Get started with Gremlin's Chaos Engineering tools to safely, securely, and simply inject failure into your systems to find weaknesses before they cause customer-facing issues. API gateways are a critical component of distributed systems and cloud-native deployments. They perform many important functions including request routing, caching, user authentication, rate limiting, and metrics collection. However, this means that any failures in your API gateway can put your entire deployment at risk.

It’s important to ensure the logging and monitoring of a service is as consistent across environments as the code itself. However, it can be expensive and cumbersome to test the logging functionality with the usual required log exporters, database infrastructure, and processing requirements of normal production-grade solutions.

When it comes to conversations around application architecture or working with integrations between applications, you’ve likely heard a couple terms pop up a few times: microservice and APIs. You might also have run across the common misconception that microservices are just a way to implement APIs so they can communicate with each other. As you’ll see in this article, there are alternative ways to architect our microservice applications.

We're excited to announce that autoscaling is now available on Elastic Cloud. In our initial release, autoscaling monitors the storage utilization of your Elasticsearch data nodes and the available memory capacity for your machine learning jobs.

Logz.io has always prided itself as a company pushing the use of open source tech. As we have moved to expand our reach with metrics and traces over the past year and a half, we have doubled down on our own contributions to the community. With (distributed) traces in particular, we have been able to forge ahead. Our relationship with the teams at Jaeger and OpenTelemetry have really blossomed (and we are kind of proud to have supported the latter in the run-up to the OpenTelemetry v1.0 release).

With our ability to ingest GCP logs and metrics into Splunk and Splunk Infrastructure Monitoring, there’s never been a better time to start driving value out of your GCP data. We’ve already started to explore this with the great blog from Matt here: Getting to Know Google Cloud Audit Logs. Expanding on this, there’s now a pre-built set of dashboards available in a Splunkbase App: GCP Application Template for Splunk!

It is incredibly useful to be able to identify the most unusual data in your Elasticsearch indices. However, it can be incredibly difficult to manually find unusual content if you are collecting large volumes of data. Fortunately, Elastic machine learning can be used to easily build a model of your data and apply anomaly detection algorithms to detect what is rare/unusual in the data. And with machine learning, the larger the dataset, the better.

Artificial Intelligence is in the news a lot, and it’s hyped as a cure for all ills in the same breath it’s suspected of spelling doom for us all. What’s the truth behind all the noise? What does artificial intelligence do, seeing as it is simply everywhere. The truth of the matter is that whatever the would-be prophets say, artificial intelligence and machine learning is here, now, and has applications to your day-to-day.