- 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
- Application development tools and mindset
- Extract, Transform, and Load (ETL) tools and mindset
Components, Events, and Processor Patterns
Exercise: Streaming Data Feeds into HDFS
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
- Java programming experience.
- Experience with Maven.
- Data engineers
Virtual environment working well and trainer positive attitude
Wojciech Lukawski - Orsted Polska sp. z o.o.
I liked trainer's attitude and choice of examples. Trainer was very willing to help and answer questions. Trainer tried to go with as many examples as possible, even though we were short on time.
Waldemar Sobiecki - Orsted Polska sp. z o.o.
Excersises, working with actual nifi. It was working very well - working on live, virtual machines.
Orsted Polska sp. z o.o.
The trainer is very polite, tolerant, helpful. I was not afraid to ask for help when I couldn't handle something myself. I like that most of the exercises we did together without splitting into separate groups or by ourselves. That way we could ask questions on regular basis.
Elżbieta Doniek - Orsted Polska sp. z o.o.
Very practical, a lot of exercises during the training
Andrii Feshchenko - Orsted Polska sp. z o.o.
Developing a Custom Apache Nifi Processor using JSON provided a useful demostration of how we can use NiFi to transform data before forwarding to our analytical tools.
Kenny MacLeod - MOD A BLOCK
James answered my every question, was extremely patient and explained me everything. NIFI was Greek and latin for me and i have learnt what was a processor, flunnel and the root process from Beginner level to Advanced Level.
Firdous Hashim Ali - MOD A BLOCK
That I had it in the first place.
Peter Scales - CACI Ltd
Work exercises with cluster to see performance of nodes across cluster and extended functionality