Workflow orchestration · Apache Software Foundation

Apache NiFi

Open-source dataflow tool with a drag-and-drop UI for routing, transforming, and mediating data between systems in near real time.

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Apache NiFi is an open-source dataflow automation tool, distinct from batch-DAG orchestrators like Airflow, in that it is built around continuous, flow-based processing: data moves through a visual graph of processors as discrete units called FlowFiles, each carrying its own attributes and provenance record as it is routed, transformed, or split. Pipelines are built by dragging and connecting pre-built processors (of which there are hundreds, covering databases, message queues, cloud storage, and file systems) in a web-based canvas, rather than writing code, which lowers the barrier for integration-heavy, non-analytics use cases like IoT ingestion, log routing, and system-to-system mediation. NiFi tracks full data provenance for every FlowFile, supports back-pressure and prioritized queuing between processors, and can run as a cluster for horizontal scale. It is entirely self-hosted with no first-party managed cloud offering, and is best suited to real-time data movement and routing rather than scheduled analytics transformations.

At a glance

Vendor Apache Software Foundation
Pricing model Open source + paid options
Free tier Yes
Deployment Self-hosted
Open source Yes (Apache-2.0)
Best for Teams needing visual, real-time data routing and mediation between systems, not just scheduled batch pipelines.

Pricing

Apache NiFi is free, open-source software with no vendor-hosted product or pricing page.

Pricing has not been verified yet — see the vendor's site.

Features

  • Visual, drag-and-drop flow-based programming canvas
  • Hundreds of pre-built processors for systems and protocols
  • Full data provenance tracking per FlowFile
  • Back-pressure and prioritized queuing between steps
  • Clustered deployment for horizontal scale
  • Real-time and near-real-time data routing
  • Fine-grained, per-flow access control

Integrations

Profile last reviewed September 21, 2026

Alternatives

Apache Airflow The de facto standard open-source workflow orchestrator, defining pipelines as Python DAGs of scheduled, dependent tasks. Kestra Open-source, event-driven orchestration platform defining workflows declaratively in YAML, with 800+ plugins for data, infra, and AI tasks. Argo Workflows Kubernetes-native workflow engine that runs each pipeline step as a container, defined as Kubernetes custom resources (CRDs).

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Terms to know

Data pipeline The automated sequence of steps that moves data from source systems to a destination, transforming it along the way. ETL (extract, transform, load) A data integration pattern that transforms data before loading it, unlike ELT which transforms after loading. Change data capture (CDC) A technique for detecting and streaming only the rows that changed in a source system, instead of reloading everything. Workflow orchestration Scheduling, sequencing and monitoring interdependent data tasks so each step runs only after the ones it depends on succeed. Data provenance The documented history of where a dataset came from and every transformation applied to it before its current state.

Related guides

How to choose a workflow orchestrator Pick a workflow orchestrator by pipeline shape, who owns it, and whether you already run Kubernetes — not by the demo DAG.