Artificial Intelligence for IT Operations (or AIOps for short) continues to be a hot topic among developers, SREs, and DevOps professionals. The case for AIOps is especially crucial given the expansive nature of today’s observability efforts across hybrid and multi-cloud environments. As with most observability platforms, it all starts with your telemetry data: metrics, logs, traces, and events.
Apache Kafka is a high-throughput, low-latency platform for handling real-time data feeds. Its storage layer is in essence a massively scalable pub/sub message queue designed as a distributed transaction log. It can be used to process streams of data in real-time, building up a commit log of changes. Kafka has strong ordering guarantees that enable it to handle all sorts of dataflow patterns including very low latency messaging and efficient multicast publish / subscribe.
The perfect incident management checklist doesn’t need to be a fantasy. In fact, it shouldn’t be! The perfect incident management checklist should cover several topics, be broken down into bite-size sections, and help team members quickly identify tasks that fall under their responsibility. We asked our experts what should be included in the perfect incident management checklist. Here are their answers.
At incident.io, we’re building tools to help people respond to incidents, often by automating their organisations’ process. Much of this is powered by our Workflows product, which customers can use to achieve things like: Workflows as a product feature are incredibly powerful, and we’re proud of the value they provide to our customers. Behind-the-scenes, though, building something like workflows can be difficult.
This is the second in a two part series on how we built our workflow engine, and continues from Building workflows (part 1). Having covered core workflow concepts and a deep-dive into the Workflow Builder in part one, this post describes the workflow executor, and concludes the series with an evaluation of the project against our goals.
