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Elasticsearch 7 and Elastic Stack
Frank Kane
  • Course duration: 8h 34m
Elasticsearch 7 and Elastic Stack teaches you to search, analyze, and visualize big data on a cluster with Elasticsearch, Logstash, Beats, Kibana, and more. Fuzzy searches, partial matches, search-as-you-type, pagination, sorting—this course covers them all. You’ll even learn how to take Elasticsearch beyond web search, using Elasticsearch as an alternative to Hadoop and Spark to aggregate and graph Petabytes of data in a matter of milliseconds. Fully updated and upgraded to Elasticsearch 7, this essential liveCourse adds highly marketable skills to your toolbox!


Distributed by Manning Publications

This course was created independently by big data expert Frank Kane and is distributed by Manning through our exclusive liveVideo platform.

Table of Contents detailed table of contents

Installing and understanding Elasticsearch

Introduction

Installing Elasticsearch [Step by Step]

Intro to HTTP and RESTful API’s

Elasticsearch basics: Logical concepts

The Elastic Stack

Term Frequency / Inverse Document Frequency (TF/IDF)

Using Elasticsearch

What’s new in Elasticsearch 7

How Elasticsearch scales

Quiz: Elasticsearch concepts and architecture

Wrap up

Mapping and indexing data

Intro

Connecting to your cluster

Introducing the MovieLens data set

Analyzers

Importing a single movie via JSON / REST

Insert many movies at once with Bulk API

Updating data in Elasticsearch

Deleting data in Elasticsearch

[Exercise} Insert, update and delete a movie.

Dealing with concurrency

Using analyzers and tokenizers

Data modeling and parent/child relationships Part 1

Data modeling and parent/child relationships Part 2

Wrap up

Searching with Elasticsearch

Introduction

"Query Lite" interface

JSON search in-depth

Phrase matching

[Exercise] Querying in different ways

Pagination

Sorting

More with filters

[Exercise] Using filters

Fuzzy queries

Partial matching

Query-time search as you type

N-Grams Part 1

N-Grams Part 2

Wrap up

Importing data into your index - big or small

Introduction

Importing data with a script

Importing with client libraries

[Exercise] Importing with a script

Introducing Logstash

Installing Logstash

Running Logstash

Logstash and MySQL, Part 1

Logstash and MySQL Part 2

Logstash and S#

Elasticsearch and Kafka Part 1

Elasticsearch and Kafka Part 2

Elasticsearch and Apache Spark Part 1

Elasticsearch and Apache Spark Part 2

[Exercise] Importing data with Spark

Wrap Up

Aggregation

Introduction

Aggregations, buckets and metrics

Histograms

Time series

[Exercise] Generating histogram data

Nested aggregations Part 1

Nested aggregations Part 2

Wrap up

Using Kibana

Introduction

Installing Kibana

Playing with Kibana

[Exercise] Exploring data with Kibana

Wrap up

Analyzing log data with Elastic Stack

Introduction

FileBeat and the Elastic Stack architecture

X-Pack security

Installing FileBeat

Analyzing logs with Kibana dashboards

[Exercise] Log analysis with Kibana

Wrap up

Elasticsearch operations and SQL support

Introduction

Choosing the right number of shards

Adding indices as a scaling strategy

Index aliast rotation

Index lifecycle management

Choosing you cluster’s hardware

Heap sizing

Monitoring

Elasticsearch SQL

Failover in action Part 1

Failover in action Part 2

Snapshots

Rolling restarts

Wrap up

Elasticsearch in the cloud

Introduction

Amazon Elasticsearch service Part 1

Amazon Elasticsearch service Part 2

The Elastic cloud

Wrap up

You made it!

Wrapping up

About the subject

Elasticsearch 7 is a powerful tool for powering search, capable of searching and indexing huge amounts of unstructured data—but there are more uses for Elasticsearch than websites. Increasingly, Elasticsearch is being used as a real-time alternative to Hadoop and Spark for analysing big data sets. Elasticsearch’s ability to aggregate and graph Petabytes of data in a matter of milliseconds has made it a valuable skill to have in today's job market.

About the video

Frank Kane brings his decade of experience at Amazon.com and IMBD.com to teach you Elasticsearch 7, from installation to operations. You’ll learn what's new in Elasticsearch 7, including tools for managing security with the Elastic stack, and cover the often-overlooked problem of importing data into an Elasticsearch index. You’ll discover that Elasticsearch isn't just for search anymore, learning to use its powerful aggregation capabilities for structured data, including bucketing and analyzing data, and visualizing it using the Kibana web UI. Fuzzy searches, partial matches, search-as-you-type, pagination, sorting—this course covers them all. 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.

Prerequisites

For tech-minded individuals familiar with web services and REST. Some exposure to Linux and JSON-formatted data would also be beneficial.

What you will learn

  • Install and configure Elasticsearch 7 on a cluster
  • Search full-text and structured data in different ways
  • Integrate Elasticsearch with Spark, Kafka, relational databases, S3, and other systems
  • Manage operations on production Elasticsearch clusters

About the instructor

Frank Kane holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. He spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to millions of customers every day. Sundog Software, his own company specializing in virtual reality environment technology and teaching others about big data analysis, is his pride and joy.

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