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:

​

Administrators

​

  • Install and configure Apachi NiFi

  • Source, transform and manage data

  • Automate dataflows

  • Enable streaming analytics

  • Apply various approaches for data ingestion

  • Transform Big Data

​

Developers

​

  • 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

 

Troubleshooting

 

Summary and Conclusion

​

​

2. Developers:

​

Introduction

​

  • 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

​

​