Foundations of Elasticsearch
Learn Elasticsearch fundamentals to search, analyze, and manage large datasets with hands-on mapping and querying.
Learn Elasticsearch fundamentals to search, analyze, and manage large datasets with hands-on mapping and querying.
This comprehensive course provides a thorough introduction to Elasticsearch, a powerful search and analytics engine designed for handling large-scale data. Starting with installation and configuration, students will learn the core architectural concepts that make Elasticsearch an effective tool for search operations. The curriculum progresses through essential topics including RESTful APIs, data mapping, indexing strategies, and advanced search capabilities. Through hands-on experience with real-world datasets like MovieLens, participants will gain practical skills in connecting to clusters, importing data, managing concurrency, and implementing effective search queries. Special attention is given to optimization techniques such as analyzers, tokenizers, and query refinement methods that enhance search performance. By course completion, students will be equipped with the knowledge to implement Elasticsearch in various scenarios, making them valuable assets in data-driven environments.
Instructors:
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What you'll learn
Install and configure Elasticsearch in various environments
Interact with Elasticsearch clusters using RESTful APIs
Design efficient mapping strategies for optimized data organization
Implement bulk operations for handling large datasets
Manage concurrency issues in multi-user environments
Apply analyzers and tokenizers to improve search quality
Skills you'll gain
This course includes:
4.5 Hours PreRecorded video
1 assignment
Access on Mobile, Tablet, Desktop
Batch access
Shareable certificate
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There are 3 modules in this course
This course provides a comprehensive introduction to Elasticsearch, structured around three key areas. The first section covers installation, configuration, and core architectural concepts, including RESTful APIs and the Term Frequency/Inverse Document Frequency algorithm. Students then progress to mapping and indexing techniques, learning to efficiently organize and manage data through various methods such as bulk operations, updates, and deletions. The course also explores analyzers, tokenizers, and advanced data modeling approaches including parent-child relationships. The final module focuses on search capabilities, teaching students how to execute and optimize queries using techniques like phrase matching, fuzzy searches, partial matching, and filters. Throughout the course, practical examples using the MovieLens dataset provide hands-on experience, preparing students to implement Elasticsearch in real-world scenarios.
Installing and Understanding Elasticsearch
Module 1 · 1 Hours to complete
Mapping and Indexing Data
Module 2 · 1 Hours to complete
Searching with Elasticsearch
Module 3 · 2 Hours to complete
Instructor
Enhancing IT Education Through Expert-Led Learning
Packt Course Instructors are dedicated to delivering high-quality educational content across a wide range of IT topics, offering over 5,000 eBooks and courses designed to improve student outcomes in technology-related fields. With a focus on practical knowledge, instructors leverage their industry expertise to create engaging learning experiences that help students grasp complex concepts and apply them effectively. The courses cover diverse subjects, from programming languages to advanced data analysis, ensuring that learners at all levels can find relevant resources to enhance their skills. Additionally, Packt emphasizes personalized learning paths and provides analytics tools for educators to monitor student engagement and success, making it a valuable partner in academic settings.
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