Elasticsearch: Build, Query & Optimize with ELK

Overview

By completing this course, learners will be able to design data models, configure clusters, build custom analyzers, and construct powerful queries in Elasticsearch. They will also master the use of Cluster, Indices, and Document APIs to manage distributed data efficiently. The course provides practical skills in configuring analyzers, avoiding split-brain issues, translating SQL queries into Elasticsearch DSL, and implementing advanced queries such as geo_point and geo_shape searches. This course benefits learners by bridging the gap between theory and hands-on application, enabling them to use Elasticsearch with Logstash and Kibana for real-time data ingestion, indexing, and visualization. Unlike traditional database training, this course emphasizes distributed architecture, near real-time search, and practical troubleshooting strategies. What makes this course unique is its comprehensive coverage of Elasticsearch fundamentals through to advanced APIs, combined with real-world scenarios that highlight scalability, performance optimization, and query precision. Whether you are a beginner in NoSQL systems or an IT professional aiming to go beyond basics, this course equips you with the knowledge and confidence to apply Elasticsearch effectively in modern data-driven environments.

Syllabus

Foundations of Elasticsearch and NoSQL

This module introduces learners to the ELK Stack, the basics of NoSQL, and the fundamental building blocks of Elasticsearch. Students will explore core concepts such as clusters, nodes, mappings, and data types, while also practicing with developer tools for real-time interaction.

Analyzers, Transactions, and Cluster Configuration

This module explores how analyzers process text for search, the importance of tokenizers and filters, and Elasticsearch’s approach to transactions. It also covers cluster settings, discovery configuration, and techniques to prevent the split-brain problem.

Querying in Elasticsearch

This module teaches learners to build and optimize queries in Elasticsearch. Topics include query vs. filter contexts, full-text queries, term-level queries, range queries, and advanced geospatial queries for real-world applications.

Data Modeling and Custom Analyzers

This module focuses on effective data modeling and designing custom analyzers. Learners will explore SQL-to-DSL translation, dynamic templates, and strategies for managing multiple custom analyzers to improve indexing and search accuracy.

Elasticsearch APIs and Document Management

This module introduces Elasticsearch APIs for managing clusters, indices, and documents. Students will learn how to monitor cluster health, manage indices with aliases, and perform CRUD operations with Document APIs and the Bulk API.