Apache NiFi

Apache NiFi

Apache NiFi is an open source automated data flow tool for managing the movement of data between disparate systems. It provides a visual command and control capability to allow you to easily build dataflows to ingest, curate, analyze and act on data.
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data-flow etl ingestion curation automation

Apache NiFi: Open Source Automated Data Flow Tool

Apache NiFi is an open source automated data flow tool for managing the movement of data between disparate systems. It provides a visual command and control capability to allow you to easily build dataflows to ingest, curate, analyze and act on data.

What is Apache NiFi?

Apache NiFi is an open source software project from the Apache Software Foundation designed to automate and manage the flow of data between software systems. NiFi provides an easy-to-use, drag-and-drop graphical user interface that allows users to build flexible and scalable dataflows to ingest, process, distribute and act on streaming data across an enterprise.

Some key capabilities and benefits of Apache NiFi include:

  • Guaranteed data delivery via resilient dataflows
  • Web-based user interface provides a central control system
  • Real-time operational feedback and control of dataflows
  • Data provenance to track flowfiles from start to finish through a dataflow
  • Designed for extension and integration with other software and systems
  • Integrated security, flexible scaling, and failover/redundancy features
  • Extensible architecture plugs in additional functionality through extensions

Apache NiFi allows organizations to build automated and managed dataflows between traditional business systems like ERPs and CRMs as well as more modern systems and data sources including IoT devices, clickstreams, social feeds and more. Its flexibility and extensibility make it suitable for a wide variety of data flow use cases.

Apache NiFi Features

Features

  1. Visual command & control interface for creating dataflows
  2. Real-time operational monitoring
  3. Provenance for tracking dataflow execution history & lineage
  4. Flexible scaling model to meet different workload demands
  5. Extensible through custom processors & controller services
  6. Secure through role-based access control, encryption, etc.

Pricing

  • Open Source

Pros

Open source with large community support

Robust set of out-of-the-box processors

Web-based UI simplifies building complex dataflows

Good for streaming & batch data processing

Integrates with many data sources & technologies

Cons

Steep learning curve

Not optimized for interactive/ad-hoc queries

Limited native support for machine learning workflows


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