Operations | Monitoring | ITSM | DevOps | Cloud

To find out more about cloud migrations, the pitfalls that await the unwary, and what the security implications are, I recently sat down with Dustin Dorsey, Systems & Data Architect at Biobot Analytics, based in Cambridge, MA. In the first post in this series, we talked about cloud providers being responsible for security ‘of’ the cloud, while their clients are responsible for security ‘in’ the cloud.

This month, we were thrilled to welcome SquaredUp customers from all over the world to our in-person workshop in sunny Marlow, UK. It was a wonderful day of learning and sharing ideas, and a unique opportunity for SquaredUp users to meet the people behind the product (us!), network with like-minded customers, and get an exclusive look at the latest product updates. We were excited to showcase our Dashboard Server product roadmap and share our vision for the future of SquaredUp.

InfluxDB and Kafka aren’t competitors – they’re complimentary. Streaming data, and more specifically time series data, travels in high volumes and velocities. Adding InfluxDB to your Kafka cluster provides specialized handling for your time series data. This specialized handling includes real-time queries and analytics, and integration with cutting edge machine learning and artificial intelligence technologies. Companies like as Hulu paired their InfluxDB instances with Kafka.

Application Performance Monitoring (APM) is a technology approach that provides real-time information about how your software applications are performing. With a comprehensive view into application health and availability, APM can do things like: Both the importance and the usage of APM has grown in recent years. That’s because companies rely on increasingly complex applications to run their businesses. Here is what you need to know about Application Performance Monitoring.

Elastic® SQL inputs (metricbeat module and input package) allows the user to execute SQL queries against many supported databases in a flexible way and ingest the resulting metrics to Elasticsearch®. This blog dives into the functionality of generic SQL and provides various use cases for advanced users to ingest custom metrics to Elastic®, for database observability. The blog also introduces the fetch from all database new capability, released in 8.10.

The challenge for every organization is gathering actionable observability information from all your systems, in a timely manner, without creating a substantial operational burden for the teams managing the collection tooling. While each observability solution has its unique benefits and challenges, the one common burden expressed by teams is the management of the metadata of the metrics, traces, and logs.

Today we’re announcing the general availability of Icinga DB Web v1.1.0. You can find all issues related to this release on our Roadmap. Please make sure to also check the respective upgrading section in the documentation.

Do you want to build software faster and release it more often without the risks of negatively impacting your user experience? Imagine a world where there is not only less fear around testing and releasing in production, but one where it becomes routine. That is the world of feature flags. A feature flag lets you deliver different functionality to different users without maintaining feature branches and running different binary artifacts.