Dataputkissa

Dataputkissa is a Finnish term used in data engineering to describe the end-to-end flow of data through a pipeline from source systems to analytical destinations. The word is formed from data and putki (pipe) with the inessive suffix -ssa, roughly translating to “in the data pipe.” The concept is commonly used to discuss how data moves, is transformed, and is delivered for analysis.

In practical terms, dataputkissa covers the stages of data ingestion, processing or transformation, storage, and consumption. It encompasses components such as data sources, ingestion mechanisms (for example, batch loaders or message brokers), processing engines (ETL or ELT jobs, streaming processors), storage layers (data lakes or data warehouses), and downstream consumers (reports, dashboards, or machine learning models). The framework emphasizes data lineage, quality gates, and the latency characteristics of the data flow.

There are multiple modes within dataputkissa. Batch and streaming are common variants, with ETL (extract, transform, load) and ELT (extract, load, transform) representing different processing philosophies. Dataputkissa is not a formal standard but a descriptive model used in Finnish-language discussions, education, and practice to illustrate data flow, governance, and architectural decisions.

Limitations of the concept include the risk of oversimplifying data quality and governance. Real-world implementations require explicit metadata, monitoring, error handling, and security considerations to ensure reliability across the pipeline.

See also: data pipeline, ETL, ELT, data lake, data warehouse, data lineage.