Data In Motion: flume

Migrating Apache Flume Flows to Apache NiFi: JMS To/From Anywhere

Article 8 - This - JMS

Article 7 - https://www.datainmotion.dev/2019/10/migrating-apache-flume-flows-to-apache_15.html

Article 6 - https://www.datainmotion.dev/2019/10/migrating-apache-flume-flows-to-apache_9.html

Article 5 - https://www.datainmotion.dev/2019/10/migrating-apache-flume-flows-to-apache_35.html

Article 4 - https://www.datainmotion.dev/2019/10/migrating-apache-flume-flows-to-apache_8.html

Article 3 - https://www.datainmotion.dev/2019/10/migrating-apache-flume-flows-to-apache_7.html

Article 2 - https://www.datainmotion.dev/2019/10/migrating-apache-flume-flows-to-apache.html

Article 1 - https://www.datainmotion.dev/2019/08/migrating-apache-flume-flows-to-apache.html

Source Code: https://github.com/tspannhw/flume-to-nifi

This is a simple use case of being a gateway between JMS and other sources and sinks. We can do a lot more than that in NiFi. We can be a JMS Consumer or Producer. All with No Code. We can work with topics and queues and any message types you have. We can turn tabular messages (JSON, CSV, XML, AVRO, Parquet, Grokable Text) into Records and process them at speed with queries, updates, merging and fast record processing that is schema aware. So we know your fields and types and can validate those for you while real-time querying that data as it is sent from and to JMS topics and queues with Apache Calcite SQL. We can store your schemas in our Cloudera Schema Registry and allow for REST API access to them. Schemas are accessible from Spark, Flink, Kafka, NiFi and more.

It is extremely easy to do this in NiFi.

In our example we are using Apache ActiveMQ 5.15 as our example JMS Broker. We are grabbing example data from a few different REST sources and pushing to and from our JMS broker.