Optimize your data flows with Apache NiFi: a powerful open-source platform for collecting, transforming, and routing your data in real time.
Today, data flows between various systems. The ability to reliably and securely collect, transform, and route this data is essential. Apache NiFi, an open-source project from the Apache Foundation, stands out as a key tool for designing, executing, and monitoring complex data flows in real time.
Apache NiFi what is it?
NiFi is a data flow management platform designed to move and transform data between heterogeneous systems. With a low-code graphical interface, users can build complex data pipelines without writing code, while benefiting from fine-tuned performance management, security, and error recovery.
Low-code… but for technicians
While NiFi offers a visual design interface that allows users to model data flows using drag & drop, it doesn’t mean it’s entirely accessible to non-technical users.
In practice, configuring many processors requires:
- SQL skills for database interactions.
- Proficiency in transformation languages like Jolt for manipulating JSON.
- A solid understanding of data formats (Avro, Parquet, CSV, JSON).
- Scripting knowledge (Groovy, Python) for advanced transformations.
The graphical approach simplifies flow construction and readability, but technical expertise remains essential to build robust, high-performance, and scalable pipelines.
NiFi is therefore primarily designed for Data Engineers, DataOps, and Data Architects, while offering comprehensive documentation and an ecosystem that facilitate onboarding.
Typical use cases
- Multi-source data ingestion: Databases, APIs, files, IoT.
- Real-time data transformation: Enrichment, normalization, aggregation.
- Conditional routing: Delivery to data warehouses, data lakes, analytical services.
- Comprehensive monitoring and traceability: NiFi tracks every event end-to-end.
Architecture and key components
1. FlowFiles
Every piece of data moving through NiFi is encapsulated in a FlowFile, containing both the payload (content) and associated metadata (attributes), enabling granular event tracking.
2. Processors
NiFi offers over 300 native processors (connectors) to:
- Connect to SQL/NoSQL databases,
- Consume Kafka messages,
- Interact with cloud services (Azure Blob Storage, S3, GCS),
- Transform, parse, and enrich data.
3. Flow Controller
The Flow Controller orchestrates flow execution, ensures state persistence, and coordinates interactions between processors.
4. Repositories
- Content Repository: Temporarily stores raw data.
- FlowFile Repository: Stores metadata of FlowFiles.
- Provenance Repository: Maintains a complete history of each FlowFile, ensuring full traceability.
5. Cluster and scalability
NiFi can be deployed in standalone mode or as a cluster to handle massive data volumes. Each cluster node executes part of the flows, with centralized coordination.
NiFi workflow example for retail
Let’s consider a retailer looking to collect real-time transaction data from its points of sale, enrich it with product information, and send it to an Azure Data Lake for analysis.
A NiFi data flow for this use case could look like this:
- Ingestion: A processor consumes Kafka messages containing transaction data.
- Enrichment: Another processor queries a PostgreSQL database to fetch associated product details.
- Transformation: Data is formatted into Parquet to optimize storage in Azure Data Lake.
- Conditional Routing: Transactions are sent to different Azure Blob Storage containers based on region or product type.
- Monitoring & Alerting: In case of an error, a Slack or Teams notification is sent to the DataOps team.
All of this is designed via drag & drop in NiFi’s interface, with visual tracking of every data movement.
Key benefits for CIOs and Data Architects
1. Low-code and team autonomy
NiFi’s graphical interface allows DataOps and business teams to build and modify data flows without relying on developers, accelerating development cycles.
2. Security and compliance
- Granular access control (RBAC) via Apache Ranger or NiFi’s built-in mechanisms.
- Native encryption for data in transit and at rest.
- Full traceability ensured by the Provenance Repository.
3. Flexibility and connectivity
- Over 300 native connectors, including databases, messaging systems (Kafka, JMS), cloud storage, and REST APIs.
- Supports both batch and streaming workflows, making it ideal for hybrid architectures.
4. Controlled scalability
- Deployable in standalone mode or as a cluster.
- Suitable for on-premises, cloud (Azure, AWS, GCP), or hybrid environments.
Comparison with an orchestrator (ex : Kestra)
Unlike an orchestrator like Kestra, which focuses on task sequencing and supervision, NiFi manages the continuous flow of data between systems, with a strong emphasis on real-time transformation and individual data traceability. Both tools are often complementary in a modern data architecture, with Kestra handling workflow orchestration and NiFi ensuring seamless data movement and transformation.
Why choose Apache NiFi?
- Intuitive and accessible graphical design for easy flow creation.
- End-to-end traceability of every data event.
- Extensive library of ready-to-use connectors.
- Supports both streaming and batch pipelines.
- Flexible deployment: standalone, cluster, cloud, Kubernetes.
- Native integration with Apache Kafka, Hadoop, Spark, and cloud data lakes (S3, ADLS, GCS).
Beyond NiFi: MiNiFi - the lifhtweight extension for distributed data collection
MiNiFi (Minimal NiFi) is a lightweight version of Apache NiFi, designed to deploy collection agents directly closer to data sources. While NiFi is designed as a centralized platform for orchestrating and transforming data flows, MiNiFi acts as a distributed collector, capable of running on IoT devices, edge servers, or resource-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 that:
- Collects local metrics (temperature, vibration, consumption).
- Applies initial transformations (filtering, enrichment).
- Sends data to a central NiFi cluster for more complex processing and aggregation.
Key points
Ultra-lightweight: Designed to run in resource-constrained environments.
Automatable deployment: Flows are defined in NiFi and then deployed to MiNiFi.
Fully compatible with NiFi: MiNiFi agents send data to a central NiFi instance, ensuring end-to-end traceability.
Supports standard protocols: MQTT, HTTP, files, syslog, etc.
MiNiFi extends NiFi’s capabilities to Edge and IoT architectures, enabling the construction of hybrid pipelines, with data collection close to the source and centralized processing in the company’s data lake or data hub.
Conclusion
Whether you are an IT department looking for a reliable solution to streamline your data flows or a DataOps team seeking agility and autonomy, Apache NiFi addresses your challenges in data collection, transformation, routing, and monitoring, while ensuring security, performance, and observability.
In today’s modern data landscape, where data flows are becoming increasingly complex, NiFi is a key building block of any Data and AI architecture.
