Overview

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Build practical Elasticsearch skills and learn to create, query, and manage distributed search solutions with the ELK Stack. You’ll begin by exploring NoSQL fundamentals and the core architecture of Elasticsearch, including clusters, nodes, indices, documents, mappings, and data types. Using Kibana Dev Tools, you’ll interact with Elasticsearch and test queries in real time. You’ll then examine how character filters, tokenizers, and token filters process text, and implement edge n-gram and synonym analyzers to improve search relevance and autocomplete. You’ll also configure cluster discovery and gateway settings to improve stability and prevent split-brain issues. As you progress, you’ll distinguish query and filter contexts and build match, term-level, range, prefix, geo_point, and geo_shape queries. You’ll use Cluster, Indices, Document, and Bulk APIs to monitor cluster health, manage shards and aliases, and perform efficient CRUD operations. Designed for beginners in NoSQL and IT professionals ready to advance their search expertise, this course uniquely connects distributed architecture, near real-time search, query precision, and practical troubleshooting. You’ll also understand how Elasticsearch works with Logstash and Kibana for real-time data ingestion, indexing, and visualization. Enroll to develop applicable skills for scalable, 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.