The market for high-performance data analytics (HPDA) is expected to grow between 2022 - 2027.

The market for high-performance data analytics (HPDA) is expected to grow between 2022 - 2027.


Introduction

HPDA is a type of data analysis that combines high-performance computing (HPC) and data analysis to find patterns and insights. The capacity to search very huge data sets in real time has been made possible by the introduction of high-performance cloud computing and data analysis.

What is high performance data analytics?

Big data analysis has been based on high performance computing (HPC) for many years. However, the constantly growing amounts of data require new forms of high-performance computing in order to release unimaginably large amounts of data. High-performance data analysis is a term that was coined to describe the intersection of big data analysis and high-performance computing.

Powerful data analytics is a fast process of scanning very large data sets to gain insights. It does this using HPC parallel processing to run powerful analysis software.

For government organisations and businesses that need to combine high-performance computing with compact data processing, high-performance data analysis infrastructure is a new and quickly increasing sector.

In 2016, the global market for high-performance data analytics was valued at $ 26 billion, and it is predicted to reach $ 196 billion by 2025. The advantages of thorough data analysis

Hadoop and Spark, for example, do not have access to high-performance computing, which has traditionally been utilised for complicated modelling and simulation. Incompatible systems are brought together through powerful data analysis. The fundamental advantage of this convergence is the speed with which new insights emerge, resulting in improved decisions.

The powerful data analysis also offers the advantages of ultra-fast communication between processing elements to avoid input / output bottlenecks. Further advantages of the powerful data analysis are error detection, diagram modeling, diagram visualization, flow analysis, exploratory data analysis and structural analysis.

High-performance data analysis framework

A high-performance data analysis framework's primary purpose is to assist the data analyst in maintaining productivity and improving performance.

This is called a framework-as-an-application that harnesses the power of a high-performance computer system.

High-performance computing systems for data analysis

Data analysis using high-performance computer systems is possible using the following techniques:

Graph Analysis - Uses graphical modeling and visualization to understand large, complex networks.

Computationally Intensive Analysis - Fixed computationally intensive issues with new techniques.

Streaming Analytics analysis of fast streaming data with high bandwidth and high throughput with innovative algorithms. Exploratory data analysis - analyze multiple streaming data sources.

High-performance data analysis - frequent runs

Stanford researchers have created "Weld," a common runtime environment for high-performance data analysis and improving performance in data-intensive applications.

The Weld report says that modern analytics applications combine multiple functions from different libraries and frameworks to create more complex workflows. But the performance of the combined workflow is always below the hardware limitations due to the large data transfer functions.

Weld's high-performance analytics and extensive data solutions include a "common runtime environment for data-intensive applications that optimize entire libraries and features." This speeds up existing structures 30x without changing the API for users.

Does OmniSci offer a high-performance data analysis solution? Yes. OmniSci allows big data analysts to find insights that are directly related to the speed at which they communicate with the data. Analysts are no longer burdened by the slow speed and lack of granularity offered by traditional data science and great data analysis tools. They can use OmniSci to interact with multiple volumes of data, effortlessly and directly. Learn more about OmniSci solutions for big data analysts.

The Global High Performance Data Analytics (HPDA) market report provides a comprehensive market analysis for the forecast period. The report includes various functions as well as an analysis of trends and factors that play an important role in the market. These factors; Market dynamics include drivers, constraints, opportunities and challenges that outline the impact of these market factors. Driving forces and constraints are internal factors, while opportunities and challenges are external market factors. The Global High Performance Data Analytics (HPDA) market study provides insight into market growth in terms of sales throughout the forecast period. High Performance Data Analytics (HPDA) Market size and forecast

More information:

High Performance Data Analytics (HPDA) aims to use High Performance Computing (HPC) to analyze large data sets for patterns and reports. along with (HPDA) it is possible to easily analyze large data sets and take out conclusions about the information contained in them. HPDA demonstrates the value of very large datasets, increases HPC infrastructure availability, and provides benefits such as error detection with help of graphical modeling and visualization, streaming analysis, survey data analysis, and architecture analysis.

Market dynamics:

1. Market drivers

1.1 Increasing the amount of data in different sectors

1.2 Advanced analytical methods are needed to provide HPDA solutions

1.3 Ability of HPC systems to process data at higher speeds

1.4 Adoption of an open source framework for big data analysis


2. Market restrictions

2.1 Rising investment costs

2.2 Strict government rules and regulations

2.3 Programming complexity due to high parallelism

Market Segmentation Analysis in (HPDA): 

The Global High Performance Data Analytics (HPDA) market is divided into type, component, deployment model, vertical, and area.

High Performance Data Analytics (HPDA) Market segmentation analysis

1. By type:

1.1 Structures

1.2 No structures

1.3 Semi-structures

By folder:

2.1 Software

2.2 Hardware

2.3 Services

3. Through the insert mode:

3.1 In the cloud

3.2 On site

4. According to the vertical:

4.1 Health care

4.2 Government and defense

4.3 IT and Telecom

4.4 Banking, Financial Services and Insurance (BFSI)

4.5 Transport and logistics

4.6 Retail and consumer goods

4.7 Media and entertainment

4.8 Academy and research

4.9 Others

5. By region:

5.1 North America (USA, Canada, Mexico)

5.2 Europe (Germany, United Kingdom, France, rest in Europe)

5.3 Asia and the Pacific (China, India, Japan, the rest of Asia and the Pacific)

5.4 Latin America (Brazil, Argentina, rest in Latin America)

5.5 Middle East and Africa

Landscape competition:

The main players in the market are the following:

1. IBM Corporation

2. SAP SE

3. Cisco systems

4. Juniper Networks

5. Red Hat, Inc.

6. Teradata

7. Dell, Inc.

8. Hewlett-Packard Enterprise

9. Oracle Corporation

10. Microsoft Corporation

11. ATOS SE

12. Intel Corporation

13. Cray, Inc.

14. Institute of SAS

Mergers and acquisitions, new product launches, expansions, partnerships, joint ventures, collaborations, and other organic and inorganic growth tactics are employed by these key businesses.

Source - High Performance Data Analytics: Powerful Computing Drives Meaningful Insights
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