How Kpow became mission-critical to Verrency's payments infrastructure

Building payment innovation on a young streaming platform

Verrency is a Melbourne fintech whose platform lets banks add value-added services to their payment pipeline without a significant internal IT spend, and gives those banks ways to personalize the banking experience so consumers can tailor products to their own needs. Verrency’s customers are financial institutions and partners around the world who expect zero downtime and real-time performance, while often needing to consume data at their own pace.

Development of the platform began in 2016, with delivery and maintenance managed by a small team. CTO Euan Walker’s team had prior experience in banking and finance that gave it confidence the JVM was the right foundation, but the platform’s real-time, reliability, and durability requirements called for something new. The team chose Apache Kafka as the heart of its real-time computation, a durable log of transaction histories, and an integration option for partners and third-party providers. “We’ve been fortunate to have engineers who know Kafka well, but it has a learning curve,” Walker said.

The challenge: pioneering Kafka in a market with no playbook

Verrency was one of the first teams to adopt Kafka as a full streaming compute platform in Melbourne, starting with a pilot that also evaluated Kinesis before Kafka became a full part of the company’s infrastructure. “There still aren’t many people who’ve done big, complex Kafka projects here in Melbourne, and that can pose a challenge,” Walker said. With few local peers to learn from, engineers spent real time working through Kafka Streams issues and RocksDB idiosyncrasies on their own.

Operating Kafka in production surfaced a second problem: the tooling to manage, deploy, and monitor it was thin. Kafka introduces its own vocabulary, and Kafka Streams adds still more concepts on top, among topologies, partitions, brokers, and offsets. For Walker, that made an old engineering question much harder to answer: where is my data? “With Kafka, your data is hidden away, and troubleshooting can be time-consuming, even for what you hope would be a simple task,” he said. “I think this is the key challenge for Kafka in smaller organizations.”

The solution: becoming a vocal beta tester for Kpow

Verrency was an early adopter of Kpow, brought in while the company was still building out its Kafka platform and while the ecosystem of tools for managing, deploying, and monitoring Kafka was still thin. Amazon MSK became a vital part of the mix around the same time, cutting the operational surface area of running Kafka and letting a product-focused fintech spend its time building products rather than spelunking logs. Kpow worked alongside that setup rather than against it, giving engineers the visibility MSK alone didn’t provide.

Data Inspect became the feature Verrency’s engineers reached for most, giving them a direct answer to the “where is my data” problem instead of a time-consuming manual search. That day-to-day usage turned Verrency’s engineers into some of Kpow’s most vocal beta testers, and their feedback shaped the product as it developed, including its kREPL and custom Kafka Language work. “Kpow is built by a team with a passion for Kafka, but who also carry the scars of some tough implementations,” Walker said. “It’s a tool by and for engineers, and I look forward to seeing it continue to evolve.”

The results: from bewildering internals to trusted infrastructure

For Verrency, Kpow became the simplest, quickest, and most cost-effective way for engineers to access their own data, working seamlessly alongside Amazon MSK rather than requiring a separate operational path. Walker was direct about where that leaves the tool in Verrency’s stack: “Kpow will be a key part of our infrastructure as long as we’re using Kafka.”

That trust was earned through day-to-day use, not granted upfront. Data Inspect turned troubleshooting from a manual, time-consuming search into something engineers could resolve directly, and the feedback loop ran both ways: Verrency’s engineers helped shape features like kREPL and the custom Kafka Language as they used the product. Verrency has since grown into a core part of Melbourne’s Kafka community and was named a finalist for Excellence in Payments and Excellence in Establishing Global Presence at the Finnies Awards, evidence of the platform’s momentum since those early days.

Taken together, Kpow gave Verrency’s engineers a fast way to answer where their data actually was, a channel to shape the tool they depended on, and infrastructure it expects to keep relying on for as long as it runs Kafka, letting one of Melbourne’s earliest Kafka teams build with confidence in a market with no local playbook to follow.

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