Duration
4 days
Overview
This course is designed for both new Elasticsearch users and Elasticsearch professionals. It begins with the basics for getting started with the Elastic Stack, then quickly dives deep into topics ranging from optimizing search performance to building efficient clusters. You will learn about various ways to ingest and process data, how to write complex search requests and work with their responses, strategies for scaling your clusters up or down, managing indices in large clusters and multiple clusters, general cluster management and troubleshooting recommendations.
After completing this course, you will be well on your way to becoming an Elastic Certified Engineer.
Audience
Software Developers
Software Engineers
Data Architects
System Administrators
DevOps
Prerequisites
- No prior knowledge of the Elastic Stack required
- Working knowledge of Linux terminal commands and editors recommended
Requirements
- Stable internet connection
- Mac, Linux, or Windows
- Latest version of Chrome or Firefox (other browsers not supported)
- Disable any ad blockers and restart your browser before class
Outline
Getting started
Learn how Elasticsearch and the components of the Elastic Stack work together to solve a myriad of use cases and real-world problems. Understand the type of data Elasticsearch users typically work with. Learn the many ways to send data into Elasticsearch. Learn the basics of how Kibana can be used to search and analyze data.
Data modeling
Learn about how Elasticsearch is handling strings in your documents. Grasp the differences between keywords and text fields. Use a mapping to configure how Elasticsearch should store and access data. Learn about commonly used field data types and some specialized types designed for various use cases in search or analytics. Learn about analyzers, its anatomy, and how to build custom analyzers. Learn to use tools like dynamic templates and dynamic field mapping to automate mapping creation.
You know, for Search
Learn about Elasticsearch’s own Query DSL, the search language for Elasticsearch. Learn the structure of the queries and what Elasticsearch returns, particularly how Elasticsearch scores documents. Start with some basic queries and compose more complex search requests. Learn the good practices when building a search application using search templates and async search.
Data processing
Mappings are great for optimizing data storage. But if the data itself needs to be changed, it passes through a data processor before it gets indexed. Learn about different tools in the Elastic Stack that can be used to process data and in which situation you should use them. Learn about important use cases for enriching data and how to set it up with Elasticsearch. Learn about the power of runtime field and how to define them using Elasticsearch’s own scripting language: Painless.
Elasticsearch aggregations
Explore aggregations in detail, including the different types of aggregations, how to perform metric and bucket aggregations, and information on how to use some of the more common aggregations. Learn how to combine aggregations to answer any questions you have on a dataset. Learn how to transform Elasticsearch indices to perform more efficient aggregations.
The one about shards
Data in indices is stored in one or more shards. Learn how shards make distributed search possible and how replica shards can be used for high availability and to improve search performance. Learn when to scale your cluster up or down. Explore the anatomy of write and search operations to see how shards are communicating with each other.
Data management
Learn how to manage indices, from creation to migration to cold storage and backup. See how features like data streams, index templates, and index lifecycle management can be used to automate the whole process.
Cluster management
Learn about replicating your data on multiple clusters and how to search across different clusters. Learn how to optimize search performance by debugging complex queries or improving slow search requests. Learn about relevance and relevance tuning. Learn how to understand the health of your cluster, diagnose health issues, and troubleshoot.
Programme scope, delivery arrangements and suitability are confirmed during enquiry.