Elastic Observability Engineer Training - How to Process & Structure Data

This video will guide you through the new Elastic Observability Engineer course while providing tips on how to structure data properly as an observability best practice. Properly parsing and structuring your data is an important first step in building an efficient and effective observability solution using the Elastic Stack. Effectively indexing and structuring data into Elasticsearch is critical for establishing efficient search criteria and effective results. Logstash filters and ingest pipelines make processing unstructured data easier by providing a set of common processors to efficiently parse, transform, and index that data into the desired structure. In this webinar, we will explore concepts from the all-new Elastic Observability Engineer course, including how to process and structure data using a variety of common processors. Our expert instructors will demonstrate various solutions and built-in features that convert, enrich, process, and structure different types of fields from unstructured data. In addition, we will show how to create your own pipeline of processors for transformations that are not possible using the prebuilt processors. Highlights: Introduce common ingest processors to parse unstructured data into structured data Discuss best practices for dissecting, converting, and enriching your fields Explore different scenarios that require either a built-in processor or a pipeline of processors for certain types of unstructured data Speakers: Tamara Rosini and Lutf Ur Rehman, Education Engineers at Elastic. Questions? Check out https://discuss.elastic.co/ Connect with the Elastic community through Slack: https://ela.st/slack Be sure to join your local and virtual Elastic User Groups to stay up-to-date on upcoming meetups: https://community.elastic.co/ #elasticstack #observability #elk #elasticsearch #elasticsearchtutorial #techcommunity #logstash #data