There’s an insidious disease increasingly afflicting DevOps teams. It begins innocuously. A team member suggests adding a new logging tool. The senior dev decides to upgrade the tooling. Then it bites. You’re spending more time navigating between windows than writing code. You’re scared to make an upgrade because it might break the toolchain. The disease is tool sprawl.
AIOps is a DevOps strategy that brings the power of machine learning to bear on observability and system management. It’s not surprising that an increasing number of companies are now adopting this approach. AIOps first came onto the scene in 2015 (coincidentally the same year as Coralogix) and has been gaining momentum for the past half-decade. In this post, we’ll talk about what AIOps is, and why a business might want to use it for their log analytics.
Elasticsearch is a distributed search and analytics engine used for real-time data processing of several different data types. Elasticsearch has built-in processing for numerical, geospatial, and structured text values. Unstructured text values have some built-in analytics capabilities, but custom text fields generally require custom analysis. Built-in text analysis uses analyzers provided by Elasticsearch, but customization is also possible.
Azure, AWS, and GCP cloud services are invaluable to their enterprise customers. When providers like Microsoft are hit with DNS issues or other errors that lead to downtime, it has huge ramifications for their users. The recent Azure cloud services outage was a good example of that. In this post, we’ll look at that outage and examine what it can teach us about enterprise cloud services and how we can reduce risk for our own applications.
President Joe Biden recently signed an executive order which made adhering to cybersecurity standards a legal requirement for federal departments and agencies. The move was not a surprise. It comes after a string of high-profile cyber-attacks and data breaches in 2020 and 2021. The frequency and scale of these events exposed a clear culture of lax cybersecurity practices throughout both the public and private sectors.
When building a microservices system, configuring events to trigger additional logic using an event stream is highly valuable. One common use case is receiving notifications when errors are seen in one of your APIs. Ideally, when errors occur at a specific rate or frequency, you want your system to detect that and send your DevOps team a notification. Since AWS APIs often use stateless functions like Lambdas, you need to include a tracking mechanism to send these notifications manually.
Salesforce was the first of many SaaS-based companies to succeed and see massive growth. Since they first started out in 1999, Software-as-a-Service (SaaS) tools have taken the IT sector and, well the world, by storm. For one, they mitigate bloatware by moving applications from the client’s computer to the cloud. Plus, the sheer ease of use brought by cloud-based, plug-and-play software solutions has transformed all sorts of sectors.
Amazon Web Services (AWS) introduced a preview of Timestream in November 2018 before releasing the full version in October 2020. AWS Timestream is a time series database that can process trillions of events daily. It is faster and less costly than relational databases offered by AWS for processing time-series information. In this article, we will look at what Timestream can do compared to some other AWS databases, and how to use Timestream to help monitor the health of your system.
