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Boost your new product ideas with HPE Big Data Analytics

AudreyCox

Hewlett Packard Enterprise partners are creating the next wave of on‐premises, SaaS, cloud, and appliance‐based applications with our powerful platform for OEM software solutions. 

We all know the reality: Big Data is here to stay; those that embrace it have the upper hand.  A recent IDC forecast shows that the Big Data technology market will grow at a 26.4% compound annual growth rate to $41.5 billion USD through 2018—or about six times the growth rate of the overall information technology market. Additionally, by 2020, IDC believes that line‐of‐business buyers will drive analytics beyond its historical sweet spot of relational (performance management) to the double‐digit growth rates of real‐time intelligence and exploration/discovery of the unstructured worlds.

To manage the incredible volume, velocity, and variety of data several new technologies have emerged.

Because most (>80%) of the newly created data is unstructured in nature (email, text, IM, log files), it is the job of Big Data analytics technology to combine the “known” with the “unknown” and to deliver value in ways never before possible. From data monetization to customer retention to compliance to traffic optimization, enterprises that embrace this emerging technology are changing the dynamics of business in every vertical market. 

 

OEM opportunity

All of this brings major opportunity for OEM software vendors. They can profit by creating analytic data management features or entirely new applications that put customers on a faster path to better, data‐driven decision making. 

To such capabilities, many embedded software providers are attempting unorthodox approaches to row-oriented OLTP databases, document stores, and Hadoop variations. Alternatively, some companies are attempting to build their own Big Data management systems. But such custom database solutions take thousands of hours of research and development, require specialized support and training, and may not be as adaptable to continuous enhancement as a pure‐play analytics platform. Both approaches are costly and often outside the core competency of businesses that are looking to bring solutions to market quickly. Your customers want an analytical OEM software platform that can: 

  • Manage huge data volumes
  • Deliver fast analytics:
  • Embed machine learning
  • Handle user-defined functions 
  • Support data scientists
  • Use highly advanced analytics
  • Require minimal administration

 

How HPE Vertica can handle most demanding Big Data challenges 

HPE Vertica is built from the ground up for the unique requirements of Big Data analytics. With its massively parallel processing system, it can handle petabyte-scale and has done so in some of the most demanding use cases across multiple industries. Because it is a column store and offers compression of data, the database delivers very fast analytics in a limited footprint, taking query times from hours to minutes, or minutes to seconds with a fraction of the memory and disk required. 

Every release of HPE Vertica is certified and tested with visualization and ETL tools. HPE Vertica distributes Python and R features for supporting the new generation of data scientists. Finally, HPE Vertica is more than just a database, providing advanced SQL‐based analytics from graph analysis to triangle counting to Monte Carlo simulations to geospatial and more. It is a full‐featured analytics platform. 

       analytics.png

Secrets behind HPE Vertica performance 

Because it’s specifically designed for analytic workloads, Vertica is quite different from other commercial alternatives. Vertica differs from OLTP DBMS and proprietary appliances (which typically embed row‐store DBMSs) by grouping data together on disk by column rather than by row (that is, so that the next piece of data read o disk is the next attribute in a column, not the next attribute in a row). This enables Vertica to read only the columns referenced by the query, instead of scanning the whole table as row‐oriented databases must do. This speeds up query processing dramatically by reducing disk I/O. 

 

By grouping data together on disk by column, Vertica creates the perfect scenario for data compression—lots of similar or repetitive values can be compressed very aggressively. Vertica features a library of many compression algorithms, which it applies automatically based on data type. Typically, Vertica occupies up to 90% less disk space than the data loaded into it. This not only lowers storage costs, but also speeds up querying by further reducing disk I/O. 

 

Learn More

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  • Big Data & Analytics
  • OEM
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About the Author

AudreyCox

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