Reuse Playwright Code across Files and Tests with Fixtures
Learn how to leverage Playwright test fixtures to DRY your code and reuse it across tests and spec files.
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Comparing Amazon ECS launch types: EC2 vs. Fargate
Amazon Elastic Container Service (ECS) is a fully managed container orchestration service that enables users to easily run, manage and scale containers on AWS. With ECS, you can deploy containers either on a cluster of Amazon EC2 instances or on AWS Fargate, a serverless computing engine for containers. In this article, we’ll look at how these two launch types compare and explore how to start using them.
How to Deploy a Cribl Stream Leader, Cribl Stream Worker, and Redis Containers via Docker
As mentioned in our documentation, Cribl Stream is built on a shared-nothing architecture. Each Worker Node and its processes operate separately and independently. This means that the state is not shared across processes or nodes.This means that if we have a large data set we need to access across all worker processes, we have to get creative. There are two main ways of doing this: In this blog, we’ll walk through how to deploy a Stream leader, Stream worker, and Redis containers via Docker.
Why DevOps needs an AIOps approach?
This need for AIOps was simmering conveniently and gradually reaching its threshold when the pandemic suddenly hit the world, pushing organizations into remote work. The sudden, global-scale change raised challenges for IT operations teams to monitor and detect incidents in a distributed environment and maintain cybersecurity and compliance. While the pandemic pushed some organizations into the reality of remote work, others were already on their way to digital transformation.
Automating Root Cause Analysis with AIOps
A lot is expected of automation in IT environments in the next few years. By 2024 Gartner predicts IT automation will drive a 20% reduction in unplanned downtime and lower operational costs by 30%. At the same time, the efficiencies generated by IT automation and analytics will allow organizations to refocus 30% of their IT operations management resources from support to “continuous engineering.”
AIOps Essentials: Automating actions from AIOps analysis | AIOps Use Cases (5/5)
Artificial intelligence for IT operations (AIOps) is a way to automate tasks that are typically carried out by site reliability engineers (SREs). It aims to make the lives of SREs easier by helping them reduce the amount of noise coming from systems, surface issues more easily, and perform root cause analysis by correlating data from different systems.
AIOps Essentials: How to use Distributed Tracing for Root Cause Analysis | AIOps Use Cases (4/5)
Artificial intelligence for IT operations (AIOps) is a way to automate tasks that are typically carried out by site reliability engineers (SREs). It aims to make the lives of SREs easier by helping them reduce the amount of noise coming from systems, surface issues more easily, and perform root cause analysis by correlating data from different systems.
AIOps Essentials: Issue Detection using Anomaly Detection on top of APM | AIOps Use Cases (3/5)
Artificial intelligence for IT operations (AIOps) is a way to automate tasks that are typically carried out by site reliability engineers (SREs). It aims to make the lives of SREs easier by helping them reduce the amount of noise coming from systems, surface issues more easily, and perform root cause analysis by correlating data from different systems
AIOps Essentials: How to Reduce Noise in Ingested Telemetry on Elastic | AIOps Use Cases (2/5)
Artificial intelligence for IT operations (AIOps) is a way to automate tasks that are typically carried out by site reliability engineers (SREs). It aims to make the lives of SREs easier by helping them reduce the amount of noise coming from systems, surface issues more easily, and perform root cause analysis by correlating data from different systems.
AIOps Essentials: What is AIOps? | AIOps Use Cases with Elastic Observability (1/5)
Artificial intelligence for IT operations (AIOps) is a way to automate tasks that are typically carried out by site reliability engineers (SREs). It aims to make the lives of SREs easier by helping them reduce the amount of noise coming from systems, surface issues more easily, and perform root cause analysis by correlating data from different systems. AIOps can also automate actions based on identified problems using machine learning. In this video series, we demonstrate how to use Elastic to implement AIOps.