Using Elasticsearch and Kibana
Scalable Search and Analytics for Document Data
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
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
$ 94.99
per course
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