Core Elasticsearch Training in Republic of the Philippines

Exposure to or interest in Elasticsearch, relational databases, distributed systems, or information retrieval.

Developers who would like to build real-time search solutions and analytics solutions.

The course aims to provide a solid foundation in search and information retrieval. Starting with fundamental concepts and covers best practices, key features, and distributed search application development with Elasticsearch.

During the course there will be time for discussion as well as attendee case studies. At the end of the training you will have an in-depth understanding of how Elasticsearch works, you will be able to reliably analyze, understand, and solve common problems, and be ready to build state-of-the-art search applications.

Introduction

• Terminology, basic concepts, implementation, setup, and basic operations.
• What is Elasticsearch?
• Overview of best practices
• What’s in a distribution?
• Understanding Elasticsearch cluster, shards, and replicas
• Discussion of configuration, APIs, and local gateway

Multi-Tenancy

• Value of multiple indices, index aliases, and cross-index operations
• Introduction to data flow

Elasticsearch Index

• In-depth analysis of mappings, indexing, and operations
• Discussion of transaction logs and Lucene indexing
• Understanding configuration options, mappings, APIs, and available settings

Search

• Understanding search Query DSL In-depth understanding of search components: aggregations, search types, highlighting and other options.
• Overview of bitSets, filters and Lucene

Advanced Search and Mapping

• Introduction to aggregations and nested document relations
• Understanding nested objects and parent-child relationships
• The importance of geolocation, mapping, indexing query percolation, relevancy, searching, and more
• Advanced Distributed Model
• Cluster state recovery, low level replication, low-level recovery, and shard allocation
• How to approach data architecture
• Index templates, features, and functionality

Big Data Design Pattern

• In-depth content on multiple indices, overallocation, shard overallocation, node types, routing, replication, and aliases

Preparing for Production

• Discussion on capacity planning and data flow
• Performance tuning, more on data flow, and memory allocation.

Running in Production

• Installation, configuration, memory file descriptions, and hardware
• Monitoring, alerts, thread pools, information and stats API