, analyze and visualize all your data from one place.

Bring the power of Splunk Enterprise to Hadoop. Seamlessly search and analyze Hadoop-based data as part of your Splunk Enterprise deployment with Splunk Analytics for Hadoop. Splunk Analytics for Hadoop gives you the power to rapidly detect patterns and find anomalies across petabytes of raw data in Hadoop without the need to move or replicate data.

    Interactively query raw data by previewing results and refining searches using the same Splunk Enterprise interface
    Quickly create and share charts, graphs and dashboards
    Ensure security with role-based access control and HDFS pass-through authentication
    Splunk Analytics for Hadoop natively supports Apache Hadoop and Amazon EMR, Cloudera CDH, Hortonworks Data Platform, IBM InfoSphere BigInsights, MapR M-series and Pivotal HD distributions

For organizations of all sizes, data management has shifted from an important competency to a critical differentiator that can determine market winners and has-beens. Fortune 1000 companies and government bodies are starting to benefit from the innovations of the web pioneers. These organizations are defining new initiatives and reevaluating existing strategies to examine how they can transform their businesses using Big Data. In the process, they are learning that Big Data is not a single technology, technique or initiative. Rather, it is a trend across many areas of business and technology.

But today, new technologies make it possible to realize value from Big Data. For example, retailers can track user web clicks to identify behavioral trends that improve campaigns, pricing and stockage. Utilities can capture household energy usage levels to predict outages and to incent more efficient energy consumption. Governments and even Google can detect and track the emergence of disease outbreaks via social media signals. Oil and gas companies can take the output of sensors in their drilling equipment to make more efficient and safer drilling decisions.

Specifically, Big Data relates to data creation, storage, retrieval and analysis that is remarkable in terms of volume, velocity, and variety

    Velocity. Clickstreams and ad impressions capture user behavior at millions of events per second; high-frequency stock trading algorithms reflect market changes within microseconds; machine to machine processes exchange data between billions of devices; infrastructure and sensors generate massive log data in real-time; on-line gaming systems support millions of concurrent users, each producing multiple inputs per second.

    Variety. Big Data data isn't just numbers, dates, and strings. Big Data is also geospatial data, 3D data, audio and video, and unstructured text, including log files and social media. Traditional database systems were designed to address smaller volumes of structured data, fewer updates or a predictable, consistent data structure. Traditional database systems are also designed to operate on a single server, making increased capacity expensive and finite. As applications have evolved to serve large volumes of users, and as application development practices have become agile, the traditional use of the relational database has become a liability for many companies rather than an enabling factor in their business. Big Data databases,such as MongoDB, solve these problems and provide companies with the means to create tremendous business value

Big Data for the Enterprise

With Big Data databases, enterprises can save money, grow revenue, and achieve many other business objectives, in any vertical.

        Build new applications: Big data might allow a company to collect billions of real-time data points on its products, resources, or customers – and then repackage that data instantaneously to optimize customer experience or resource utilization. For example, a major US city is using MongoDB to cut crime and improve municipal services by collecting and analyzing geospatial data in real-time from over 30 different departments.
        Improve the effectiveness and lower the cost of existing applications: Big data technologies can replace highly-customized, expensive legacy systems with a standard solution that runs on commodity hardware. And because many big data technologies are open source, they can be implemented far more cheaply than proprietary technologies.
        Realize new sources of competitive advantage: Big data can help businesses act more nimbly, allowing them to adapt to changes faster than their competitors.
        Increase customer loyalty: Increasing the amount of data shared within the organization – and the speed with which it is updated – allows businesses and other organizations to more rapidly and accurately respond to customer demand.

Secunets Technologies will provide the required expertise for your organization to leverage on benefits of big data especially big data analytics