Apache NiFi vs StreamPipes vs Kestra: Self-Hosted Data Pipeline Orchestration 2026

Building reliable data pipelines is one of the most common infrastructure challenges for engineering teams. Whether you are moving data from IoT sensors, syncing databases, or orchestrating complex ETL workflows, the right platform makes the difference between a maintainable system and a brittle mess.

In this guide, we compare three leading open-source platforms for self-hosted data pipeline orchestration: Apache NiFi, Apache StreamPipes, and Kestra. Each takes a fundamentally different approach to the problem, and understanding those differences is the key to picking the right tool.

Why Self-Host Your Data Pipeline Platform

Commercial data integration platforms like Fivetran, Matillion, and Informatica Cloud charge per-connector or per-row pricing that can quickly become prohibitive as data volumes grow. Self-hosted alternatives give you full control over your data, unlimited connectors (subject to what the community has built), and predictable infrastructure costs.

Self-hosting also matters when your data cannot leave your network – healthcare, finance, and industrial IoT deployments often have strict data residency requirements that rule out cloud-hosted SaaS options entirely.

Apache NiFi: Visual Data Flow Automation

Apache NiFi is the most mature of the three platforms, originally developed at the NSA and open-sourced in 2014. It provides a web-based interface for designing data flows using a drag-and-drop canvas, with over 400 built-in processors covering databases, message queues, file systems, cloud storage, and web APIs.

NiFi’s architecture is built around a flowfile model – each piece of data moving through the system carries its own metadata, and processors transform, route, or enrich flowfiles as they pass through. This makes NiFi ideal for scenarios where you need fine-grained control over data routing, backpressure handling, and failure recovery.

Key stats: 6,060 GitHub stars, last updated April 2026, primary language Java.

When to Use NiFi

• You need real-time data ingestion with backpressure management
• Your team prefers visual, no-code flow design
• You require guaranteed delivery with exactly-once or at-least-once semantics
• You need to route data to dozens of different sinks based on content

Docker Compose Setup for Apache NiFi

NiFi does not ship an official docker-compose.yml, but the community-standard single-node deployment is straightforward:

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version: '3.8'

services:
  nifi:
    image: apache/nifi:1.28.1
    container_name: nifi
    ports:
      - "8443:8443"
    environment:
      - NIFI_WEB_HTTPS_PORT=8443
      - SINGLE_USER_CREDENTIALS_USERNAME=admin
      - SINGLE_USER_CREDENTIALS_PASSWORD=changeme-admin-password-here
      - NIFI_CLUSTER_IS_NODE=true
      - NIFI_CLUSTER_NODE_PROTOCOL_PORT=8082
    volumes:
      - nifi_database_repository:/opt/nifi/nifi-current/database_repository
      - nifi_flowfile_repository:/opt/nifi/nifi-current/flowfile_repository
      - nifi_content_repository:/opt/nifi/nifi-current/content_repository
      - nifi_provenance_repository:/opt/nifi/nifi-current/provenance_repository
      - nifi_state:/opt/nifi/nifi-current/state
      - nifi_logs:/opt/nifi/nifi-current/logs
      - nifi_conf:/opt/nifi/nifi-current/conf
    restart: unless-stopped

volumes:
  nifi_database_repository:
  nifi_flowfile_repository:
  nifi_content_repository:
  nifi_provenance_repository:
  nifi_state:
  nifi_logs:
  nifi_conf:

Start the service with docker compose up -d and access the UI at https://localhost:8443/nifi. The default credentials are set via the SINGLE_USER_CREDENTIALS_* environment variables.

Apache StreamPipes: Self-Service IoT Data Analytics

Apache StreamPipes is designed specifically for IoT and industrial data streams. Its core philosophy is enabling non-technical users to connect sensors, analyze data streams, and build dashboards without writing code. Unlike NiFi’s processor-centric model, StreamPipes uses a pipeline model with sources, processors, and sinks connected in a linear flow.

StreamPipes includes built-in support for OPC-UA, MQTT, and Modbus – industrial protocols that NiFi requires third-party processors to handle. It also ships with a rule engine for real-time event processing and a data lake integration for historical analysis.

Key stats: 720 GitHub stars, last updated April 2026, primary language Java.

When to Use StreamPipes

• Your primary data sources are IoT devices, PLCs, or industrial sensors
• You need OPC-UA, MQTT, or Modbus protocol support out of the box
• Non-technical team members need to build and monitor data pipelines
• You want integrated rule-based alerting and dashboarding

Docker Compose Setup for Apache StreamPipes

StreamPipes provides an official docker-compose.yml for getting started:

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version: '3.8'

services:
  backend:
    image: apache/streampipes-backend:latest
    container_name: streampipes-backend
    depends_on:
      - couchdb
      - activemq
    environment:
      - SP_BACKEND_HOST=backend
      - SP_COUCHDB_HOST=couchdb
      - SP_COUCHDB_PORT=5984
      - SP_JMS_HOST=activemq
      - SP_JMS_PORT=61616
    ports:
      - "8030:8030"
    volumes:
      - streampipes-data:/root/.streampipes
    restart: unless-stopped

  ui:
    image: apache/streampipes-ui:latest
    container_name: streampipes-ui
    ports:
      - "80:80"
    depends_on:
      - backend
    restart: unless-stopped

  couchdb:
    image: couchdb:3.3
    container_name: streampipes-couchdb
    environment:
      - COUCHDB_USER=admin
      - COUCHDB_PASSWORD=admin
    volumes:
      - couchdb-data:/opt/couchdb/data
    restart: unless-stopped

  activemq:
    image: apache/activemq-classic:6.1.3
    container_name: streampipes-activemq
    ports:
      - "61616:61616"
    restart: unless-stopped

volumes:
  streampipes-data:
  couchdb-data:

The StreamPipes UI is available at http://localhost:80 and the backend API at http://localhost:8030.

Kestra: Event-Driven Orchestration for Mission-Critical Workflows

Kestra is the newest of the three platforms but has grown rapidly to over 26,000 GitHub stars. It takes a fundamentally different approach: instead of visual drag-and-drop, Kestra uses YAML-defined workflows (called “flows”) with a rich plugin ecosystem. Every flow is version-controlled, triggerable by schedules, events, or API calls, and fully observable with execution logs, metrics, and a visual execution tree.

Kestra’s strength is in orchestration – coordinating complex, multi-step workflows that involve databases, APIs, file transfers, and compute jobs. While NiFi focuses on data movement and StreamPipes on IoT analytics, Kestra is the generalist that excels at ETL orchestration, data quality pipelines, and batch processing.

Key stats: 26,759 GitHub stars, last updated April 2026, primary language Java.

When to Use Kestra

• You need scheduled, event-driven orchestration of complex multi-step workflows
• Your team prefers Infrastructure-as-Code (YAML) over visual editors
• You require tight Git integration for version-controlled pipeline definitions
• You need to orchestrate non-data tasks alongside data pipelines (notifications, infrastructure provisioning, etc.)

Docker Compose Setup for Kestra

Kestra ships a clean docker-compose.yml with PostgreSQL as the backing store:

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version: '3.8'

volumes:
  postgres-data:
    driver: local
  kestra-data:
    driver: local

services:
  postgres:
    image: postgres:18
    volumes:
      - postgres-data:/var/lib/postgresql/data
    environment:
      POSTGRES_DB: kestra
      POSTGRES_USER: kestra
      POSTGRES_PASSWORD: k3str4_secure_password
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -d kestra -U kestra"]
      interval: 30s
      timeout: 10s
      retries: 10
    restart: unless-stopped

  kestra:
    image: kestra/kestra:latest
    pull_policy: always
    command: server standalone
    volumes:
      - kestra-data:/app/storage
      - /var/run/docker.sock:/var/run/docker.sock
    environment:
      KESTRA_CONFIGURATION: |
        datasources:
          postgres:
            url: jdbc:postgresql://postgres:5432/kestra
            username: kestra
            password: k3str4_secure_password
        repository:
          type: postgres
        storage:
          type: local
          local:
            base-path: "/app/storage"
        queues:
          type: postgres
        tasks:
          tmp-dir:
            path: /tmp/kestra-wd/tmp
    ports:
      - "8080:8080"
      - "8081:8081"
    depends_on:
      postgres:
        condition: service_healthy
    restart: unless-stopped

Access the Kestra UI at http://localhost:8080. Flows are defined as YAML files and can be synced from a Git repository using Kestra’s built-in Git sync feature.

Feature Comparison

Choosing the Right Platform

The choice between these three platforms comes down to your primary workload:

Choose Apache NiFi when you need real-time data ingestion with guaranteed delivery. Its flowfile architecture excels at scenarios where data arrives continuously from multiple sources and needs to be routed, transformed, and distributed to dozens of destinations. The visual interface makes it accessible to operators who are not developers.

Choose Apache StreamPipes when your data comes primarily from IoT devices and industrial equipment. Its native OPC-UA, MQTT, and Modbus support, combined with built-in rule engines and dashboarding, makes it the best fit for manufacturing, energy, and smart building deployments.

Choose Kestra when you need to orchestrate complex, multi-step workflows that go beyond simple data movement. Its YAML-based flow definitions integrate naturally into Git workflows, its scheduling and event-triggering are first-class citizens, and its plugin ecosystem covers virtually every integration point you might need.

For teams that need both real-time data movement AND complex orchestration, a common pattern is using NiFi for data ingestion and routing, then triggering Kestra flows for downstream processing, quality checks, and loading.

Deployment Considerations

Resource Requirements

All three platforms are Java-based and require a minimum of 4 GB RAM for single-node deployments. For production use:

• NiFi: 8+ GB RAM, especially when handling large flowfiles. Content and provenance repositories benefit significantly from fast SSDs.
• StreamPipes: 4-8 GB RAM. The CouchDB backing store and ActiveMQ broker add overhead but are lightweight at moderate data volumes.
• Kestra: 4-8 GB RAM. PostgreSQL is the primary resource consumer; the Kestra server itself is relatively lightweight.

Persistence and Backup

Each platform stores critical state that must be backed up:

• NiFi: Back up the flow.json.gz configuration file plus all repository volumes. The content repository can be recreated, but the provenance and flowfile repositories should be preserved for disaster recovery.
• StreamPipes: The CouchDB volume (streampipes-data) contains all pipeline definitions and user data. ActiveMQ state is ephemeral.
• Kestra: The PostgreSQL database contains all flow definitions, execution history, and plugin state. The local storage volume holds task artifacts.

Scaling Patterns

• NiFi scales via clustering with ZooKeeper coordination. Each node in the cluster processes a subset of the flow, and load balancing is automatic.
• StreamPipes is primarily designed for single-node or small-cluster deployments. Horizontal scaling is limited.
• Kestra supports multi-node standalone mode where multiple server instances share a PostgreSQL database and local storage (via NFS or cloud storage). For high-throughput workloads, Kestra Enterprise adds worker pools and Redis-based queuing.

FAQ

Can Apache NiFi replace commercial ETL tools like Informatica?

For many use cases, yes. NiFi’s 400+ processors cover the same connectors as commercial ETL platforms, and its visual interface provides a comparable user experience. However, NiFi lacks built-in data quality profiling and metadata management features that enterprise platforms include. Teams can complement NiFi with dedicated data quality tools to fill this gap.

Is Kestra suitable for real-time streaming data?

Kestra is primarily designed for batch and event-driven workflows rather than continuous streaming. It can process streaming data by triggering flows on events (Kafka messages, file arrivals, webhook calls), but it does not provide the sub-second, continuous processing model that Apache Flink or Kafka Streams offer. For true streaming workloads, pair Kestra with a streaming engine.

Does Apache StreamPipes support custom data source plugins?

Yes. StreamPipes provides a plugin SDK (Java-based) that allows you to create custom source adapters, processors, and sinks. The development process is documented in the official StreamPipes developer guide, and plugins can be deployed alongside the main StreamPipes container.

How do these platforms handle data transformation?

NiFi uses processors like ReplaceText, JoltTransformJSON, and ExecuteScript (supporting Groovy, Python, and JavaScript) for inline transformations. StreamPipes offers built-in processors for common operations (filtering, aggregation, mapping) and a custom rule editor. Kestra supports transformations through its plugin ecosystem – you can use Python, JavaScript, SQL, or any language supported by a Kestra task plugin.

Which platform has the best community support?

Kestra currently has the largest and most active community, reflected in its 26,000+ GitHub stars and frequent release cadence. Apache NiFi has a long-established community with extensive documentation and a large installed base in enterprise environments. StreamPipes has a smaller but focused community centered around IoT and industrial use cases.

Can I run multiple platforms side by side?

Absolutely. A common architecture uses NiFi for data ingestion from diverse sources, Kestra for orchestrating downstream ETL and quality workflows, and StreamPipes for real-time IoT analytics. Each runs in its own Docker Compose stack with dedicated resources, and they communicate via Kafka, REST APIs, or shared object storage.

For related reading, see our Meltano vs Airbyte vs Singer data pipeline guide for ELT-focused alternatives, the Apache Flink vs Bytewax vs Beam stream processing guide for real-time computation engines, and our Debezium vs Maxwell vs Canal CDC guide for database change data capture.