Search, analyze, and visualize big data on a cluster with Elasticsearch, Logstash, Beats, Kibana, and more.
Elasticsearch 7 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.
We will cover setting up search indices on an Elasticsearch 7 cluster, 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 will 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 will 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 will bucket and analyze data using Elasticsearch, and visualize it using the Elastic Stack's web UI, Kibana.
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!
The supporting files for this course are available at - https://github.com/PacktPublishing/Elasticsearch-7-and-Elastic-Stack---In-Depth-and-Hands-On-
- 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
- Learn how to manage operations on your Elastic Stack, using X-Pack to monitor your cluster's health.
- Learn 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.