Data extraction is the process of gathering data from one or more sources for processing or storage, serving as the first step in an extract-transform-load (ETL) process. Businesses use data extraction to compile data needed to inform their decision-making process. The importance of data extraction lies in its role in consolidating data, creating datasets for machine learning models, and enabling accurate predictions and decision-making through analysis of past and present data trends. Different types of data extraction are based on the organization of the source data, including structured, semi-structured, and unstructured data extraction. Various techniques have been adopted for extracting data, such as manual data extraction, web scraping, database querying and exporting, application programming interface (API) calls and integration, robotic process automation (RPA), and optical character recognition (OCR). Several tools are available to help streamline and automate data extraction processes, including Python and its libraries, Tesseract, ParseHub, Airbyte, ScrapeStorm, and others. Choosing the right tool is crucial, taking into account the type of data to be extracted, the data source, and the characteristics of both, as well as business needs. Despite technological advancements, businesses still face challenges in data extraction, such as heterogeneity of data sources, legal complexities of web scraping, poor API documentation, changes in web page structure, and ensuring data security and compliance. Best practices for effective data extraction include implementing validation checks, documenting the data extraction process, leveraging parallel processing and data compression, and utilizing data observability tools.