Optimize your data flows with Apache NiFi: a powerful open source platform to collect, transform and route your data in real time.
Apache NiFi: Manage your data streams
Apache NiFi is an open-source platform for managing real-time data streams. Designed for Data Engineers and DataOps teams, this low-code solution allows you to collect, transform, and route data between heterogeneous systems—without writing a single line of code in most common scenarios.
By 2025, more than 400 native processors are available, and the active Apache project community ensures regular updates and a constantly evolving ecosystem.
What exactly is Apache NiFi?
NiFi is a data flow management platform designed to move and transform data between heterogeneous systems. Through a low-code graphical interface, users assemble complex pipelines without writing code, while benefiting from granular control over performance, security, and error recovery.
Apache NiFi is open-source software maintained by the Apache Foundation. Its source code is freely accessible, which promotes transparency, auditability, and community contributions.
Key takeaway : Apache NiFi is an open source data streaming platform, accessible via a low-code interface, suitable for modern Data and AI architectures.
Low-code… but for technicians
Although NiFi offers a graphical design interface that allows for visual flow modeling (drag & drop), it is not a tool accessible to non-technical users.
In practice, configuring many processors requires:
- SQL skills for interacting with databases,
- proficiency in transformation languages like Jolt for manipulating JSON,
- a good understanding of data formats (Avro, Parquet, CSV, JSON),
- Some scripting skills (Groovy, Python) for certain advanced transformations.
The graphical approach simplifies the construction and readability of workflows, but technical expertise remains essential to build robust, efficient, and scalable pipelines.
NiFi is therefore primarily aimed at Data Engineers, DataOps and Data Architects, while offering comprehensive documentation and starting points that facilitate initial setup.
Key takeaway : NiFi is a low-code approach geared towards technicians. The graphical interface accelerates low-code development, but does not replace data expertise.
Typical use cases
- Multi-source ingestion : databases, APIs, files, IoT.
- On-the-fly transformation : enrichment, normalization, aggregation.
- Conditional routing : routing to warehouses, data lakes, analytical services.
- Monitoring and traceability : NiFi tracks every event from end to end, ensuring complete observability of your business processes.
Architecture and key components
1. FlowFiles
Each piece of data flowing through NiFi is encapsulated in a FlowFile , containing the payload and associated metadata (attributes). This allows for granular tracking of each event in your data flow.
2. Processors
NiFi offers over 400 native processors (data source, connectors, transformers) enabling:
- connecting to SQL/NoSQL databases.
- consume Kafkaesque messages.
- interact with cloud services (Azure Blob Storage, S3, GCS).
- transform, parse and enrich the data.
- These processors form the starting points of any NiFi pipeline, and the active community regularly releases new connectors.
3. Flow Controller
The Flow Controller orchestrates the execution of flows, ensures state persistence, and coordinates interactions between processors. It is at the heart of the pipeline development and implementation process.
4. Repositories
- Content Repository : temporarily stores raw data.
- FlowFile Repository : stores the metadata of the FlowFiles.
- Provenance Repository : retains the complete history of each FlowFile, ensuring total traceability, essential for compliance and auditing.
5. Cluster and Scalability
NiFi can be deployed in standalone mode or in a cluster to handle massive volumes. Each node executes a portion of the streams, with centralized coordination.
Key takeaway : NiFi architecture is based on traceable FlowFiles, more than 400 native processors and flexible standalone or cluster deployment.
Example of a NiFi workflow for retail
Let's take the example of a retailer wishing to collect real-time transactions from its points of sale, enrich them with product information and send them to its Azure data lake for analysis.
The NiFi feed might look like this:
- Ingestion : a processor consumes Kafka messages containing transactions.
- Enrichment : a processor queries a PostgreSQL database to retrieve associated product information.
- Transformation : Parquet formatting to optimize storage in Azure Data Lake.
- Conditional routing : sending to different Azure Blob containers depending on the region or product type.
- Monitoring and alerting : in case of error, a Slack or Teams message is sent to the DataOps team.
The entire system is designed using drag and drop via the NiFi interface, with visual tracking of each piece of data transmitted, illustrating how NiFi accelerates the rapid development of data applications without sacrificing robustness.
Key takeaway : NiFi reduces data development cycles in retail by centralizing ingestion, transformation, routing and alerting in a single, visual workflow.
Advantages for CIOs and Data Architects
1. Low-code and team autonomy
NiFi's graphical interface allows DataOps and Business teams to build and modify flows without relying on developers, accelerating development cycles and reducing technical debt.
2. Safety and compliance
- Fine-grained access control (RBAC) via Apache Ranger or NiFi's internal mechanism.
- Native encryption of data in transit and at rest.
- Complete traceability thanks to the Provenance Repository.
3. Flexibility and connectivity
- Over 400 native connectors: databases, messaging (Kafka, JMS), cloud storage, REST APIs.
- Supports batch and streaming flows, ideal for hybrid architectures.
4. Controlled Scalability
- Standalone or cluster deployment .
- Suitable for on-prem , cloud (Azure, AWS , GCP) or hybrid environments .
Key takeaway : NiFi combines low-code autonomy, enterprise-grade security and over 400 native connectors, an open source platform designed for demanding data environments.
Comparison with an Orchestrator (e.g., Kestra)
Unlike an orchestrator like Kestra , which manages the sequencing and supervision of tasks, Apache NiFi manages the continuous flow of data between systems, with a strong emphasis on real-time transformation and traceability of individual data.
The two tools are often complementary in a modern data architecture: NiFi for data transport and transformation, an orchestrator for coordinating business processes and multi-stage workflows.
Why choose Apache NiFi?
- Intuitive and accessible graphic design .
- End-to-end traceability of each event .
- Large library of ready-to-use connectors .
- Suitable for streaming and batch pipelines .
- Flexible deployment: standalone, cluster, cloud, Kubernetes .
- Native integration with Apache Kafka, Hadoop , Spark and cloud data lakes (S3, ADLS, GCS) .
There's NiFi, but also MiNiFi: The lightweight extension for distributed collection
MiNiFi (Minimal NiFi) is a lightweight version of Apache NiFi, designed to deploy collection agents directly as close as possible to the data sources. Where NiFi is conceived as a centralized platform to orchestrate and transform data streams, MiNiFi acts as a distributed collector , capable of running on IoT devices , edge servers, or constrained environments (low CPU/RAM).
Typical use case
In an industrial monitoring or IoT scenario, each sensor or machine can be equipped with a MiNiFi agent which:
- Collects local metrics (temperature, vibration, consumption).
- Applies initial simple transformations (filtering, enrichment).
- Sends the data to a central NiFi cluster for more complex processing and aggregation.
Key points
- Ultra-lightweight : designed to operate in confined environments.
- Automatable deployment : the flows are defined in NiFi and then deployed to MiNiFi.
- Compatible with NiFi : MiNiFi agents send their data to a central NiFi, ensuring end-to-end traceability.
- Supports classic protocols : MQTT, HTTP, files, syslog, etc.
MiNiFi therefore extends NiFi's capabilities to Edge and IoT architectures, enabling the construction of hybrid pipelines , with collection as close as possible to the sources and centralized processing in the company's data lake or data hub.
