Mattermost v10.3 Changelog
Join Jesús Espino, senior staff software engineer at Mattermost, as he walks us through what's new in Mattermost v10.3.
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Configuring Kafka Brokers for High Resilience and Availability
In a Kafka setup, high availability isn’t just nice to have—it’s a lifeline. Downtime, data loss, or hiccups in message flow can make or break critical applications. Let’s be real: setting up Kafka brokers to be resilient takes some fine-tuning, but it’s absolutely worth it. Imagine dealing with failovers smoothly or knowing your data is protected even if a broker goes down—this is what configuring for resilience is all about.
How to Perform Health Checks on Your Kafka Cluster: Ensuring Optimal Performance and Reliability
When managing Kafka clusters, health checks are essential—not just a luxury. They’re your frontline defense in maintaining stability and performance, helping you catch issues before they snowball. Let’s dive into effective ways to assess your Kafka cluster’s health, from tracking key metrics to taking proactive steps that keep your operations running smoothly.
Mattermost v10.2 Changelog
Join Jesús Espino, senior staff software engineer at Mattermost, as he walks us through what's new in Mattermost v10.2.
Troubleshooting Kafka Monitoring on Kubernetes
Let’s be honest: setting up Kafka monitoring on Kubernetes can feel like you’re trying to solve a puzzle without all the pieces in place. Between connectivity snags, configuration issues, and keeping tabs on resource usage, it’s easy to feel like you’re constantly firefighting. But tackling these issues head-on with a few go-to solutions can save a lot of headaches down the road.
Cost-Effective Strategies for Kafka Resource Management
Running Kafka at peak efficiency doesn’t come cheap. But with some smart tweaks, it’s entirely possible to keep costs down while making sure everything flows smoothly. The key is to balance your resource usage across CPU, memory, and storage to get the most bang for your buck. Let’s dive into some strategies that will help you stretch those resources, streamline your Kafka setup, and avoid breaking the bank.
Common Kafka Cluster Management Pitfalls and How to Avoid Them
Managing a Kafka cluster is no small feat. While Kafka’s distributed messaging system is incredibly powerful, keeping it running smoothly takes careful planning and a keen eye on the details. Small mistakes in Kafka management can quickly add up, leading to bottlenecks, unexpected downtime, and overall reduced performance. Let’s explore some common Kafka management pitfalls and, more importantly, how to steer clear of them.
Balancing Performance and Cost in MQ Modernization
When it comes to modernizing MQ systems, finding the right balance between performance and cost can feel like walking a tightrope. With budget constraints on one side and the need for high performance on the other, it’s easy to tip too far in either direction. But here’s the thing—when you modernize your MQ infrastructure thoughtfully, you can achieve both efficiency and budget optimization.
Concept Demo: Filtering and alerting restricted materials
In this demo, we’re sharing a conceptual demo showcasing solutions that support our customers in their data loss prevention efforts. We’re working on new capabilities to block, filter, and alert restricted content from being shared in channels or teams with the wrong access levels. Data loss prevention is crucial for our customers in highly secure environments and consequences are severe if standards aren’t followed or if a data spill occur. To support our customers we’re exploring ways to prevent human error that could lead to data spillage.
How to Balance Load in Kafka for Improved Performance
Keeping a Kafka cluster optimized can feel like a balancing act. Every piece—brokers, partitions, producers, and consumers—has to work in harmony, or you’ll start running into bottlenecks. To get Kafka to run smoothly and handle growing traffic loads, balancing load across the system is key. Let’s go over practical load-balancing techniques that can improve Kafka performance, keep everything running efficiently, and prevent data slowdowns from building up.