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In an event-driven architecture, event routers are the components that connect event consumers to event producers. Not all implementations of event routers are the same, nor do any of them offer an all-purpose solution, so deciding which one to use depends on your use case and project's needs. Understanding their capabilities and limitations provides key insights that empower you to confidently decide which one to use and prepare you to navigate its shortcomings.

When you’re one of many developers commanding streaming applications running in Apache Kafka, you want enough data observability to fly your own data product to the moon. But you also want to boldly go where no developer has gone before to discover new applications. At the same time, you don’t want to be exposed to sensitive data that summons you to your compliance team, crashing you back down to earth.

You’ve been handed the not-so-easy task of scoping a managed Kafka for your team. How do you start the shortlist? Post something on Reddit? Skim read a gazillion review blogs? Crash Google Chrome opening a thousand tabs to compare feature lists? If you’re going to run a Kafka POC with two or three vendors, or you’re trying to find the best Kafka for your business, how can you narrow down your selection? Let’s get to it.

Once upon a time (2017), in an office far far away, you may have been cornered in a conversation with someone from Legal about GDPR. It could have gone something like this: “You there, Data Engineer” “Yep, that’s me” “What PII do we have residing in this Apache Kafka database?” You probably mumbled something about Kafka not being a database. “And who can read/ write the data?

A decade ago, all developers could talk about was breaking down the monolith and event-driven architectures. Especially in the financial services industry, to become more nimble and accelerate their application delivery. They leveraged messaging systems to decouple the application, and specifically Apache Kafka has transitioned from being a data integration technology to the leading messaging system for microservices.

2021 is the year data engineers shift from analytics support to governing data flows. As data becomes an organization's most valuable commodity, it is those with the governance to confidently analyze and act on it that will develop the next big applications.

Splunk is a technology that made processing huge volumes and complex datasets accessible to security and IT teams. Despite its strengths for monitoring and investigation, Splunk is a bit of a one-way street. Once it's in Splunk, it's not that easy to stream the data elsewhere in great volume. And it doesn’t mean it’s the best technology for all IT and Security use cases. Or the cheapest.

In the previous posts in this series, we have discussed Kerberos and LDAP authentication for Kafka. In this post, we will look into how to configure a Kafka cluster to use a PAM backend instead of an LDAP one. The examples shown here will highlight the authentication-related properties in bold font to differentiate them from other required security properties, as in the example below. TLS is assumed to be enabled for the Apache Kafka cluster, as it should be for every secure cluster.