Searching and Analyzing Data with Elasticsearch: Getting Started
Elasticsearch is a popular enterprise search engine, which allows you to build powerful search capability. This course focuses on understanding search components and algorithms from first principles, and applying these in practice using REST APIs.
What you'll learn
Elasticsearch is one of the most popular open source technologies, which allows you to build and deploy efficient and robust search quickly. In this course, Searching and Analyzing Data with Elasticsearch: Getting Started, you'll be introduced to Elasticsearch by learning the basic building blocks of search algorithms, and how the basic data structure at the heart of every search engine works. First, you'll cover how to install and set up a single node server, index and update documents whose contents you want to search, perform a variety of search queries on these document contents, and run analysis to extract insights from your data. Next, you'll explore the TF/IDF algorithm for search ranking and relevance, and the important factors which determine how a document is scored for every search term. Finally, you'll learn how Elasticsearch handles a variety of searches, such as full-text queries, term queries, compound queries, and filters. You'll also run analytical queries on interesting data subsets specified by search terms. By the end of this course, you'll have the necessary knowledge to utilize Elasticsearch in practice.
Table of contents
- Version Check 0m
- Prerequisites and Course Overview 3m
- A Brief History of Search 4m
- How Does Search Work? 4m
- The Inverted Index 6m
- Lucene, an Open Source Search Library 3m
- Introducing Elasticsearch 5m
- Installing and Setting up Elasticsearch 6m
- Basic Concepts in Elasticsearch 9m
- Monitoring the Health of the Cluster 4m
- Introducing the cURL Command Line Utility 5m
- Creating Indices 4m
- Adding Documents to an Index 6m
- Retrieving Whole and Partial Documents 3m
- Updating Whole and Partial Documents 5m
- Deleting Documents and Indices 3m
- Performing Bulk Operations on Documents 6m
- Bulk Indexing of Documents from a JSON File 3m
- Recap: How Search Works 3m
- The Query and Filter Context 3m
- Setting up Fake Data for Queries 5m
- Search Using Query Params 7m
- Search Using the Request Body 5m
- Source Filtering Document Contents 6m
- Full Text Searches 7m
- The TF/IDF Algorithm for Relevance 7m
- Queries with Common Terms 6m
- Boolean Compund Queries 5m
- Term Queries and the Boost Parameter 3m
- Search Using the Filter Context 4m
About the author
Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing ... more