Dataflows

Dataflows are pipelines that move data from source systems through a sequence of processing steps to one or more destinations. They define how data elements travel and transform within a graph of operations, often modeled as a directed acyclic graph (DAG). Dataflows emphasize the movement and transformation of data, and can operate in batch, streaming, or hybrid modes.

In a typical dataflow, data is ingested from sources such as relational databases, files, or event streams; it passes through transformations that may include filtering, joining, aggregation, enrichment, and format changes; and it is written to sinks such as data warehouses, data lakes, or operational systems. Metadata, including data lineage, timing, and quality indicators, is captured to support governance and reproducibility.

Dataflow engines provide orchestration, parallel execution, fault tolerance, and progress monitoring. They can run as scheduled batch pipelines, real-time streaming pipelines, or continuously updating systems. The graph structure enables automatic scheduling of independent steps, parallel processing, and incremental updates where supported.

Common concerns include data quality, schema evolution, backpressure handling, latency, and observability. Design considerations focus on idempotency, restartability after failures, and clear provenance. Dataflows are implemented with a range of tooling—from specialized data integration platforms to general-purpose processing frameworks—and are expressed in declarative languages (such as SQL or dataflow DSLs) or in general-purpose programming languages.

Use cases include data integration for analytics and data warehouses or lakes, real-time dashboards, event-driven architectures, and data preparation for machine learning. Effective dataflow design balances timeliness, correctness, and resource usage while maintaining visibility into data lineage and governance.