Believe it or not, we are getting to the end of this small series about a potential real-time data processing pipeline. In this final part I will show how Grafana can retrieve our pipeline data from PostgreSQL and visualize it as a graph. But before we dive into it, let’s have a quick recap of the previous topics.
As mentioned in the post related to ActiveMQ, Spark and Bahir, Spark does not provide a JDBC sink out of the box. Therefore, I will have to use the foreach sink and implement an extension of the org.apache.spark.sql.ForeachWriter. It will take each individual data row and write it to PostgreSQL.
Even though I want to use PostgreSQL, I am actually
As a quick reminder, here is the Scala code that I have used so far to retrieve the data from ActiveMQ and write it to a memory sink.
// create a named session
val spark = SparkSession
// read data from the OscStream topic
val mqttDf =
Welcome back to the second part of my series, showcasing a real-time data processing pipeline! In part 1, I explored visual real-time sensor data simulation, as the entry point into our pipeline. Now it’s time to find out, how we can get the generated data into Apache ActiveMQ, by transferring it via the OSC protocol.
Apache ActiveMQ™ is the most popular open source, multi-protocol, Java-based messaging server. It supports a variety of Cross Language Clients and Protocols, and therefore makes it an excellent choice for our pipeline.