Elasticsearch 7 and the Elastic Stack – In Depth & Hands On!

Elasticsearch 7 and the Elastic Stack – In Depth & Hands On!

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 16.5 Hours | 6.02 GB


Search, analyze, and visualize big data on a cluster with Elasticsearch, Logstash, Beats, Kibana, and more.

New for 2020! We’ve teamed up with Coralogix to co-produce the most comprehensive Elastic Stack course we’ve seen. 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 course covers it all, from installation to operations, with over 100 lectures including 11 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 and Kibana Lens.

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.

What you’ll learn

  • Install and configure Elasticsearch 7 on a cluster
  • Create search indices and mappings
  • Search full-text and structured data in several different ways
  • Import data into Elasticsearch using several different techniques
  • Integrate Elasticsearch with other systems, such as Spark, Kafka, relational databases, S3, and more
  • Aggregate structured data using buckets and metrics
  • Use Logstash and the “ELK stack” to import streaming log data into Elasticsearch
  • Use Filebeats and the Elastic Stack to import streaming data at scale
  • Analyze and visualize data in Elasticsearch using Kibana
  • Manage operations on production Elasticsearch clusters
  • Use cloud-based solutions including Amazon’s Elasticsearch Service and Elastic Cloud
+ Table of Contents

Installing and Understanding Elasticsearch
1 Udemy 101 Getting the Most From This Course
2 Section 1 Intro
3 Installing Elasticsearch [Step by Step]
4 Elasticsearch Overview
5 Intro to HTTP and RESTful API’s
6 Elasticsearch Basics Logical Concepts
7 Term Frequency Inverse Document Frequency (TFIDF)
8 Using Elasticsearch
9 What’s New in Elasticsearch 7
10 How Elasticsearch Scales
11 Quiz Elasticsearch Concepts and Architecture
12 Section 1 Wrapup

Mapping and Indexing Data
13 Section 2 Intro
14 Connecting to your Cluster
15 Introducing the MovieLens Data Set
16 Analyzers
17 Import a Single Movie via JSON REST
18 Insert Many Movies at Once with the Bulk API
19 Updating Data in Elasticsearch
20 Deleting Data in Elasticsearch
21 [Exercise] Insert, Update and Delete a Movie
22 Dealing with Concurrency
23 Using Analyzers and Tokenizers
24 Data Modeling and ParentChild Relationships, Part 1
25 Data Modeling and ParentChild Relationships, Part 2
26 Flattened Datatype
27 Dealing with Mapping Exceptions
28 Section 2 Wrapup

Searching with Elasticsearch
29 Section 3 Intro
30 Query Lite interface
31 JSON Search In-Depth
32 Phrase Matching
33 [Exercise] Querying in Different Ways
34 Pagination
35 Sorting
36 More with Filters
37 [Exercise] Using Filters
38 Fuzzy Queries
39 Partial Matching
40 Query-time Search As You Type
41 N-Grams, Part 1
42 N-Grams, Part 2
43 Search as you Type Field Type
44 Section 3 Wrapup

Importing Data into your Index – Big or Small
45 Section 4 Intro
46 Importing Data with a Script
47 Importing with Client Libraries
48 [Exercise] Importing with a Script
49 Introducing Logstash
50 Installing Logstash
51 Running Logstash
52 Logstash and MySQL, Part 1
53 Logstash and MySQL, Part 2
54 Importing CSV Data with Logstash
55 Importing JSON Data with Logstash
56 Logstash and S3
57 Parsing and Filtering Logstash with Grok
58 Logstash Grok Examples for Common Log Formats
59 Logstash Input Plugins, Part 1 Heartbeat
60 Logstash Input Plugins, Part 2 Generator Input and Dead Letter Queue
61 Logstash Input Plugins, Part 3 HTTP Poller
62 Logstash Input Plugins, Part 4 Twitter
63 Syslog with Logstash Deep Dive
64 Elasticsearch and Apache Hadoop
65 Elasticsearch and Kafka, Part 1
66 Elasticsearch and Kafka, Part 2
67 Elasticsearch and Apache Spark, Part 1
68 Elasticsearch and Apache Spark, Part 2
69 [Exercise] Importing Data with Spark
70 Section 4 Wrapup

Aggregation
71 Section 5 Intro
72 Aggregations, Buckets, and Metrics
73 Histograms
74 Time Series
75 [Exercise] Generating Histogram Data
76 Nested Aggregations, Part 1
77 Nested Aggregations, Part 2
78 Section 5 Wrapup

Using Kibana
79 Section 6 Intro
80 Installing Kibana
81 Playing with Kibana
82 [Exercise] Exploring Data with Kibana
83 Kibana Lens
84 Kibana Management
85 Elasticsearch SQL
86 Using Kibana Canvas
87 Section 6 Wrapup

Analyzing Log Data with the Elastic Stack
88 Section 7 Intro
89 Data Frame Transforms
90 FileBeat and the Elastic Stack Architecture
91 X-Pack Security
92 Installing FileBeat
93 Analyzing Logs with Kibana Dashboards
94 [Exercise] Log analysis with Kibana
95 Section 7 Wrapup

Elasticsearch Operations
96 Section 8 Intro
97 Choosing the Right Number of Shards
98 Adding Indices as a Scaling Strategy
99 Index Alias Rotation
100 Index Lifecycle Management
101 Choosing your Cluster’s Hardware
102 Heap Sizing
103 Monitoring
104 Troubleshooting Common Issues
105 Failover in Action, Part 1
106 Failover in Action, Part 2
107 Index Design Changes (Grouping, Splitting, and Shrinking Indices)
108 Snapshots
109 Snapshot Lifecycle Management
110 Rolling Restarts
111 Search Profiling
112 Uptime Monitoring with Heartbeat
113 Section 8 Wrapup

Elasticsearch in the Cloud
114 Section 9 Intro
115 Amazon Elasticsearch Service, Part 1
116 Amazon Elasticsearch Service, Part 2
117 The Elastic Cloud
118 Section 9 Wrapup

ELK on Kubernetes with Elastic Cloud on Kubernetes (ECK)
119 Introducing Elastic Cloud on Kubernetes (ECK), and setting up our cluster
120 Setting up Elasticsearch and Kibana on Kubernetes, and installing plugins
121 Using ECK Persistent Volumes and Setting Up a Multi-Node Elasticsearch Cluster

You Made It!
122 Wrapping Up
123 Bonus Lecture More Courses to Explore!