Search, analyze, and visualize big data on a cluster with Elasticsearch, Logstash, Beats, Kibana, and more.
Created by: Sundog Education by Frank Kane
Produced in 2021
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 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
Overall Score : 88 / 100
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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.
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.
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 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