Hadoop

Actian Vectorwise Enables Real-time Hadoop Analysis

Hadoop is the big data engine of choice for many data-driven companies in the social media, gaming and Internet consumer markets. It is also establishing an increasingly large footprint in the enterprise market where its cost-efficiencies make it an attractive alternative to big data solutions from traditional data management and storage vendors.

Despite Hadoop’s cost advantages and unstructured data storage capabilities, it was not designed for real-time analytics-- an increasingly common need for many businesses. Many businesses are therefore beginning to integrate Hadoop with analytic databases, such as Actian Vectorwise, to provide a best of both worlds solution that enables them to store very large amounts of unstructured data but also analyze it in a structured data environment.

Recognising the importance of this trend, Actian has developed a bi-directional Hadoop Data Connector that provides data-driven businesses and enterprises with a robust, scalable and fully supported capability to rapidly analyze unstructured Hadoop data—creating information out it—and then put it into an operational system or back into Hadoop store.

The Actian Hadoop Data Connector enables businesses to use Actian Vectorwise, the industry’s fastest analytic database, as the backbone query and analysis engine for their Hadoop data strategy-- provisioning fast, highly concurrent interactive query capabilities to their users with integration to operational systems and data stores.

Vectorwise has pioneered high-performance Hadoop analytics with leading social media companies such as Badoo, IsCool and NK. Data-driven businesses no longer have to choose between the big data storage and batch processing capabilities of Hadoop and the blazing fast analytic capability of Vectorwise. The Actian Hadoop Data Connector provides a rapidly implemented, robust and scalable solution that saves time and eliminates the risk of relying on custom integration code to support mission critical big data analytic applications.