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Oreilly - Using Elasticsearch and Kibana - 9781789134438
Oreilly - Using Elasticsearch and Kibana
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
  8. Show and hide more

    Oreilly - Using Elasticsearch and Kibana


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