Loonycorn – Using Elasticsearch and Kibana

Loonycorn – Using Elasticsearch and Kibana

Get Loonycorn – Using Elasticsearch and Kibana at Tenlibrary.com

Using Elasticsearch and Kibana

Scalable Search and Analytics for Document Data

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

Course Curriculum

You, This Course, and Us

• You, This Course, and Us (2:23)

Introducing Elasticsearch

• Course Outline (3:00)
• Course Materials
• A Brief History of Search (7:51)
• Steps in Search (8:14)
• Inverted Index (6:12)
• Using the Inverted Index (5:19)
• Lucene (7:20)
• Elasticsearch Introduced (5:37)
• Installing ES (8:43)
• Clusters and Nodes (5:43)
• Indices and Documents (8:26)
• Cluster Health (7:00)

CRUD Operations in Elasticsearch

• Curl (7:20)
• Create Index (8:15)
• Create Document (8:20)
• Retrieve Documents (5:23)
• Update Documents (8:18)
• Script Elements (4:40)
• Delete (4:34)
• mGet (4:39)
• The Bulk API (9:06)
• Bulk Loading (9:05)

The Query DSL (Domain-Specific Language)

• Search Recap (4:21)
• Random Data Gen (5:19)
• Contexts (5:52)
• Contexts (5:56)
• Query Params (7:15)
• Request Body (9:03)
• Source Filtering (8:32)
• Full Text Search_Match (4:10)
• Full Text Search_MatchPhrasePrefix (7:14)t
• Relevance (8:10)
• TfIdf (6:06)
• Common Terms (6:17)
• Boolean Compound Queries (6:42)
• Term Queries Boosting Terms (4:42)
• Filters (6:01)
• Wildcards (6:09)

Aggregations

• Types Of Aggregations (3:59)
• Metric Aggregations (7:12)
• Cardinality Aggregations (9:07)
• Bucketing Aggregations (5:31)
• Bucketing Aggregations_2 (6:09)
• Multilevel Nested Aggregations (5:13)
• FilterBucketAggs (6:43)

Elasticsearch and Python

• Pythonsetup (8:32)
• Create Index (4:58)
• Documents (5:07)
• Search_Count (4:40)

Kibana

• Kibana_elk (4:26)
• Kibana_Install (2:48)t
• Mapping (7:51)
• Loading Logs (6:37)
• Discovery (6:49)
• Visualize (7:00)
• Timelion (8:01)
• Dashboard (3:50)
• Anaconda and Pip (9:00)

Get Loonycorn – Using Elasticsearch and Kibana at Tenlibrary.com