"Good introduction to Apache Spark. The trainer was great at talking us through the information, specifically optimisation methods. He spoke slowly and concisely which really got his points across. He effectively tailored the course to our specifications which we also appreciated."
RL, Financial Crime Technologist, Apache Spark, April 2021
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.
There are many ways to search using
Elasticsearch — Elasticsearch’s own Query DSL, the new and easy-to-use EQL, Elasticsearch SQL, or Kibana’s KQL, just to name a few. Learn when and where to use which search language and how to search across multiple clusters.
Build some basic queries and aggregations and compose more complex search requests,
including some that use Painless scripting or
search templates.
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
the Elastic Common Schema and how to use it. Learn about runtime fields, analyzers, and how to build custom analyzers. Learn to use tools like dynamic templates and dynamic field mapping to automate mapping
creation.
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 two important use cases for processing data and what tools to use for them: enriching data to denormalize relational data and converting event-centric data to entity-centric data.
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 about cross-cluster replication, which can be used for disaster recovery but also for other scenarios like centralized reporting and data localization. Learn when to scale your cluster up or down.
Learn how to manage indices, from creation to migration to cold storage and backup. See how features like data streams, index templates, andindex lifecycle management can be used to automate the whole process.
Learn how to optimize search performance by debugging complex queries or improving slow search requests. Learn about search and filter
contexts. Learn about relevance and relevance tuning. Learn about different cluster topologies and in which scenarios they can be used.
Learn how to understand the health of your cluster, diagnose health issues, and troubleshoot. Learn about various cluster deployment types and different considerations for each.
Learn to build your own search application using some of the most popular supported client libraries like NodeJS and Python.
Attendees should have the following :
"Good introduction to Apache Spark. The trainer was great at talking us through the information, specifically optimisation methods. He spoke slowly and concisely which really got his points across. He effectively tailored the course to our specifications which we also appreciated."
RL, Financial Crime Technologist, Apache Spark, April 2021
“JBI did a great job of customizing their syllabus to suit our business needs and also bringing our team up to speed on the current best practices. Our teams varied widely in terms of experience and the Instructor handled this particularly well - very impressive”
Brian F, Team Lead, RBS, Data Analysis Course, 20 April 2022
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This Elasticsearch 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, and building your own custom search application that uses Elasticsearch on the back end
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