HPE is advancing into the Big Data market by releasing the next version of Vertica 8 and HPE Haven OnDemand Combinations.
The OnDemand combination which was released in March targets to provide developers with the ability to apply machine learning to develop next generation applications on the cloud-based offering.
Besides drag and drop interface and pre-built catalogue of cognitive services, it is also offering developers to use various machine learning API's with different combinations that are used in development projects, the idea is to speed up the process for building new and interesting mobile and enterprise applications.
The latest release of Vertica 8 provides combined architecture, advanced analytics and advanced in-database analytics(a set of capabilities to help users planning for sophisticated analysis).
New support for Microsoft Azure Cloud:
They have officially announced the collaboration to provide best-in-class hybrid computing experiences. Microsoft Azure marketplace will have HPE with Azure support which will provide you with the right to select cloud platform based on the requirement.
The new HPE Vertica 8 features are:
In-Database Machine Learning: Vertica's in-database has implemented machine learning algorithms, teams can create and deploy R based machine learning models directly in Vertica for larger sets of data, increasing the decision-making process with pinpoint .
Analysis-in-Place Analytics on Hadoop: Users can now extract value from their Hadoop data lakes with Vertica's high-performance and ORC readers that allow users to access and analyse data. Organisations can relish high performance and Hadoop-based economics in a smooth solution.
Expanded Multi-cloud capabilities and support: Organisations can now have many deployment options than ever. Vertica is developed for both Microsoft and AWS deployment. The latest release also incorporates expanded AWS seamless access to S3, tighter security and much more
Optimized Apache Spark Adapter: This optimized intelligent adapter allows fast data exchange between Vertica and Spark systems so that the data scientists can develop more robust machine Learning models in Spark for queries on small data sets and tap into extensive Vertica in-database SQL analytics for the most sophisticated queries on the largest data volumes at massive scale.