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Data extraction as a book category covers the methods, tools, and best practices for retrieving structured or semi-structured data from diverse sources—websites, APIs, databases, documents (PDFs, Word), images (OCR), logs, and legacy systems—and preparing it for analysis or integration. These books explain techniques such as web scraping, ETL (extract-transform-load) pipelines, parsing and normalization, data cleaning, automation and scheduling, and scalable processing with frameworks and cloud services, while also addressing error handling, performance tuning, and data quality. Many volumes include practical code examples, case studies, and workflows using languages and tools like Python, SQL, Apache NiFi/Beam/Spark, and specialized scraping libraries, plus guidance on legal, ethical, and privacy considerations when collecting data. The category serves a range of readers from analysts and data engineers learning hands‑on skills to architects and managers designing reliable data acquisition systems.

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