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Using Elasticsearch and Kibana
1,659 students
What you'll learn
- 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
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Course content
7 sections • 59 lectures • 6h 8m total length
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
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
Who this course is for:
- 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