As you may already realise, Kafka is not just a fancy message bus, or a pipe for big data. It’s an event streaming platform! If this is news to you, I’ll wait here whilst you read this or watch this…

Streaming data from Kafka to Elasticsearch is easy with Kafka Connect - you can see how in this tutorial and video.

One of the things that sometimes causes issues though is how to get location data correctly indexed into Elasticsearch as geo_point fields to enable all that lovely location analysis. Unlike data types like dates and numerics, Elasticsearch’s Dynamic Field Mapping won’t automagically pick up geo_point data, and so you have to do two things:

Let’s imagine we have XML data on a queue in IBM MQ, and we want to ingest it into Kafka to then use downstream, perhaps in an application or maybe to stream to a NoSQL store like MongoDB.

👉 Ingesting XML data into Kafka - Introduction

We saw in the first post how to hack together an ingestion pipeline for XML into Kafka using a source such as curl piped through xq to wrangle the XML and stream it into Kafka using kafkacat, optionally using ksqlDB to apply and register a schema for it.

The second one showed the use of any Kafka Connect source connector plus the kafka-connect-transform-xml Single Message Transformation. Now we’re going to take a look at a source connector from the community that can also be used to ingest XML data into Kafka.

We previously looked at the background to getting XML into Kafka, and potentially how [not] to do it. Now let’s look at the proper way to build a streaming ingestion pipeline for XML into Kafka, using Kafka Connect.

If you’re unfamiliar with Kafka Connect, check out this quick intro to Kafka Connect here. Kafka Connect’s excellent plugable architecture means that we can pair any source connector to read XML from wherever we have it (for example, a flat file, or a MQ, or anywhere else), with a Single Message Transform to transform the XML into a payload with a schema, and finally a converter to serialise the data in a form that we would like to use such as Avro or Protobuf.

Kafka Connect (which is part of Apache Kafka) supports pluggable connectors, enabling you to stream data between Kafka and numerous types of system, including to mention just a few:

The appropriate plugin for the technology which you want to integrate can be found on Confluent Hub.

For whatever reason, CSV still exists as a ubiquitous data interchange format. It doesn’t get much simpler: chuck some plaintext with fields separated by commas into a file and stick .csv on the end. If you’re feeling helpful you can include a header row with field names in.

order_id,customer_id,order_total_usd,make,model,delivery_city,delivery_company,delivery_address
1,535,190899.73,Dodge,Ram Wagon B350,Sheffield,DuBuque LLC,2810 Northland Avenue
2,671,33245.53,Volkswagen,Cabriolet,Edinburgh,Bechtelar-VonRueden,1 Macpherson Crossing

In this article we’ll see how to load this CSV data into Kafka, without even needing to write any code

I wanted to get some data from a Kafka topic:

into Postgres, so did the easy thing and used Kafka Connect with the JDBC Sink connector.

ksqlDB 0.7 will add support for message keys as primitive data types beyond just STRING (which is all we’ve had to date). That means that Kafka messages are going to be much easier to work with, and require less wrangling to get into the form in which you need them. Take an example of a database table that you’ve ingested into a Kafka topic, and want to join to a stream of events. Previously you’d have had to take the Kafka topic into which the table had been ingested and run a ksqlDB processor to re-key the messages such that ksqlDB could join on them. Friends, I am here to tell you that this is no longer needed!