Using Elasticsearch and Kibana

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Introduction

What are the requirements? A basic understanding of HTTP and JSON (Javascript Object Notation) Python is helpful for the portions of the course that deal with the ES Python client What is the target audience? Developers looking to add robust enterprise search functionality Business analysts looking to use ES and Kibana for business intelligence Data professionals looking to use the ElasticSearch search engine

Description

Elasticsearch wears two hats: It is both a powerful search engine built atop Apache Lucene, as well as a serious data warehousing/BI technology. This course will help you use the power of ES in both contexts ES as search engine technology: How search works, and the role that inverted indices and relevance scoring play The tf-idf algorithm and the intuition behind term frequency, inverse document frequency and field length Horizontal scaling using sharding and replication Powerful querying functionality including a query-DSL Using REST APIs - from browser as well as from cURL ES as data warehouse/OLAP technology: Kibana for exploring data and finding insights Support for CRUD operations - Create, Retrieve, Update and Delete Aggregations - metrics, bucketing and nested aggs Python client usage

Goal of Course

Construct robust, scalable search for production use in web and enterprise apps Query ES using the ES Domain Specific Language Perform aggregations to extract insights and run analytics on ES Interface with ES using Python

You, This Course, and Us

Introducing Elasticsearch

CRUD Operations in Elasticsearch

The Query DSL (Domain-Specific Language)

Aggregations

Elasticsearch and Python

Kibana