# mirrormaker

Published articles for mirrormaker.

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## Building a GlobalStream: Replication Challenges and Optimizations

DevFeed: [Building a GlobalStream: Replication Challenges and Optimizations](<https://devfeed.tech/articles/building-a-globalstream-replication-challenges-and-optimizations-26257.md>)

Original publisher: [Read original article](<https://medium.com/adyen/building-a-globalstream-replication-challenges-and-optimizations-57be4e1aeae1?source=rss----64941d9fbc09---4>)

Author: Adyen

Published: 2026-07-13T09:18:08Z

Content type: article

Language: en

Sources: [Adyen Tech](<https://devfeed.tech/sources/adyen-tech.md>)

Topics: [Replication](<https://devfeed.tech/topics/replication.md>), [Kafka](<https://devfeed.tech/topics/kafka.md>), [Streaming](<https://devfeed.tech/topics/streaming.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [systems](<https://devfeed.tech/topics/systems.md>)

Tags: [data-replication](<https://devfeed.tech/tags/data-replication.md>), [kafka](<https://devfeed.tech/tags/kafka.md>), [latency](<https://devfeed.tech/tags/latency.md>), [mirrormaker](<https://devfeed.tech/tags/mirrormaker.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [payments](<https://devfeed.tech/tags/payments.md>), [replication](<https://devfeed.tech/tags/replication.md>), [streaming](<https://devfeed.tech/tags/streaming.md>), [tech](<https://devfeed.tech/tags/tech.md>)

### AI overview

Adyen describes the challenges of replicating Kafka data across continents with MirrorMaker 2. The article covers data-location tradeoffs, Kafka Connect behavior, parameter tuning, and optimizations intended to sustain high-throughput replication while controlling latency.

### Source excerpt

By Gaurav Singh & Luciano Sabença, Streaming Platform Team Global data streaming requires careful tradeoffs between performance, compliance, and reliability. At Adyen, replication is essential: data must remain close to producers to reduce latency and meet regulations, while also being centralized for analytics. In this blog, we share our experience optimizing MirrorMaker 2 for large-scale, cross-continent Kafka replication, the parameter tuning we applied, and the hidden behaviors in Kafka Connect that ultimately unlocked the required throughput. Why Replicate? There are many things you need to take into account when designing a data streaming platform for a company such as Adyen. One of the most important aspects of it is data location and replication. Due to performance, reliability, and compliance, keeping data close to producers is usually a good idea. After all, you don't want to wait in line at the cashier to confirm the payment for that fat burger you just bought on that nice beach in Australia while your payment was being sent to Europe. However, it's also a common practice to have all data in a centralized cluster(s) in centralized locations for analytical purposes. These two need a bridge, which is where mirroring comes in. An example replication flow diagramMirror Maker When it comes to copying data between Kafka clusters, Mirror Maker is the default open source tool. MirrorMaker 2 is the current version, and it's built on top of Kafka Connect, a platform designed to make it easier to integrate Kafka with other tools such as databases and distributed file systems, and -- why not? -- another Kafka cluster. Setting up Mirror Maker is fairly straightforward, but tuning it to the performance Adyen requires isn't. Let's go over the journey into the depths of Kafka Connect and parameter tuning to find a solution! Playing the Volume Game Kafka is a highly flexible tool and serves as the backbone for many different architectural patterns. It can be used for batch