Archive for October, 2023

Using IBM Event Automation with Amazon MSK

Wednesday, October 25th, 2023

Written with Chris Patmore

IBM Event Automation helps companies to accelerate their event-driven projects wherever businesses are on their journey. It provides multiple components (Event Streams, Event Endpoint Management, and Event Processing) which together lay the foundation of an event-driven architecture that can unlock the value of the streams of events that businesses have.

A key goal of Event Automation is to be composable. The three components can be used together, or they can each be used to extend and enhance an existing event-driven deployment.

Amazon MSK (Managed Streaming for Kafka) is a hosted, managed Kafka service available in Amazon Web Services. If a business has started their event-driven journey using MSK, then components from Event Automation can help to enhance this. This could be by offering management and governance of their MSK topics. And it could be by providing an intuitive low-code authoring canvas to process the events on their MSK topics.

Working with Amazon MSK is a nice example of the benefits of the composability of Event Automation, by helping businesses to get more value from their existing MSK topics.

In this blog post, we want to show a few different examples of where this can be done. For each example, we’ll provide a high-level diagram and description. We’ll also share a demonstration that we created to show it in action.

(more…)

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Connecting App Connect Enterprise to Event Endpoint Management

Friday, October 20th, 2023

Configuring IBM App Connect Enterprise to consume messages from Kafka topics in IBM Event Endpoint Management requires careful configuration. In this post, I’ll share the steps I use that help me to avoid missing any required values.

If this sounds familiar, it might be because I wrote a post like this about using App Connect Enterprise to work with topics from Event Streams. People seem to have found that post useful, so I thought I’d do something similar for topics in Event Endpoint Management this time.

To illustrate this, I’ll create a simple App Connect flow that consumes messages from a Kafka topic and publish them to an MQTT topic.

The key to getting this to work correctly first time is to make sure that values are accurately copied from Event Endpoint Management to App Connect.

To help with this, I use a grid like the one below.

The instructions in this post start with Event Endpoint Management, and explain how to populate the grid with the information you need.

Then the instructions will switch to App Connect, and explain how to use the values in the grid to set up your App Connect flow.

What this is Values you will see in my screenshots Your value
A Topic name
DEMO.ACE
B Bootstrap address
my-eem-gateway-ibm-egw-rt-event-automation.apps.dalelane.cp.fyre.ibm.com:443
C SASL mechanism
PLAIN
D SASL config
org.apache.kafka.common.security.plain.PlainLoginModule required;
E Security protocol
SASL_SSL
F Certificate
eem-cert.jks
G Certificate password
STOREPASSW0RD
H Username
eem-9c8fc5d9-fddd-48dd-ab41-e062214166e5
I Password
dd08a1fc-99be-4931-8059-70aef88c1f0c
J Policy project name
demo-policies
K Policy name
demo-eem-policy
L Security identity name
eem-credentials
M Truststore identity name
eem-truststore

(more…)

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Understanding windows in Event Processing

Wednesday, October 11th, 2023

Event Processing (one of the new capabilities of IBM Event Automation) makes it easy to perform stateful processing of streams of events from Kafka topics. In this post, I want to drill down a little into how windowed processing behaves.

I’ve enjoyed the chance to see the solutions that people have started to build with the tool. As part of this, I’ve been helping several people to understand the results produced by the event processing flows they’ve made.

These often started with different questions, such as:

  • Why hasn’t my flow produced any results?
  • Why isn’t my one-minute window producing one result every minute?
  • Why did the last one event on my Kafka topic cause results to be produced for several different windows?
  • etc.

However, these are often symptoms of a single common question: how windowed processing operations behave.

I’ve tried to come up with simple ways to demonstrate how it works, so in this post I want to share how I’m currently explaining it.


youtu.be/x_r6GNZmsd4

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