dataomvormingstechnieken

Data transformation, sometimes referred to as dataomvorming in Dutch, is the process of converting data from one format, structure, or meaning to another to enable analysis, storage, or integration. It is a core activity in data engineering and data workflows, and it can occur within data pipelines, data warehouses, or data lakes.

Core transformations include cleaning (handling missing values, correcting errors, and dealing with outliers), normalization and type conversion, aggregation and summarization, enrichment with external data, and restructuring such as joining, splitting, pivoting, or sorting. These operations aim to improve data quality, consistency, and usability for downstream processes such as reporting or modeling.

Two principal process models are ETL (extract, transform, load) and ELT (extract, load, transform). In ETL, transformation occurs before loading data into the target system; in ELT, data is loaded first and transformed within the target environment. Data transformation can be performed programmatically using languages like SQL and Python, or with specialized tools and frameworks such as Apache Spark, Talend, or Informatica. Data wrangling and automation workflows are common to support repeatability and governance.

Applications span data warehousing, business intelligence, analytics, and preprocessing steps for machine learning, including feature engineering. Challenges include maintaining data quality and lineage, ensuring reproducibility, managing privacy and security, and scaling transformations to large or streaming datasets. As data ecosystems evolve, transformation practices often adapt toward in-database processing, ELT patterns, and automated data quality controls to support reliable insights.