GPU Acceleration Advancing the Evolution of Fast and Big Data Posted on : Jul 25 - 2017

With enterprise data warehouses and Hadoop data lakes now well established in the enterprise, organizations are able to store the large amounts of data they are generating, sourcing and curating. Across retail, financial, and healthcare sectors, IT leaders have found that dynamically shifting marketplace characteristics, consumer trends, social sentiment and care delivery efficiency all increasingly depend on performance-sensitive access to priority data feeds. Fast data augments these established information reservoirs by dramatically boosting the organization’s ability to act immediately, as things are happening, thereby maximizing time-to-value for perishable insights.

This article explains how constant advances in data analytics and other technologies have led to today’s highest-performing solution: the in-memory database powered by a graphics processing unit (GPU), which is capable of yielding analytical insights 100-1,000 times faster and at as little as one-tenth the cost of many big data alternatives.

From Transactions to Fast Data: The Evolution of Data Analytics

Data analytics can be considered to have evolved in four distinct phases. The driving forces for each new phase is universally acknowledged to be the relentless growth in the volume and variety, and most recently, the velocity of data.

• Relational Databases have long formed, and continue to form, the foundation for nearly all on-line transaction-processing (OLTP) applications.

• Data Warehouses substantially increased scale and enabled the first Big Data applications, but struggle to accommodate the growth in semi-structured and unstructured data.

• Data Lakes facilitate analysis of all data (structured, semi-structured and unstructured) using both proprietary and open source software running on clusters of commodity servers. This phase made big data more capable, scalable and affordable, but challenges can surface with end user requirements increasingly calling for sub-second query response times for streaming data analytics.

• Fast Data builds on all three prior phases to enable streaming data to be ingested and analyzed simultaneously at the “speed of thought” to satisfy the performance-sensitive needs of users and the scalability demands of the modern data-driven enterprise. View More