High Performance Data Analytics
High Performance Data Analytics Definition
High performance data analytics (HPDA) uses high performance computing (HPC) combined with data analytics to discover patterns and insights. The advent of high performance cloud computing and data analytics created the ability to interrogate extremely large data sets in real time.
What is High Performance Data Analytics?
Big data analytics has depended on high performance computing (HPC) for many years. But the continued and exponential increase in data means new forms of high performance computing will be required to unlock unimaginably massive amounts of data. High Performance Data Analytics is a term that was coined to describe the confluence of big data analytics and high performance computing.
High performance data analytics is the process of quickly examining extremely large data sets to find insights. This is done by using the parallel processing of high performance computing to run powerful analytic software.
High performance data analytics infrastructure is a new and fast-growing market for government and commercial enterprises that need to combine high-performance computing with data-intensive analysis.
The global high performance data analytics market was $26 billion in 2016 and is projected to reach $196 billion by 2025.
Benefits of High Performance Data Analytics
Methods for big data analytics such as Hadoop and Spark do not have access to the high performance computing that has long been used for complex modeling and simulations. High performance data analytics brings once-incompatible systems together. The central benefit of this convergence is an acceleration of insights that lead to better decisions.
High performance data analytics also provides the benefit of super fast communication between processing elements to avoid input/output bottlenecks. Other benefits of high performance data analytics include error detection, graph modeling, graph visualization, streaming analytics, exploratory data analysis and architecture analysis.
High Performance Data Analytics Framework
An important goal of a high performance data analytics framework is to help a data analyst maintain productivity and improve performance.
This is called framework-as-an-application, which leverages the strengths of high performance computing systems.
High Performance Data Analytics Computing Systems
Data analytics with high performance computing systems is possible with the following techniques:
- Graph Analytics — Uses graph modeling and visualization to understand large, complex networks.
- Compute Intensive Analytics — Solves computationally intensive problems with innovative techniques.
- Streaming Analytics — Rapidly analyzes high-bandwidth and high-throughput streaming data with new algorithms.
- Exploratory Data Analysis — Analyzes massive streaming data sources.
High Performance Data Analytics Common Runtimes
Stanford researchers created “Weld,” which is a common runtime for high performance data analytics and improves the performance of data-intensive applications.
A report about Weld said modern analytics applications combine multiple functions from different libraries and frameworks to build increasingly complex workflows. But the performance of the combined workflow is often below hardware limits due to the extensive data movement across functions.
The high performance analytics and big data solutions offered by Weld include a “common runtime for data-intensive applications that optimizes across disjoint libraries and functions.” This can speed up existing frameworks by 30x without changing user-facing APIs.
Does OmniSci Offer a High Performance Data Analytics Solution?
Yes. OmniSci lets big data analysts find insights directly related to the speed they interact with the data. Analysts will no longer be burdened by the slow speed and lack of granularity offered by mainstream data science and big data analytics tools. They can use OmniSci to interact with huge volumes of data, effortlessly and instantly. Learn more about solutions from OmniSci for Big Data Analysts.