Apache NiFi Training Course 

(up to 4 days: 3 days for Administrator + 1 day for Developers) 

Note: this outline is our proposal, but the training can be tailored to your specific requirements upon prior request ahead of the proposed course date.

Why Learn Apache NiFi?

Apache NiFi is an open source platform for automating and managing data flow between systems. It is an efficient and reliable data processing and delivery system. It offers a web-based user interface for building, tracking , and managing data flows. It has a fully customizable and configurable data flow mechanism that can change data during runtime. It is easily extensible by designing custom components.

At the end of this training, you will be able to:


  • Install and configure Apachi NiFi

  • Source, transform and manage data

  • Automate dataflows

  • Enable streaming analytics

  • Apply various approaches for data ingestion

  • Transform Big Data


  • Understand NiFi's architecture and dataflow concepts

  • Develop extensions using NiFi and third-party APIs

  • Custom develop their own Apache Nifi processor

  • Ingest and process real-time data from disparate and uncommon file formats and data sources



Course details

The agenda covers both fundamentals and advanced topics.

The final training outline will be designed depending on your particular requirements.

The practical exercises constitute a big part of the course time, besides demonstrations and theoretical presentations. Discussions and questions can be asked throughout the course.


Course Outline

1. Administrators:

Introduction to Apache NiFi

  • Data at rest vs data in motion


Overview of Big Data and Apache Hadoop

  • HDFS and MapReduce architecture


Setting up and Running a NiFi Cluster

  • Cluster Integration

  • Load Balancing/Redundancy

  • Mass Orchestration of NiFi (via Ansible)


NiFi Operations

  • Database Aggregating, Splitting and Transforming

  • Data Extractions, Logging, etc.

  • Integrating with Splunk (optional)


Monitoring and Recovery

  • Recovering without Data Loss

  • Autonomous Recovery


Optimizing NiFI

  • Performance tuning

  • Optimizing Nifi Setup


Best practices




Summary and Conclusion

2. Developers:


  • Data at rest vs data in motion


Overview of Big Data Tools and Technologies

  • Hadoop (HDFS and MapReduce) and Spark


Installing and Configuring NiFi


Overview of NiFi Architecture


Development Approaches

  • Application development tools and mindset

  • Extract, Transform, and Load (ETL) tools and mindset


Design Considerations


Components, Events, and Processor Patterns


Exercise: Streaming Data Feeds into HDFS


Error Handling


Controller Services


Exercise: Ingesting Data from IoT Devices using Web-Based APIs


Exercise: Developing a Custom Apache Nifi Processor using JSON


Testing and Troubleshooting


Contributing to Apache NiFi


Summary and Conclusion