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Azure Data Lake

What is High Performance Data Analytics? And How it Works?

High performance data analytics (HPDA) is a method of analyzing large data sets that utilizes high performance computing (HPC).

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:

  • Modeling and visualization of graphs is used in graph analytics to understand large, complex networks.
  • Analyzing computationally intensive problems using innovative techniques.
  • Analyzes streaming data in real-time with new algorithms for high-bandwidth and high-throughput.
  • Analyzes massive streaming data sources in exploratory data analysis.
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