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ITS offers state-of-the-art web scraping services and solutions by using industry-standard tools and techniques crafted in Python language. Our designed crawlers and bots can crawl any website and gather any information from the website in a matter of minutes. Data extraction from online sources has been thoroughly analysed, and wrapper algorithm techniques are being used to the greatest extent possible. It is an algorithm programme that can be constructed manually or semi-automatically, and it is used to assess the source of data on the websites that are visited. This work presents a complete description of the methodologies, which has been organised into a taxonomy based on previous research in the areas of Wrappers Induction, Semi-Automatic Wrapper Generation, and Automatic Wrapper Generation. In addition, throughout this work, a critical critique of each strategy has been outlined for your consideration. There are different recall rates and directness rates for each of these strategies. Moreover, it provides insight into the effectiveness of the extraction technique. Performing a thorough evaluation of this taxonomy would provide an excellent opportunity for future research of cutting-edge data extraction techniques. Relational databases, also known as structured data repositories within the digital structure, are typically organised in rows and columns and store structured data. The majority of corporate data, on the other hand, is in the form of unstructured data. Extracting unstructured, semistructured, and structured data from user requirements on web sites on the internet is known as data extraction, and it may be performed at any degree of automation. Web pages include data that is formalised in a structured data format, which is contained inside a data area. Using tools to manipulate and analyse data has always necessitated the use of large amounts of computational server resources. This article will evaluate current strategies for data extraction from heterogeneous data in the Big Data environment, with a particular emphasis on the Big Data environment. The purpose of this study is to cover several data extraction methodologies, as well as the fundamental tools required for extracting desired data from a variety of web-based data sources. Information Extraction Ways, Automatic Wrapper Generation, Semi-Automatic Wrapper Generation, Wrapper Induction, and Wrapper Maintenance are some of the approaches that will be investigated in this paper. Despite the fact that many of the needed procedures from online sources have been tried and developed, there are still a dearth of evaluations for these strategies. This study examines data extraction methods based on wrapper techniques and analyses them in order to determine the most effective method for extracting data from internet sources.Data extraction from online sources has been thoroughly analysed, and wrapper algorithm techniques are being used to the greatest extent possible. It is an algorithm programme that can be constructed manually or semi-automatically, and it is used to assess the source of data on the websites that are visited. This work presents a complete description of the methodologies, which has been organised into a taxonomy based on previous research in the areas of Wrappers Induction, Semi-Automatic Wrapper Generation, and Automatic Wrapper Generation. In addition, throughout this work, a critical critique of each strategy has been outlined for your consideration. There are different recall rates and directness rates for each of these strategies. Moreover, it provides insight into the effectiveness of the extraction technique. Performing a thorough evaluation of this taxonomy would provide an excellent opportunity for future research of cutting-edge data extraction techniques. Relational databases, also known as structured data repositories within the digital structure, are typically organised in rows and columns and store structured data. The majority of corporate data, on the other hand, is in the form of unstructured data. Extracting unstructured, semistructured, and structured data from user requirements on web sites on the internet is known as data extraction, and it may be performed at any degree of automation. Web pages include data that is formalised in a structured data format, which is contained inside a data area. Using tools to manipulate and analyse data has always necessitated the use of large amounts of computational server resources. This article will evaluate current strategies for data extraction from heterogeneous data in the Big Data environment, with a particular emphasis on the Big Data environment.