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30 Sep 2026, 19:00 by Brindal Patel
Throughout my career, observability and monitoring systems have played a very important role in system stability, availability, and scalability when architecting, whether a system handles millions or billions of events per day. Having architected core infrastructure across communication platforms, enterprise compliance pipelines, and high-throughput transaction systems, I have repeatedly seen how background instrumentation can silently become a bottleneck under heavy production load.
Telemetry and observability come with their own challenges. Instrumenting telemetry without degrading live business application performance, increasing request latency, or inflating cloud costs is often deprioritized initially but becomes very important as the system scales. Furthermore, it is a much harder problem to solve than processing domain events themselves in production.
30 Sep 2026, 18:00 by Alexander Fo
A pod goes into CrashLoopBackOff. You pull the logs expecting a stack trace, a panic, an error string - anything that points you somewhere. Instead, you get one line:
30 Sep 2026, 17:00 by Jeremy Morgan
One of my favorite things about F1 racing is the data behind it. F1 cars are the most complex and advanced in any racing series. They collect huge amounts of telemetry data. The tracks also gather data during events, and race engineers analyze it week after week. They study everything from weather and tire temperatures to corner exit speeds. Data drives the sport forward in a major way.
While learning about graph databases and Neo4j, I realized it was the perfect tool for answering a question I was curious about. We've all seen or heard of "Six Degrees of Kevin Bacon," where nearly any actor can be traced back to the Footloose star. I wondered: could this work for F1 drivers? By comparison, it's a much smaller dataset than famous actors.
30 Sep 2026, 16:00 by Jerzy Kopaczewski
Most AWS migration projects don't fail because of technical complexity. They fail because teams treat migration as a single activity rather than a set of distinct strategies applied to different workloads.
AWS defines seven migration strategies — the 7Rs — that determine how each application moves to the cloud. The decision of which strategy applies to which workload has more impact on project cost, timeline, and outcome than any architectural choice you'll make after. Yet in practice, most teams default to "lift-and-shift everything" without evaluating whether that's appropriate.
30 Sep 2026, 15:00 by Kai Wähner
Kafka-compatible ingestion is showing up everywhere now, including in analytics platforms that have nothing to do with running Kafka. Databricks added Kafka-compatible APIs to its Zerobus Ingest service. Snowflake went a step further at its Summit and announced Datastream, a native, fully Kafka-compatible streaming service. In both cases, existing Kafka producers can stream straight into the platform with no code changes, just a config change.
This is good news for the ecosystem. It also confirms a point I have made for years: the Kafka API has become the de facto standard for moving events around, the same way the Amazon S3 API became the standard for object storage.
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