datauttrekk

Datauttrekk is the process of retrieving data from one or more source systems for further processing, storage, analysis, or migration. It is a key step in data integration, data warehousing, and analytics, and may be performed as part of ETL (extract, transform, load) or ELT (extract, load, transform) workflows.

Extraction can be automated or manual, batch or real-time. Methods include querying databases with SQL, calling APIs, reading files from storage, web scraping, and parsing documents. Data can be structured, semi-structured, or unstructured.

Common sources include relational databases, data lakes, cloud services, APIs, spreadsheets, and websites. Output formats can be CSV, JSON, XML, SQL dumps, Parquet, or unstructured text.

Tools and techniques range from ETL/ELT platforms (such as Informatica, Talend, Apache NiFi, and Apache Airflow) to programming approaches using Python or SQL, and specialized scrapers. Data security, access controls, and logging are critical, especially when handling sensitive information.

Quality and governance considerations cover data lineage, metadata management, schema changes, validation, and deduplication. Legal and privacy considerations, including regulations like GDPR, also influence how datauttrekk is designed and operated.

Applications of datauttrekk include data migration, data consolidation for analytics, business intelligence reporting, and feeding data science or machine learning pipelines. Effective practice emphasizes clear scope, quality checks, robust error handling, and traceable provenance to support reliable data-driven decisions.