Dataprogrammering

Dataprogrammering is the practice of writing software and scripts to work with data. It covers the design, development and maintenance of programs that collect, store, transform, analyze and visualize data. The goal is to enable reliable data flows and usable insights, often within larger data systems such as data warehouses or data lakes.

Typical activities include data ingestion from various sources, data cleaning and normalization, transformation and enrichment (ETL and ELT), data modeling and schema design, writing and optimizing queries, and building data pipelines for batch or streaming processing. Data quality, security and governance are also important concerns.

Common tools and languages include SQL for querying, Python and Java or Scala for programming, and specialized platforms such as relational databases, data warehouses, data lakes and processing frameworks (for example Spark or Kafka ecosystems) along with orchestration tools like Airflow. The choice of tools depends on scale, data types and organizational needs.

Dataprogrammering is closely related to data engineering and database programming, but focuses more on the software implementation aspects of data workflows rather than statistical modelling. Education often occurs within computer science, information systems or data engineering programs, and professionals may hold roles such as data programmer, ETL developer or data integration engineer.

It is distinct from but complementary to data science and analytics, which focus more on extracting insights through statistics and machine learning.