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.
- 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
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