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

Search, analyze, and visualize big data on a cluster with Elasticsearch, Logstash, Beats, Kibana, and more

What you’ll learn

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

Requirements

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

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.