How to Get Your Organization to Appreciate Apache Kafka

This article was originally posted on the Confluent blog.

If you want to enable your organization to leverage the full value of event-driven architectures, it is not enough to just integrate Apache Kafka® and wait for people to join the party.

Written by Patrick Bönzli

Introduction

Why Kafka?

Why Kafka is relevant to your organisation

So event-driven architecture to the rescue? At least on paper. In reality, you can only address these pains if there are enough people in your organization willing to adopt Kafka. For example, the value of decoupling in the overall architecture is only significant if existing and new interfaces use event-driven architectures. The same applies to data reuse; the probability of data reuse grows as the amount of data and the number of potential consumers increase.

Why your organisation is relevant to Kafka

So the real challenge for Kafka is to win a majority inside an enterprise that can overcome architectural inertia. These people will be your Kafka fans, and they will be the reason that you can spend more time building your own Kafka fun park for and with them.

Understanding the reasons behind inertia

Thus, the challenge is a technology diffusion problem. That means, no matter how many millions of messages your Kafka cluster processes, no matter how well your cluster is secured, no matter how much you invested to make writing Kafka applications easy — it will not matter unless people understand why Kafka is important to them.

You can’t solve inertia problems with technology only, because inertia heavily relies on human capital.

So who are the people involved and how do you accelerate Kafka diffusion inside of your company? In 1962, sociologist Everett Rogers came up with a theory of the “diffusion of innovations,” which was later picked up in the domain of sales/marketing by Geoffrey Moore. You may recognize Moore from the book he authored called “Crossing the Chasm.” Rogers and Moore realized that the natural reaction of people toward new technology (or innovation) can be categorized into five different segments: innovators, early adopters, early majority, late majority, and laggards. These observations relate to overall market behaviour, but it’s likely that you have a very similar distribution inside of your organization.

The technology adoption curve for Kafka inside an enterprise.

If you’re reading this blog post, chances are high you’re an innovator or early adopter. You love new technology and think about how it can change lives for the better. That also means you belong to the smaller part of your organization, about 20%. Your job now is to show the other 80% of your organization how Kafka can change their lives for the better. What makes it even more difficult is that every segment needs slightly different reasons to adopt new technology. While early adopters appreciate understanding the intrinsic details of Kafka internals, from brokers, partitions, and replicas to transport guarantees, the majority of enterprise engineers appreciate having a quick entry into the value that Kafka offers: sending, receiving, and sourcing events (stream processing will be a focus for another time ). The difference of perceived value is commonly referred to as the chasm, and it’s an angle that many technology enthusiasts are not aware of.

Addressing the Kafka early majority

Different perspectives between early adopters and early majority of Kafka illustrated.

The illustration above depicts the different levels of abstractions that early adopters like compared to the early majority. To go a step further, a business perspective has been added as well, which helps to remove some abstractions and focuses on data content, people, and their data use cases.

The ability to explain and use Kafka as a data subscription service is the key to onboarding your organization to Kafka. It allows you to elegantly explain the main architectural patterns of event-driven architectures, like decoupling, sourcing, and reuse, without going into technical details.

Winning fans among the early majority

Essentially you want to do two things: Accelerate diffusion by offering motivation and reduce elements that cause friction. In other words, you want to start showing off Kafka as a subscription service among the early majority and you want to start building this subscription service as a means of convenience.

It is important to do both at the same time, allowing you to incrementally onboard new users through your Kafka journey. This way, you can incorporate their expectations and make them participants in the journey rather than just observers. Make sure that what you are communicating can be understood by the early majority. The following steps are helpful when talking to people:

  • Share success stories
  • Educate about event-driven architecture and Kafka
  • Network and nudge

The strategy depends on whether you already have a few successful projects going or if you first need to prepare the stage for Kafka. Unfortunately, many engineers do not like these steps because they are not deemed “technical.”

While communication helps to slowly unfold Kafka’s value, you need to prepare the field for convenience. For most engineers, this is the best part because it actually involves engineering and product thinking:

  • Data transparency or “seeing is believing”
  • Self-service data and infrastructure access
  • Low-level API abstractions

The following explains these six strategies in more detail.

Share success stories and educate

Network and nudge people

At some point, when you have your first influencers interested, you need to start talking with others up the hierarchy ladder and test their awareness of event-driven architectures and how your organization can benefit from adopting them. This scenario resembles a 1:1 sales pitch, and while you might not like this step, consider it as a career-building opportunity.

Seeing is believing

Using these tools will force you to explain the technical details of Kafka rather than talk about the value that Kafka creates. In the same way, you don’t want to explain to someone the essentials of driving a car by showing off your car’s engine.

What you need is something easier, more elegant, and more abstract. You want to show data streams rather than topics and partitions on the first level. This is essentially what SPOUD built as a product and now offers to the world — a streaming data catalog for Kafka.

SPOUD Agoora: convenience through a data-centric view on Kafka

SPOUD Agoora is a SaaS product that easily connects to your cloud and on-premises Kafka through simple agents. Agoora targets organizations that want to add convenience to Kafka. Agoora abstracts Kafka topics as data offers with owners, permissions, documentation, schema, topology, and data quality. It provides everything you need to understand what a data stream means and whether it will solve your problems. SPOUD Agoora is currently in the open beta phase, aiming for freemium and enterprise subscription models.

Self-service access

Also, the value of Kafka for other teams rises with the growing number of data streams offered. You might know this phenomenon as the network effect or as Metcalf’s law. To get more people to use Kafka, you need to simplify the data consumption and production. It’s a chicken and egg problem. You cannot get more people using Kafka if there is no data. The main problem is to convince data producers in enterprises to publish their data as real-time events in Kafka. Unfortunately, one reason why events do not exist is because data producers do not have Kafka knowledge, or the system they run does not provide Kafka connectivity out of the box.

Don’t reinvent the wheel

When the fun park starts to get crowded…

This is why we believe that some of the first things you need to add to your convenience layer is a way to link topics to people (ownership), to protect some topics from being visible altogether, and to understand stream dependencies (topologies). As a whole, you want to enable a certain level of collaboration that counters the side effects of using shared Kafka resources.

One way to approach this is to build it all yourself, which is a lot of fun. If you can’t afford to do this, SPOUD is here to help. Our vision is to enable collaboration on Kafka to build a data as a product culture. We’d love to have you as beta testers alongside companies like Mobiliar.

To get started with SPOUD Agoora and Kafka, check out Agoora and Confluent Developer.