Packt Introduction to Apache NiFi (Hortonworks DataFlow HDF 2 0) XQZT

Packt Introduction to Apache NiFi (Hortonworks DataFlow HDF 2 0)-XQZT
English | Size: 2.00 GB
Category: CBTs
Apache NiFi was initially used by the NSA so they could move data at scale and was then open sourced. Being such a hot technology, Onyara (the company behind it) was then acquired by Hortonworks, one of the main backers of the big data project Hadoop, and then Hadoop Data Platform. Apache NiFi is now used in many top organisations that want to harness the power of their fast data by sourcing and transferring information from and to their database and big data lakes. It is a key tool to learn for the analyst and data scientists alike. Its simplicity and drag and drop interface make it a breeze to use! You can start building flows between Kafka and ElasticSearch, an FT,P and MongoDB, and so much more! Your imagination is the limit This course will take you through the Apache NiFi technology. It will help you understand its fundamental concepts, with theory lessons that walk you through the core concepts of Apache NiFi. You will also have hands-on labs to get started and build your first data flows. You will learn how to set up your connectors, processors, and how to read your FlowFiles to make the most of what NiFi has to offer. The most important configuration options will be demonstrated so you will be able to get started in no time. We will also analyse a template picked from the web and understand how to debug your flows as well as route your data to different processors based on outcomes through relationships. We will finally learn about the integrations between NiFi and Apache Kafka or MongoDB. Lots of learning ahead!

What You Will Learn:

Install and configure Apache NiFi.
Design Apache NiFi architecture.
Master core functionalities like FlowFile, FlowFile processor, connection, flow controller, process groups, and so on.
Use NiFi to stream data between different systems at scale.
Monitor Apache NiFi.
Integrate NiFi with Apache Kafka

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