Elasticsearch 8 and the Elastic Stack: In Depth and Hands On
Elasticsearch 8 and the Elastic Stack: In Depth and Hands On, available at $99.99, has an average rating of 4.54, with 132 lectures, based on 5936 reviews, and has 46126 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 8 and the Elastic Stack: In Depth and Hands On
Summary
Title: Elasticsearch 8 and the Elastic Stack: In Depth and Hands On
Price: $99.99
Average Rating: 4.54
Number of Lectures: 132
Number of Published Lectures: 122
Number of Curriculum Items: 132
Number of Published Curriculum Objects: 122
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.
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 8is 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 8 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 15 hours of video.
We’ll show you how to set up search indices on an Elasticsearch 8 cluster (if you need Elasticsearch 6 or 7 – 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 8and illustrate all the new syntax requirements of Elasticsearch commands, now that things deprecated through the Elasticsearch 7 have been removed. Almost every hands-on activity has been re-recorded to ensure compatibility with Elasticsearch 8.
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, monitoring 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 Opensearch 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: Important note
Lecture 3: Section 1 Intro
Lecture 4: Installing Elasticsearch [Step by Step]
Lecture 5: Elasticsearch Overview
Lecture 6: Intro to HTTP and RESTful API's
Lecture 7: Elasticsearch Basics: Logical Concepts
Lecture 8: Term Frequency / Inverse Document Frequency (TF/IDF)
Lecture 9: Using Elasticsearch
Lecture 10: What's New in Elasticsearch 8
Lecture 11: How Elasticsearch Scales
Lecture 12: Quiz: Elasticsearch Concepts and Architecture
Lecture 13: 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: A note on entering CURL commands.
Lecture 7: Import a Single Movie via JSON / REST
Lecture 8: Insert Many Movies at Once with the Bulk API
Lecture 9: Updating Data in Elasticsearch
Lecture 10: Deleting Data in Elasticsearch
Lecture 11: [Exercise] Insert, Update and Delete a Movie
Lecture 12: Dealing with Concurrency
Lecture 13: Using Analyzers and Tokenizers
Lecture 14: Data Modeling and Parent/Child Relationships, Part 1
Lecture 15: Data Modeling and Parent/Child Relationships, Part 2
Lecture 16: Flattened Datatype
Lecture 17: Dealing with Mapping Exceptions
Lecture 18: 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: Logstash and MySQL, Part 1
Lecture 9: Logstash and MySQL, Part 2
Lecture 10: Importing CSV Data with Logstash
Lecture 11: Importing JSON Data with Logstash
Lecture 12: Logstash and S3
Lecture 13: Parsing and Filtering Logstash with Grok
Lecture 14: Workaround if the Grok Debugger app in the next lecture is down…
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: Syslog with Logstash Deep Dive
Lecture 20: Elasticsearch and Kafka, Part 1
Lecture 21: Elasticsearch and Kafka, Part 2
Lecture 22: Elasticsearch and Apache Spark, Part 1
Lecture 23: Elasticsearch and Apache Spark, Part 2
Lecture 24: [Exercise] Importing Data with Spark
Lecture 25: Using Elasticsearch 8 as a Vector Database with Embeddings and dense_vector
Lecture 26: 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
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: 70 votes
- 2 stars: 98 votes
- 3 stars: 469 votes
- 4 stars: 1882 votes
- 5 stars: 3417 votes
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