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Packt Publishing - Elasticsearch 5 and Elastic Stack - In Depth and Hands On

Packt Publishing - Elasticsearch 5 and Elastic Stack - In Depth and Hands On
English | Size: 2.01 GB
Category: CBTs
This comprehensive course covers it all, from installation to operations, with 60 lectures including 8 hours of video.

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. We'll cover setting up search indices on an Elasticsearch 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'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. 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.

What You Will Learn
* Install and configure Elasticsearch 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
(Buy premium account for maximum speed and resuming ability)

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Tags: publishing, elasticsearch, elastic

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