Elasticsearch 7 and the Elastic Stack: Hands On

Frank Kane, Founder of Sundog Education, ex-Amazon

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98 Lessons (8h 33m)
    • 1. Intro: Installing and Understanding Elasticsearch

      0:44
    • 2. Installing Elasticsearch [Step by Step]

      17:35
    • 3. Please follow me on Skillshare!

      0:16
    • 4. Intro to HTTP and RESTful API's

      11:48
    • 5. Elasticsearch Basics: Logical Concepts

      1:58
    • 6. Elasticsearch Overview

      5:44
    • 7. Term Frequency / Inverse Document Frequency (TF/IDF)

      3:48
    • 8. Using Elasticsearch

      3:59
    • 9. What's New in Elasticsearch 7

      3:41
    • 10. How Elasticsearch Scales

      7:27
    • 11. Quiz: Elasticsearch Concepts and Architecture

      4:08
    • 12. Section 1 Wrapup

      0:30
    • 13. Intro: Mapping and Indexing Data

      0:36
    • 14. Connecting to your Cluster

      7:03
    • 15. Introducing the MovieLens Data Set

      3:53
    • 16. Analyzers

      8:27
    • 17. Import a Single Movie via JSON / REST

      10:25
    • 18. Insert Many Movies at Once with the Bulk API

      5:29
    • 19. Updating Data in Elasticsearch

      6:28
    • 20. Deleting Data in Elasticsearch

      2:15
    • 21. [Exercise] Insert, Update and Delete a Movie

      4:14
    • 22. Dealing with Concurrency

      10:20
    • 23. Using Analyzers and Tokenizers

      10:47
    • 24. Data Modeling and Parent/Child Relationships, Part 1

      5:24
    • 25. Data Modeling and Parent/Child Relationships, Part 2

      7:00
    • 26. Section 2 Wrapup

      0:23
    • 27. Intro: Searching with Elasticsearch

      0:29
    • 28. "Query Lite" interface

      8:05
    • 29. JSON Search In-Depth

      10:13
    • 30. Phrase Matching

      6:21
    • 31. [Exercise] Querying in Different Ways

      4:25
    • 32. Pagination

      6:18
    • 33. Sorting

      7:54
    • 34. More with Filters

      3:34
    • 35. [Exercise] Using Filters

      2:39
    • 36. Fuzzy Queries

      6:05
    • 37. Partial Matching

      5:30
    • 38. Query-time Search As You Type

      4:00
    • 39. N-Grams, Part 1

      5:16
    • 40. N-Grams, Part 2

      8:11
    • 41. Section 3 Wrapup

      0:20
    • 42. Intro: Importing Data

      0:50
    • 43. Importing Data with a Script

      8:16
    • 44. Importing with Client Libraries

      6:35
    • 45. [Exercise] Importing with a Script

      3:55
    • 46. Introducing Logstash

      4:50
    • 47. Installing Logstash

      8:57
    • 48. Running Logstash

      5:11
    • 49. Logstash and MySQL, Part 1

      7:55
    • 50. Logstash and MySQL, Part 2

      7:47
    • 51. Logstash and S3

      7:55
    • 52. Elasticsearch and Kafka, Part 1

      5:58
    • 53. Elasticsearch and Kafka, Part 2

      6:45
    • 54. Elasticsearch and Apache Spark, Part 2

      8:20
    • 55. Elasticsearch and Apache Spark, Part 2

      5:58
    • 56. [Exercise] Importing Data with Spark

      8:48
    • 57. Section 4 Wrapup

      0:36
    • 58. Intro: Aggregation

      0:59
    • 59. Aggregations, Buckets, and Metrics

      10:13
    • 60. Histograms

      7:39
    • 61. Time Series

      6:03
    • 62. [Exercise] Generating Histogram Data

      4:21
    • 63. Nested Aggregations, Part 1

      6:03
    • 64. Nested Aggregations, Part 2

      8:45
    • 65. Section 5 Wrapup

      0:23
    • 66. Intro: Using Kibana

      0:20
    • 67. Installing Kibana

      4:37
    • 68. Playing with Kibana

      10:06
    • 69. [Exercise] Log analysis with Kibana

      3:19
    • 70. Section 6 Wrapup

      0:21
    • 71. Intro: Analyzing Log Data with the Elastic Stack

      0:31
    • 72. FileBeat and the Elastic Stack Architecture

      7:33
    • 73. X-Pack Security

      3:10
    • 74. Installing FileBeat

      5:58
    • 75. Analyzing Logs with Kibana Dashboards

      9:52
    • 76. [Exercise] Log analysis with Kibana

      5:25
    • 77. Section 7 Wrapup

      0:31
    • 78. Intro: Elasticsearch Operations and SQL Support

      0:39
    • 79. Choosing the Right Number of Shards

      5:09
    • 80. Adding Indices as a Scaling Strategy

      4:02
    • 81. Index Alias Rotation

      3:52
    • 82. Index Lifecycle Management

      2:09
    • 83. Choosing your Cluster's Hardware

      3:17
    • 84. Heap Sizing

      3:14
    • 85. Monitoring

      6:25
    • 86. Elasticsearch SQL

      5:30
    • 87. Failover in Action, Part 1

      7:12
    • 88. Failover in Action, Part 2

      8:46
    • 89. Snapshots

      9:51
    • 90. Rolling Restarts

      6:39
    • 91. Section 8 Wrapup

      0:29
    • 92. Intro: Elasticsearch in the Cloud

      0:58
    • 93. Amazon Elasticsearch Service, Part 1

      7:20
    • 94. Amazon Elasticsearch Service, Part 2

      5:32
    • 95. The Elastic Cloud

      9:48
    • 96. Section 9 Wrapup

      0:11
    • 97. Wrapping Up

      3:54
    • 98. Let's Stay in Touch

      0:46

About This Class

New for 2019! Elasticsearch 7 is a powerful tool not only for powering search on big websites, but also for analyzing big data sets in a matter of milliseconds! It's an increasingly popular technology, and a valuable skill to have in today's job market. This comprehensive course covers it all, from installation to operations, with over 90 lectures including 8 hours of video.

We'll cover setting up search indices on an Elasticsearch 7 cluster (if you need Elasticsearch 5 or 6 - we have other courses on that), and querying that data in many different ways. Fuzzy searches, partial matches, search-as-you-type, pagination, sorting - you name it. And it's not just theory, every lesson has hands-on examples where you'll practice each skill using a virtual machine running Elasticsearch on your own PC.

We'll explore what's new in Elasticsearch 7 - including index lifecycle management, the deprecation of types and type mappings, and a hands-on activity with Elasticsearch SQL. We've also added much more depth on managing security with the Elastic Stack, and how backpressure works with Beats.

We cover, in depth, the often-overlooked problem of importing data into an Elasticsearch index. Whether it's via raw RESTful queries, scripts using Elasticsearch API's, or integration with other "big data" systems like Spark and Kafka - you'll see many ways to get Elasticsearch started from large, existing data sets at scale. We'll also stream data into Elasticsearch using Logstash and Filebeat - commonly referred to as the "ELK Stack" (Elasticsearch / Logstash / Kibana) or the "Elastic Stack".

Elasticsearch isn't just for search anymore - it has powerful aggregation capabilities for structured data. We'll bucket and analyze data using Elasticsearch, and visualize it using the Elastic Stack's web UI, Kibana.

You'll learn how to manage operations on your Elastic Stack, using X-Pack to monitor your cluster's health, and how to perform operational tasks like scaling up your cluster, and doing rolling restarts. We'll also spin up Elasticsearch clusters in the cloud using Amazon Elasticsearch Service and the Elastic Cloud.

Elasticsearch is positioning itself to be a much faster alternative to Hadoop, Spark, and Flink for many common data analysis requirements. It's an important tool to understand, and it's easy to use! Dive in with me and I'll show you what it's all about.