Distributed tracing allows you to track the execution of your user's transactions by following them between applications’ microservices. It provides easy to understand visualizations of transactional lifespan with the ability to pinpoint any slowdowns and errors in response to microservices. During my presentation at Illuminate, I shared that we extended the Sumo Logic platform to cover application performance use cases.

When providing services to your customers you need to keep an eye on everything that could impact your success with that – from low-level performance metrics to high-level business key performance indicators. From server-side logs to stack traces giving you full visibility into business and software processes that underpin your product. That’s where cloud monitoring tools and services come into play.

What are logs? In computing, when generally speaking of “log”, we refer to information belonging to a more or less low level reported by the operating system or a specific application that helps identify what is being done, including errors, problems or minor warnings, and when that happens, it indicates the date and time. In some cases, the source, the user, the IP address and other interesting fields from the point of view of what has happened can be identified.

Amazon Redshift is a cloud-based data warehousing solution that makes it easy to collect and analyze large quantities of data within the cloud. Cloud data warehouse services like Redshift can remove some of the performance and availability pain-points associated with on-premises data warehousing, but they are not a silver bullet. Getting the most out of Redshift requires carefully monitoring Redshift clusters in order to identify stability issues and performance bottlenecks.

Anyone who has ever had to administer IBM Spectrum Protect™ (formerly Tivoli Storage Manager or TSM) knows that eventually, you’ll end up parsing the activity log for advanced problem determination or running advanced queries on the summary table for extended reporting. This is a huge pain! With the latest enhancements to Galileo for Spectrum Protect, you need not go elsewhere for answers.

LogQL, the Loki query language, is heavily inspired by Prometheus PromQL. However, when it comes to filtering logs and finding the needle in the haystack, the query language is very specific to Loki. In this article we’ll give you all the tips to create fast filter queries that can filter terabytes of data in seconds. In Loki there are three types of filters that you can use.

When approached by stakeholders in their organization, few security teams can confidently demonstrate that logging and alerting capabilities are working as expected. Organizations have become more distributed and reliant on cloud offerings for use cases such as identity and access management, user productivity, and file storage. Meanwhile, adversaries have extended their operational capabilities in cloud environments.

Monitoring and analytics have been an issue for Serverless systems since they were invented. While it’s easy to attach an agent like NewRelic or DataDog to a server or container, function monitoring requires a different approach. Serverless applications, where logic is distributed over a large number of functions, attaching agents or wrappers leads to cost increase and development overhead.