Elasticsearch 7 and the Elastic Stack: In Depth and Hands On

Elasticsearch 7 and the Elastic Stack: In Depth and Hands On, available at $59.99, has an average rating of 4.6, with 127 lectures, based on 78 reviews, and has 1148 subscribers.

You will learn about 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 various 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 This course is ideal for individuals who are Any technologist tasked with fast, scalable searching and analysis of big data sets. It is particularly useful for Any technologist tasked with fast, scalable searching and analysis of big data sets.

Enroll now: Elasticsearch 7 and the Elastic Stack: In Depth and Hands On

Summary

Title: Elasticsearch 7 and the Elastic Stack: In Depth and Hands On

Price: $59.99

Average Rating: 4.6

Number of Lectures: 127

Number of Published Lectures: 127

Number of Curriculum Items: 127

Number of Published Curriculum Objects: 127

Original Price: $22.99

Quality Status: approved

Status: Live

What You Will 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 various 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

Who Should Attend

Any technologist tasked with fast, scalable searching and analysis of big data sets.

Target Audiences

Any technologist tasked with fast, scalable searching and analysis of big data sets.

THERE IS AN UPDATED VERSION OF THIS COURSE AVAILABLE! Please search for “Elasticsearch 8 and the Elastic Stack” unless you specifically need to learn Elasticsearch 7.

——————–

Elasticsearchand the Elastic Stackare important tools for managing massive data. You need to know the problems it solves and how it works to design the best systems, and be the most valuable engineer you can be.

Elasticsearch 7is a powerful tool for analyzing big data sets in a matter of milliseconds! It’s increasingly popular technology for powering search and analytics on big websites, and a valuable skill to have in today’s job market. This course covers it all, from installation to operations. Learn how to use Elasticsearch 7 and implement it in your work within the next few days.

We’ve teamed up with Coralogixto co-produce the most comprehensive Elastic Stack course we’ve seen— with over 100 lectures including 16 hours of video.

We’ll show you how to set up search indices on an Elasticsearch 7 cluster (if you need Elasticsearch 6 or 8 – we have other courses on that), and query 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 Logstashand 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 aggregationcapabilities for structured data, which allows you to glean new insights from your indexed 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.

Course Curriculum

Chapter 1: Installing and Understanding Elasticsearch

Lecture 1: Udemy 101: Getting the Most From This Course

Lecture 2: Section 1 Intro

Lecture 3: Installing Elasticsearch [Step by Step]

Lecture 4: Elasticsearch Overview

Lecture 5: Intro to HTTP and RESTful API's

Lecture 6: Elasticsearch Basics: Logical Concepts

Lecture 7: Term Frequency / Inverse Document Frequency (TF/IDF)

Lecture 8: Using Elasticsearch

Lecture 9: What's New in Elasticsearch 7

Lecture 10: How Elasticsearch Scales

Lecture 11: Quiz: Elasticsearch Concepts and Architecture

Lecture 12: Section 1 Wrapup

Chapter 2: Mapping and Indexing Data

Lecture 1: Section 2 Intro

Lecture 2: Connecting to your Cluster

Lecture 3: Note: alternate download location for the MovieLens data set

Lecture 4: Introducing the MovieLens Data Set

Lecture 5: Analyzers

Lecture 6: Import a Single Movie via JSON / REST

Lecture 7: Insert Many Movies at Once with the Bulk API

Lecture 8: Updating Data in Elasticsearch

Lecture 9: Deleting Data in Elasticsearch

Lecture 10: [Exercise] Insert, Update and Delete a Movie

Lecture 11: Dealing with Concurrency

Lecture 12: Using Analyzers and Tokenizers

Lecture 13: Data Modeling and Parent/Child Relationships, Part 1

Lecture 14: Data Modeling and Parent/Child Relationships, Part 2

Lecture 15: Flattened Datatype

Lecture 16: Dealing with Mapping Exceptions

Lecture 17: Section 2 Wrapup

Chapter 3: Searching with Elasticsearch

Lecture 1: Section 3 Intro

Lecture 2: "Query Lite" interface

Lecture 3: JSON Search In-Depth

Lecture 4: Phrase Matching

Lecture 5: [Exercise] Querying in Different Ways

Lecture 6: Pagination

Lecture 7: Sorting

Lecture 8: More with Filters

Lecture 9: [Exercise] Using Filters

Lecture 10: Fuzzy Queries

Lecture 11: Partial Matching

Lecture 12: Query-time Search As You Type

Lecture 13: N-Grams, Part 1

Lecture 14: N-Grams, Part 2

Lecture 15: "Search as you Type" Field Type

Lecture 16: Section 3 Wrapup

Chapter 4: Importing Data into your Index – Big or Small

Lecture 1: Section 4 Intro

Lecture 2: Importing Data with a Script

Lecture 3: Importing with Client Libraries

Lecture 4: [Exercise] Importing with a Script

Lecture 5: Introducing Logstash

Lecture 6: Installing Logstash

Lecture 7: Running Logstash

Lecture 8: ERRATA for following lecture

Lecture 9: Logstash and MySQL, Part 1

Lecture 10: Logstash and MySQL, Part 2

Lecture 11: Importing CSV Data with Logstash

Lecture 12: Importing JSON Data with Logstash

Lecture 13: Logstash and S3

Lecture 14: Parsing and Filtering Logstash with Grok

Lecture 15: Logstash Grok Examples for Common Log Formats

Lecture 16: Logstash Input Plugins, Part 1: Heartbeat

Lecture 17: Logstash Input Plugins, Part 2: Generator Input and Dead Letter Queue

Lecture 18: Logstash Input Plugins, Part 3: HTTP Poller

Lecture 19: Logstash Input Plugins, Part 4: Twitter

Lecture 20: Syslog with Logstash Deep Dive

Lecture 21: If you run into trouble at the end of the next exercise…

Lecture 22: Elasticsearch and Kafka, Part 1

Lecture 23: Elasticsearch and Kafka, Part 2

Lecture 24: Elasticsearch and Apache Spark, Part 1

Lecture 25: Elasticsearch and Apache Spark, Part 2

Lecture 26: [Exercise] Importing Data with Spark

Lecture 27: Section 4 Wrapup

Chapter 5: Aggregation

Lecture 1: Section 5 Intro

Lecture 2: Aggregations, Buckets, and Metrics

Lecture 3: Histograms

Lecture 4: Time Series

Lecture 5: [Exercise] Generating Histogram Data

Lecture 6: Nested Aggregations, Part 1

Lecture 7: Nested Aggregations, Part 2

Lecture 8: Section 5 Wrapup

Chapter 6: Using Kibana

Lecture 1: Section 6 Intro

Lecture 2: Installing Kibana

Lecture 3: Playing with Kibana

Lecture 4: [Exercise] Exploring Data with Kibana

Lecture 5: Kibana Lens

Lecture 6: Kibana Management

Lecture 7: Elasticsearch SQL

Lecture 8: Using Kibana Canvas

Lecture 9: Elasticsearch and Apache Hadoop

Lecture 10: Section 6 Wrapup

Chapter 7: Analyzing Log Data with the Elastic Stack

Lecture 1: Section 7 Intro

Lecture 2: Data Frame Transforms

Lecture 3: FileBeat and the Elastic Stack Architecture

Instructors

Sundog Education by Frank Kane
Join over 800K students learning ML, AI, AWS, and Data Eng.
Frank Kane
Ex-Amazon Sr. Engineer and Sr. Manager, CEO Sundog Education
Coralogix Ltd.
Logging Built For CI/CD
Sundog Education Team
Sundog Education Team

Rating Distribution

1 stars: 2 votes
2 stars: 1 votes
3 stars: 8 votes
4 stars: 25 votes
5 stars: 42 votes

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