With progressive delivery, DevOps, scrum, and agile methodologies, the software delivery process has become faster and more collaborative than ever before. Scrum has emerged as a ubiquitous framework for agile collaboration, instilling some basic meetings and roles into a team and enabling them to begin iterating on product increments quickly. However, as scrum teams grow and systems become more complex, it can be difficult to maintain productivity levels in your organization.

You have to capture everything to investigate security issues thoroughly, right? More often than not, data that at one time was labeled irrelevant and thrown away is found to be the missing piece of the puzzle when investigating a malicious attacker or the source of an information leak. So, you need to capture every network packet.

When you’re working with large data sets, having that data structured in a way that means you can use software to process and understand it will enable you to derive insights far more quickly than any manual approach. Logfile data is no exception.

Debugging is a fundamental skill in the arsenal of any engineer. Mistakes happen, and bugs are inevitable, but a skilled debugger can catch a bug early and find an elegant solution.

You’ve probably seen the term AIOps appear as the subject of an article or talk recently, and there’s a reason. AIOps is merging DevOps principles with Artificial Intelligence, Big Data, and Machine Learning. It provides visibility into performance and system data on a massive scale, automating IT operations through multi-layered platforms while delivering real-time analytics.

Security is one of the most changeable landscapes in technology at the moment. With innovations, come new threats, and it seems like every week brings news of a major organization succumbing to a cyber attack. We’re seeing innovations like AI-driven threat detection and zero-trust networking continuing to be a huge area of investment. However, security should never be treated as a single plane.

With dozens of microservices running on multiple production regions, getting to a point where any error log can be immediately identified and resolved feels like a distant dream. As an observability company, we at Coralogix are pedantic when it comes to any issue in one of our environments. That’s why we are using an internal Coralogix account to monitor our development and production environments.

Maintaining an endpoint, especially a customer-facing one, requires constant monitoring, whether using REST or GraphQL. As the industry has looked for solutions to build a more adaptive endpoint technology, it is also a must to monitor these endpoints. GraphQL and REST are two different technologies that allow user-facing clients to link to databases and platform logic. Both GraphQL and REST include monitoring techniques.

When your Elasticsearch cluster ingests data, it needs to understand how the data is constructed. To do this, your Elasticsearch cluster undergoes a process called mapping. Mapping involves defining the type for each field in a given document. For example, a number or a string of text. But how do you know the health of the mapping process? And why do you need to monitor it? This is where mapping statistics come in. Mapping statistics give you an overall view of the mapping process.

Continuous integration (CI) and continuous delivery or deployment (CD) cover the process of automatically merging, building, and testing code changes ready for release, and – in the case of continuous deployment – releasing those changes to users. If you’re developing software for others to use, you’ll need to go through some form of build and test process before you make your latest changes available.