Elasticsearch Engineer 2

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Course Overview

Elasticsearch Engineer II Certification training uses authorized course content developed by Elastic and will be delivered by Elastic Certified Instructor. This instructor-led course is designed for Elasticsearch professionals who need to expand their skill set for developing and managing powerful search and analytics solutions with the Elastic Stack. You will learn advanced cluster management techniques, best practices for capacity planning and scaling, tips for monitoring and alerting, considerations for going into production, and more. . 

At the end of the training, participants will be able to:

  1. Field Modeling
  2. Fixing Data
  3. Advanced Search and Aggregations
  4. Cluster Management
  5. Capacity Planning
  6. Elasticsearch Internals
  7. Document Modeling
  8. Monitoring and Alerting
  9. Moving from Dev to Production

Pre-requisite

  1. Complete the Elasticsearch Engineer I course, or possess equivalent Elasticsearch knowledg
  2. Mac, Linux, or Windows
  3. Latest version of Chrome or Firefox (Safari is not 100% supported)
  4. Stable internet connection (virtual classroom)
  5. Due to virtual classroom JavaScript requirements, we recommend that you disable any ad-blockers and restart your browser before class.

Duration

2 days

Course Outline

  1. Learn how to design and model the fields in your documents, including discussions on granular fields, range types, dealing with large field cardinality, and designing for proximity matching.
  2. Hands-on Lab
  1. Learn how to use the new Painless scripting language in Elasticsearch and discuss use cases for scripting, including the Reindex, Update By Query and Delete By Query APIs.
  2. Hands-on Lab
  1. We discuss denormalizing documents, working with nested fields, and using the join field for parent/child relationships
  2. Hands-on Lab
  1. Learn some of the advanced search and aggregation techniques, including cross cluster search and pipeline aggregations.
  2. Hands-on Lab
  1. We walk through the details of managing a cluster, including how to configure shard filtering, shard allocation awareness and forced awareness.
  2. Hands-on Lab
  1. Learn about designing for scale, scaling with replicas, scaling with Indices, capacity planning use cases, and working with time-based data.
  2. Hands-on Lab
  1. Take a deep dive into how Elasticsearch works, including the details of Apache Lucene, segments, doc values, and caching.
  2. Hands-on Lab
  1. We discuss of monitoring options, including the Stats API, task monitoring, the cat API, the X-Pack Monitoring component, and guidelines for monitoring a cluster and setting up alerts.
  2. Hands-on Lab
  1. We explore items to consider when moving to production, including network setup, hardware requirements, JVM settings, and also a discussion on some of the common causes of poor query performance and how to fix them.
  2. Hands-on Lab

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