Core ElasticSearch for Operations Training | Learn to Set up, Use, & Scale Out Elasticsearch

Core ElasticSearch Operations Course Description

Elasticsearch is a search engine based on Lucene. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents. Elasticsearch can be used to search all kinds of documents. It provides scalable search, has near real-time search, and supports multitenancy. Elasticsearch is distributed, which means that indices can be divided into shards and each shard can have zero or more replicas. Each node hosts one or more shards, and acts as a coordinator to delegate operations to the correct shard(s). Re-balancing and routing are done automatically. Related data is often stored in the same index, which consists of one or more primary shards, and zero or more replica shards. Once an index has been created, the number of primary shards cannot be changed.

This instructor-led training course focused on learning ElasticSearch internals and applies to the role(s) of DBA, Developers, developer operations, and systems architect. Starts with an introduction to Elasticsearch and progresses on to concepts such as scaling, documents, and key Elasticsearch APIs and terms.

Core ElasticSearch Operations Learning Outcomes

  • Introduction to Elasticsearch
  • Exploring concepts such as scaling, high availability, and shard allocation.
  • Hands-on lab to install, manage, and operate Elasticsearch from the perspective of an administrator. 
  • Managing and operating Elasticsearch clusters.

Core ElasticSearch Operations Training - Suggested Audience

This training is ideal for database administrators, DevOps, and systems architects. Suggested attendees based on our past programs are:
  • Developers
  • Engineers
  • Software Architects
  • DBA
  • Developer Operations
  • Systems Architects

Core ElasticSearch Operations Training Duration

  • Open-House F2F (Public): 1/2 days
  • In-House F2F (Private): 1/2 days, for commercials please send us an email with group size to

Core ElasticSearch Operations Training - Prerequisites

  • Prior use of Elasticsearch is recommended, or completion of one of these courses:
  • Hands-on Workshop
  • Elastic Stack: Logging (On-Demand)
  • Comfort using the terminal or command line is recommended

System Setup Requirements

  • Laptop with connectivity to Wi-Fi and a modern web browser
  • Mac, Linux OS, or Windows 7 or later
  • A modern web browser
  • Java version 1.7u55 or later installed
  • At least 20% free disk space recommended

Core ElasticSearch training course outline includes:

1. Elasticsearch Overview 
  • What and Why 
  • Terminology: Documents, Index, Shards, Node, Cluster, Scale Up/Out

2. Operate: Configuring & Deploying 
  • Configuring Elasticsearch 
  • Deploying Elasticsearch 
  • Lab

3. Lucene: File System & Internals 
  • Inverted Index 
  • Indexing Internals and Near Real-Time Nature 
  • Transaction Log 
  • Deletes and Merges 
  • Doc Values and Field Data 
  • Lab

4. Index Management & Aliases 
  • Index Creation and Compression 
  • Dynamic Indexes 
  • Index Templates 
  • Aliases 
  • Lab

5. Node: Discovery, Types, and Cluster State 
  • Distributed Model and Discovery 
  • Master, Data, Client, and Tribe Nodes 
  • Master Election and Minimum Master Nodes 
  • Cluster State 
  • Shard Allocation 
  • Lab

6. Shards: Filtering, Awareness & Capacity Planning 
  • Filtering (Hot/Cold Architectures) 
  • Awareness (Rack) 
  • Forced Awareness (Data Center) 
  • Capacity Planning 
  • Time-Based Data

7. Backup: Snapshot and Restore 
  • High Availability vs. Backup 
  • Repository, Snapshot, and Restore 
  • Internals 
  • Lab

8. Production Monitoring 
  • APIs 
  • Marvel 
  • Alerting Best Practices 
  • JVM 
  • Query Performance 
  • Thread Pools 
  • Diagnosing Problems 
  • Lab

9. Production Operational Best Practices 
  • Memory 
  • Networking 
  • Disk 
  • Cloud 
  • Security 
  • Cluster Restart (Rolling and Full)
Keny White


Keny White is Professor of the Department of Computer Science at Boston University, where he has been since 2004. He also currently serves as Chief Scientist of Guavus, Inc. During 2003-2004 he was a Visiting Associate Professor at the Laboratoire d'Infomatique de Paris VI (LIP6). He received a B.S. from Cornell University in 1992, and an M.S. from the State University of New York at Buffalo.


After working as a software developer and contractor for over 8 years for a whole bunch of companies including ABX, Proit, SACC and AT&T in the US, He decided to work full-time as a private software trainer. He received his Ph.D. in Computer Science from the University of Rochester in 2001. "What I teach varies from beginner to advanced and from what I have seen, anybody can learn and grow from my courses".


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