Global Data Scraping Software Supply, Demand and Key Producers, 2023-2029

Global Data Scraping Software Supply, Demand and Key Producers, 2023-2029


The global Data Scraping Software market size is expected to reach $ million by 2029, rising at a market growth of % CAGR during the forecast period (2023-2029).

Data scraping (or data extraction) software is used to extract unstructured data from web pages. The data is then converted into a structured format that can be loaded into a database. Examples of unstructured data might be emails or other contact info, reports, URLs, etc. The data conversion process uses a variety of tools to assess structure, including text pattern matching, tabulation, or text analytics to comprehend the text and link it to other data.

This report studies the global Data Scraping Software demand, key companies, and key regions.

This report is a detailed and comprehensive analysis of the world market for Data Scraping Software, and provides market size (US$ million) and Year-over-Year (YoY) growth, considering 2022 as the base year. This report explores demand trends and competition, as well as details the characteristics of Data Scraping Software that contribute to its increasing demand across many markets.

Highlights and key features of the study

Global Data Scraping Software total market, 2018-2029, (USD Million)

Global Data Scraping Software total market by region & country, CAGR, 2018-2029, (USD Million)

U.S. VS China: Data Scraping Software total market, key domestic companies and share, (USD Million)

Global Data Scraping Software revenue by player and market share 2018-2023, (USD Million)

Global Data Scraping Software total market by Type, CAGR, 2018-2029, (USD Million)

Global Data Scraping Software total market by Application, CAGR, 2018-2029, (USD Million)

This reports profiles major players in the global Data Scraping Software market based on the following parameters – company overview, revenue, gross margin, product portfolio, geographical presence, and key developments. Key companies covered as a part of this study include BrightData, Scrape.do, Scrapingdog, AvesAPI, ParseHub, Diffbot, Scrape-It.Cloud, Octoparse and ScrapingBee, etc.

This report also provides key insights about market drivers, restraints, opportunities, new product launches or approvals, COVID-19 and Russia-Ukraine War Influence.

Stakeholders would have ease in decision-making through various strategy matrices used in analyzing the World Data Scraping Software market

Detailed Segmentation:

Each section contains quantitative market data including market by value (US$ Millions), by player, by regions, by Type, and by Application. Data is given for the years 2018-2029 by year with 2022 as the base year, 2023 as the estimate year, and 2024-2029 as the forecast year.

Global Data Scraping Software Market, By Region:


United States
China
Europe
Japan
South Korea
ASEAN
India
Rest of World

Global Data Scraping Software Market, Segmentation by Type


Cloud-based
On Premises

Global Data Scraping Software Market, Segmentation by Application


SMEs
Large Enterprises

Companies Profiled:
BrightData
Scrape.do
Scrapingdog
AvesAPI
ParseHub
Diffbot
Scrape-It.Cloud
Octoparse
ScrapingBee
Grepsr
Scraper API
Scrapy
Import.io
Apify
Mozenda
justLikeAPI
SerpApi
JobsPikr
TexAu
Zyte
NimbleWay
Smartproxy
Zenscrape

Key Questions Answered

1. How big is the global Data Scraping Software market?

2. What is the demand of the global Data Scraping Software market?

3. What is the year over year growth of the global Data Scraping Software market?

4. What is the total value of the global Data Scraping Software market?

5. Who are the major players in the global Data Scraping Software market?

6. What are the growth factors driving the market demand?


1 Supply Summary
2 Demand Summary
3 World Data Scraping Software Companies Competitive Analysis
4 United States VS China VS Rest of World (by Headquarter Location)
5 Market Analysis by Type
6 Market Analysis by Application
7 Company Profiles
8 Industry Chain Analysis
9 Research Findings and Conclusion
10 Appendix

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