Data Extraction
Data extraction refers to the process of retrieving relevant information from various data sources, which can include databases, websites, documents, images, or other data-intensive environments. This process is a critical first step in the data workflow, often preceding tasks like data processing and analysis. Key aspects of data extraction include:
- Source Identification. Determining the sources from which data needs to be extracted, which can range from structured databases to unstructured text files.
- Data Retrieval. Accessing the data using methods appropriate to the source, such as SQL queries for databases or web scraping for websites.
- Data Formatting. Converting the extracted data into a consistent format suitable for further processing or analysis. This may involve normalizing data formats or transforming raw data into a structured form.
- Data Quality Checks. Ensuring the accuracy and integrity of the extracted data by removing duplicates, correcting errors, and verifying completeness.
Data extraction is essential for data-driven decision making and supports a wide range of applications, from business intelligence and analytics to machine learning and artificial intelligence, where clean and structured data is crucial for obtaining reliable insights.