In what way does High Performance Data Analytics help us?
High-performance computing (HPC) has played an important role in big data analytics for many years. The massive amount of data generated today will require new forms of high-performance computing to unlock it. Big data analytics and high-performance computing are converging to form High-Performance Data Analytics.
The goal of high-performance data analytics is to find insights from extremely large data sets within a short period of time. Powerful analytical software is run using the parallel processing of high-performance computing.
The demand for high-performance data analytics infrastructure is growing rapidly among government and private companies that need to combine high-performance computing with data-intensive analyses.
How does High Performance Data Analytics benefit companies?
High-performance computing, which is essential for complex modeling and simulation, is not available to big data analytics methods such as Hadoop and Spark. Through high-performance data analytics, once incompatible systems are brought together. This convergence leads to better decisions due to an acceleration of insights.
Furthermore, high-performance data analytics provides super fast communication between processing elements to avoid input/output bottlenecks. As well to error detection, graph modeling, graph visualization, streaming analytics, exploratory data analysis, and architecture analysis, high-performance data analytics offers other benefits.
Framework for High Performance Data Analytics
A high-performance data analytics framework provides a means to improve productivity and performance for data analysts.
Using high-performance computing systems to leverage framework-as-an-application is called framework-as-an-application.
The following techniques can be used to analyze data on high-performance computing systems: