by Loonycorn | Released February 2018 | ISBN: 9781789134438

Scalable Search and Analytics for Document DataAbout This VideoHow search works, and the role that inverted indices and relevance scoring playThe tf-idf algorithm and the intuition behind term frequency, inverse document frequency and field lengthHorizontal scaling using sharding and replicationPowerful querying functionality including a query-DSLUsing REST APIs - from browser as well as from cURLKibana for exploring data and finding insightsSupport for CRUD operations - Create, Retrieve, Update and DeleteAggregations - metrics, bucketing and nested aggsPython client usageIn DetailElasticsearch 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 and ES as data warehouse/OLAP technology. Show and hide more

1. Chapter 1 : Introduction
   • You, This Course and Us 00:02:23
2. Chapter 2 : Introducing Elasticsearch
   • Course Outline 00:03:01
   • A Brief History of Search 00:07:52
   • Steps in Search 00:08:15
   • Inverted Index 00:06:13
   • Using the Inverted Index 00:05:20
   • Lucene 00:07:20
   • Elasticsearch Introduced 00:05:38
   • Installing ES 00:08:43
   • Clusters and Nodes 00:05:43
   • Indices and Documents 00:08:27
   • Cluster Health 00:07:01
3. Chapter 3 : CRUD Operations in Elasticsearch
   • Curl 00:07:20
   • Create Index 00:08:15
   • Create Document 00:08:21
   • Retrieve Documents 00:05:23
   • Update Documents 00:08:19
   • Script Elements 00:04:41
   • Delete 00:04:35
   • mGet 00:04:40
   • The Bulk API 00:09:06
   • Bulk Loading 00:09:06
4. Chapter 4 : The Query DSL (Domain-Specific Language)
   • Search Recap 00:04:21
   • Random Data Gen 00:05:20
   • Contexts 00:05:52
   • Contexts 00:05:57
   • Query Params 00:07:15
   • Request Body 00:09:03
   • Source Filtering 00:08:32
   • Full Text Search_Match 00:04:10
   • Full Text Search_MatchPhrasePrefix 00:07:14
   • Relevance 00:08:10
   • TfIdf 00:06:06
   • Common Terms 00:06:17
   • Boolean Compound Queries 00:06:43
   • Term Queries Boosting Terms 00:04:43
   • Filters 00:06:02
   • Wildcards 00:06:10
5. Chapter 5 : Aggregations
   • Types Of Aggregations 00:03:59
   • Metric Aggregations 00:07:13
   • Cardinality Aggregations 00:09:07
   • Bucketing Aggregations 00:05:32
   • Bucketing Aggregations_2 00:06:10
   • Multilevel Nested Aggregations 00:05:13
   • FilterBucketAggs 00:06:44
6. Chapter 6 : Elasticsearch and Python
   • Pythonsetup 00:08:33
   • Create Index 00:04:59
   • Documents 00:05:07
   • Search_Count 00:04:40
7. Chapter 7 : Kibana
   • Kibana_elk 00:04:27
   • Kibana_Install 00:02:48
   • Mapping 00:07:52
   • Loading Logs 00:06:38
   • Discovery 00:06:49
   • Visualize 00:07:00
   • Timelion 00:08:01
   • Dashboard 00:03:50
   • Anaconda and Pip 00:09:00
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