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Business Business Analytics & Intelligence Elasticsearch
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Elasticsearch 7 and the Elastic Stack: In Depth and Hands On

Complete Elasticsearch tutorial - search, analyze, and visualize big data with Elasticsearch, Kibana, Logstash, & Beats.
Bestseller
Rating: 4.5 out of 54.5 (3,100 ratings)
20,824 students
Created by Sundog Education by Frank Kane, Frank Kane, Coralogix Ltd.
Last updated 2/2021
English
English, French [Auto], 
30-Day Money-Back Guarantee

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
Curated for the Udemy for Business collection

Requirements

  • You need access to a Windows, Mac, or Ubuntu PC with 20GB of free disk space
  • You should have some familiarity with web services and REST
  • Some familiarity with Linux will be helpful
  • Exposure to JSON-formatted data will help

Description

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.

Who this course is for:

  • Any technologist who wants to add Elasticsearch to their toolchest for searching and analyzing big data sets.

Featured review

Angela-Maria Despotopoulou
Angela-Maria Despotopoulou
24 courses
4 reviews
Rating: 5.0 out of 5a year ago
I took this course because I will be applying those skills at work in the next few days. I wholeheartedly believe that the instructor and his team really put effort in thinking of an optimal format for the process, for it not to be boring while allowing space for hand-on experience. Moreover, Frank's articulation is very clear and comfortable to the ear, like narrating a story! Keep up and thank you!

Course content

11 sections • 124 lectures • 16h 27m total length

  • Preview02:10
  • Section 1 Intro
    00:44
  • Preview17:44
  • Preview05:44
  • Intro to HTTP and RESTful API's
    11:54
  • Preview02:04
  • Term Frequency / Inverse Document Frequency (TF/IDF)
    03:53
  • Using Elasticsearch
    04:05
  • What's New in Elasticsearch 7
    03:47
  • How Elasticsearch Scales
    07:33
  • Quiz: Elasticsearch Concepts and Architecture
    04:14
  • Section 1 Wrapup
    00:30

  • Section 2 Intro
    00:36
  • Connecting to your Cluster
    07:08
  • Note: alternate download location for the MovieLens data set
    00:09
  • Introducing the MovieLens Data Set
    03:59
  • Analyzers
    08:32
  • Import a Single Movie via JSON / REST
    10:31
  • Insert Many Movies at Once with the Bulk API
    05:34
  • Updating Data in Elasticsearch
    06:33
  • Deleting Data in Elasticsearch
    02:18
  • [Exercise] Insert, Update and Delete a Movie
    04:18
  • Dealing with Concurrency
    Preview10:25
  • Using Analyzers and Tokenizers
    10:52
  • Data Modeling and Parent/Child Relationships, Part 1
    05:29
  • Data Modeling and Parent/Child Relationships, Part 2
    07:00
  • Preview13:51
  • Dealing with Mapping Exceptions
    13:18
  • Section 2 Wrapup
    00:23

  • Section 3 Intro
    00:29
  • "Query Lite" interface
    08:10
  • JSON Search In-Depth
    10:19
  • Phrase Matching
    06:27
  • [Exercise] Querying in Different Ways
    04:31
  • Pagination
    06:23
  • Sorting
    08:00
  • More with Filters
    03:39
  • [Exercise] Using Filters
    02:45
  • Fuzzy Queries
    06:11
  • Partial Matching
    05:35
  • Query-time Search As You Type
    04:06
  • Preview05:21
  • N-Grams, Part 2
    08:11
  • Preview10:36
  • Section 3 Wrapup
    00:20

  • Section 4 Intro
    00:50
  • Importing Data with a Script
    08:22
  • Importing with Client Libraries
    06:41
  • [Exercise] Importing with a Script
    04:01
  • Preview04:54
  • Installing Logstash
    09:03
  • Preview05:16
  • Logstash and MySQL, Part 1
    08:00
  • Logstash and MySQL, Part 2
    07:47
  • Importing CSV Data with Logstash
    14:13
  • Importing JSON Data with Logstash
    20:42
  • Logstash and S3
    08:01
  • Parsing and Filtering Logstash with Grok
    14:55
  • Logstash Grok Examples for Common Log Formats
    34:18
  • Logstash Input Plugins, Part 1: Heartbeat
    07:34
  • Logstash Input Plugins, Part 2: Generator Input and Dead Letter Queue
    11:44
  • Logstash Input Plugins, Part 3: HTTP Poller
    07:53
  • Logstash Input Plugins, Part 4: Twitter
    08:57
  • Syslog with Logstash Deep Dive
    13:24
  • Preview30:06
  • Elasticsearch and Kafka, Part 1
    06:03
  • Elasticsearch and Kafka, Part 2
    06:02
  • Elasticsearch and Apache Spark, Part 1
    08:25
  • Elasticsearch and Apache Spark, Part 2
    05:58
  • [Exercise] Importing Data with Spark
    08:53
  • Section 4 Wrapup
    00:36

  • Section 5 Intro
    00:59
  • Aggregations, Buckets, and Metrics
    10:19
  • Histograms
    07:45
  • Time Series
    06:08
  • [Exercise] Generating Histogram Data
    04:27
  • Preview06:09
  • Nested Aggregations, Part 2
    08:45
  • Section 5 Wrapup
    00:23

  • Section 6 Intro
    00:20
  • Installing Kibana
    04:42
  • Playing with Kibana
    10:12
  • [Exercise] Exploring Data with Kibana
    03:25
  • Preview14:51
  • Kibana Management
    10:27
  • Elasticsearch SQL
    09:01
  • Preview25:21
  • Section 6 Wrapup
    00:21

  • Section 7 Intro
    00:31
  • Preview16:35
  • FileBeat and the Elastic Stack Architecture
    07:38
  • X-Pack Security
    03:16
  • Installing FileBeat
    06:04
  • Analyzing Logs with Kibana Dashboards
    Preview09:58
  • [Exercise] Log analysis with Kibana
    05:31
  • Section 7 Wrapup
    00:31

  • Section 8 Intro
    00:39
  • Choosing the Right Number of Shards
    05:15
  • Preview04:07
  • Index Alias Rotation
    03:58
  • Index Lifecycle Management
    02:15
  • Choosing your Cluster's Hardware
    03:22
  • Heap Sizing
    03:20
  • Monitoring
    06:30
  • Troubleshooting Common Issues
    39:58
  • Failover in Action, Part 1
    07:18
  • Failover in Action, Part 2
    08:46
  • Index Design Changes (Grouping, Splitting, and Shrinking Indices)
    22:05
  • Snapshots
    09:57
  • Snapshot Lifecycle Management
    26:10
  • Rolling Restarts
    06:45
  • Search Profiling
    11:55
  • Uptime Monitoring with Heartbeat
    20:39
  • Section 8 Wrapup
    00:29

  • Section 9 Intro
    00:58
  • Preview07:25
  • Amazon Elasticsearch Service, Part 2
    05:32
  • The Elastic Cloud
    09:54
  • Section 9 Wrapup
    00:11

  • Preview18:31
  • Setting up Elasticsearch and Kibana on Kubernetes, and installing plugins
    30:05
  • Using ECK Persistent Volumes and Setting Up a Multi-Node Elasticsearch Cluster
    24:26

Instructors

Sundog Education by Frank Kane
Founder, Sundog Education. Machine Learning Pro
Sundog Education by Frank Kane
  • 4.5 Instructor Rating
  • 101,161 Reviews
  • 454,541 Students
  • 22 Courses

Sundog Education's mission is to make highly valuable career skills in big data, data science, and machine learning accessible to everyone in the world. Our consortium of expert instructors shares our knowledge in these emerging fields with you, at prices anyone can afford. 

Sundog Education is led by Frank Kane and owned by Frank's company, Sundog Software LLC. Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis.

Due to our volume of students we are unable to respond to private messages; please post your questions within the Q&A of your course. Thanks for understanding.

Frank Kane
Founder, Sundog Education
Frank Kane
  • 4.5 Instructor Rating
  • 97,503 Reviews
  • 409,731 Students
  • 14 Courses

Frank spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers, all the time. Frank holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology, and teaching others about big data analysis.

Due to our volume of students, I am unable to respond to private messages; please post your questions within the Q&A of your course. Thanks for understanding.

Coralogix Ltd.
Logging Built For CI/CD
Coralogix Ltd.
  • 4.6 Instructor Rating
  • 3,100 Reviews
  • 20,821 Students
  • 1 Course

Coralogix provides an ML-supported logging platform that accelerates the delivery pipeline for cloud companies performing CI/CD. Beyond searching, visualizing and creating alerts, Coralogix detects anomalies in your data and provides automatic version benchmarks. Simplify your ELK stack and Grafana workflows with our 100% managed, scaled and secured platform and never get lost with 24/7 instant human reply.


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