Elasticsearch is a great platform for any data lake initiative, and ideal for analyzing your monitoring and observability data. But if you’re working with a number of different monitoring and observability tools, especially across multiple cloud environments, you might find it challenging to get all your data into Elasticsearch.

Breaking down larger, monolithic software, services, and applications into microservices has become a standard practice for developers. While this solves many issues, it also creates new ones. Architectures composed of microservices create their own unique challenges. In this article, we are going to break down some of the most common. More specifically, we are going to assess how observability-based solutions can overcome many of these obstacles.

As a concept, observability has been a relatively recent entrant into the world of information technology and cloud technology. The idea originated initially from controls system engineering. Observability refers to the concept of inferring the internal status of the system based on the outputs derived from the same. This is the conventional definition of observability.

‘Tis the season to set 2021 goals. Whether setting OKRs, KPIs, KPAs, MBOs, or any other flavor of goal-setting frameworks in an endless sea of acronym soup, chances are that you’re still dealing with a sizable disconnect between business objectives and daily engineering work. Service Level Objectives (SLOs) have boomed in popularity because they provide a common language between business stakeholders and engineers to set aligned goals.

The ability to access the internal state of your application ecosystem is critical to optimizing your applications and the experience of your users. Elastic Cloud on Microsoft Azure gives you access to Elastic Observability, allowing you to monitor your infrastructure and see how every signal interrelates by utilizing a wide variety of resources that can be deployed in minutes.