![]() Steps to integrate Splunk with a Java project This course will help you to achieve excellence in this domain. In order for us to achieve this, there a set of steps that we need to complete – and assuming that everything is complete, we will continue to look into a Java example and test our application with various combinations of logging with Splunk.ĭo you want to become a certified splunk Professional? Then enroll in " Splunk Certification Training"Course. In order to look at the various possibilities of logging examples, let us configure Splunk on to a Java project environment and check how we can leverage the use of Splunk. Splunk `logging` with Java (with an example) Splunk bridges the gaps which a single simple log management software or a security information product or a single event management product can manage all by themselves. It is advanced software that indexes and searches log files stored on a system or the like, alongside that, it is a scalable and potent software. Quickbuild, Eclipse/IntelliJ IDEA for localĢ8.000 Unit Tests + 3.Splunk is a software that enables one to monitor, search, visualize and also to analyze machine-generated data (best example are application logs, data from websites, database logs for a start) to big-data using a web style interface. Unit & Integration Tests (fully automated) Quickbuild + Jenkins Continuous Integration Gradle + Artifactory build/deployment automation Selenium/Appium Automated Functional TestsĪWS, VMware, VirtualBox Test Environments Do you have the right monitoring strategy / feedback loop?ħ Provide fast lane to production for Hotfixes!Įvery 2 weeks version is pushed to next stage = 2 weeks release cycle.Can technology help? Can process change help?.Where do you currently have bottlenecks from Dev to Ops?.Do you encourage failure and learn from it?.How is your trust level to your engineering team?.Who pushes the button “deploy”?Provide fast lane to production forģ Same Deployment Automation in every stage! No manual touches!Ĥ Same monitoring in every stage! Visibility into every stage/deployment!ĭev Stage Daily Production StageAcceptance Stage Bi-DailyĤ Same monitoring in every stage! Visibility means also easy access!Įngineers typically like Logs. … OWNERSHIP of their own source code for all deployments ![]() Owning and promoting tooling that gives engineering teams …įrom commit to deployment and into all deployments … monitoring Production and creating tickets for engineers ~200 340code commits / day Stories per sprintĤ50 99.998%Global EC2 Instances Global Availability Lessons learnt from shifting an on-prem to a cloud cultureħ COMPANY CONFIDENTIAL – DO NOT DISTRIBUTE #Perform2015ģ1000 60hUnit+Int Tests / hour UI Tests per Build Optimize Before DeployImmediate Customer 5. Very late feedback Innovation and Optimization Researcher and “Features in a Box” Ship the whole box! ![]() Transforming 6 Month Waterfalls to 1 Hour Code Deploy Our DevOps Journey: 6 Month Waterfalls to 1 Hour Code Deploys Today Anita’s team runs 170 production changes every day, running across several AWS Data Centers as well as On-Premise – something that would have been thought impossible only a few years prior. How to successfully influence key stakeholders for complete alignment The role of Feature teams and why continuous feedback is critical for them What the specific role of her DevOps team is and how it played into the transformation How to enable a complete cultural shift across multiple teams, in terms of thought process AND execution On November 17th at 1pm/10am PT Anita will present the challenges she and her team faced transforming 6 Months Waterfall to 1 Hour Code Deploys. Additionally, be able to support a 1-hour lead time from Code Change to Production”. She will lend her perspective as a key member of the team that executed on the original vision: to “implement a new cloud native offering and deploy a new feature release every 2 weeks. In the 2nd part of our webinar series, Anita Engleder, DevOps Lead at Dynatrace reviews and dissects lessons learned during the transformational journey moving Dynatrace from an on-prem culture to one that is cloud native. Transforming 6 Month Waterfalls to 1 Hour Code Deploys
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