Elasticsearch 6 and Elastic Stack - In Depth and Hands On!

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8 часов курса
О курсе

NOTE: A NEW VERSION OF THIS COURSE FOR ELASTICSEARCH 7 IS AVAILABLE. You should only enroll in this if you need to learn Elasticsearch 6 specifically. If that's you, read on!

Elasticsearch 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 comprehensive course covers it all, from installation to operations, with 60 lectures including 8 hours of video.

We'll cover setting up search indices on an Elasticsearch 6 cluster (if you need Elasticsearch 5 - we have another course 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 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.

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.

Программа
Installing and Understanding Elasticsearch
We'll cover the main components of the Elastic Stack and take a look at Elasticsearch's architecture and main concepts. And, we'll actually install Elasticsearch on your own PC and see if it works!
Udemy 101: Getting the Most From This Course
Installing Elasticsearch [Step by Step]
We'll talk about why Elasticsearch is important and what you can expect from this course. Then, we'll install a virtual Ubuntu machine right on your own desktop PC, install Elasticsearch on it, and search the complete works of William Shakespeare!
Elasticsearch Overview
Let's look at the components of the Elastic Stack from a 30,000-foot level, and see how they all fit together.
Intro to HTTP and RESTful API's
Elasticsearch exposes a RESTful API, and we communicate with Elasticsearch using nothing but standard HTTPrequests and responses. Let's cover the basics of how that works.
Using Elasticsearch
We'll cover the logical concepts of Elasticsearch, how indices work, and different ways to interact with Elasticsearch.
Elasticsearch Architecture
Let's talk about how Elasticsearch scales horizontally on a cluster, using primary and replica shards.
Quiz: Elasticsearch Concepts and Architecture
Quiz time!Let's see what you learned about Elasticsearch at a conceptual level.
Mapping and Indexing Data
Create new search indices for structured and unstructured data, insert / update / delete documents, handle concurrency issues, and model relational data.
Connecting to your Cluster
We'll walk though setting up SSH on your server, and how to connect to it from your desktop.
Getting to Know the Movielens Data Set
The Movielens dataset will be used throughout the course; let's just familiarize ourselves with it.
Create a Mapping for MovieLens
We'll define a mapping, or a schema, in Elasticsearch for our movie data prior to importing it.
Требования
  • 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
Что Вы изучите?
  • Install and configure Elasticsearch 6 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
Лекторы
Sundog Education by Frank Kane
Sundog Education by Frank Kane
Founder, Sundog Education. Machine Learning Pro

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
Frank Kane
Founder, Sundog Education

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 computingdata 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.

Платформа
Udemy
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