Dataformatkonvertering

Dataformatkonvertering, also known as data format conversion, is the process of transforming data from one format or encoding to another to enable interoperability between disparate systems, applications, or data stores. The goal is to preserve meaning and structure as much as possible while adapting to the constraints of the target format.

The typical workflow includes extracting data, parsing the source format, mapping fields and data types to the target schema, applying transformations (unit conversions, normalizations, aggregations), validating results against schemas or constraints, and serializing the data in the target format before loading it into the destination system.

Common formats include CSV, JSON, XML, and YAML for text-based formats; Parquet, Avro, and ORC for columnar data; and various database dumps. Dataformatkonvertering often requires handling schema-based formats with explicit schemas (XML Schema, JSON Schema, Avro schema) and schema-less formats that rely on conventions. Issues to manage include encoding (UTF-8), character sets, date and time representations, time zones, decimal separators, and potential information loss or precision changes during conversion.

Approaches range from one-off scripts to full-scale ETL pipelines and data integration platforms. Modern pipelines may employ schema evolution, data lineage tracking, validation tests, and automated testing to ensure reliability and auditability.

Applications span data migration, system modernization, data warehousing, analytics, and data lake ingestion, where diverse source systems must be brought into a common, queryable form. Challenges include scale, performance, schema drift, and maintaining fidelity across updates.