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.
This class has several hands-on supporting materials as you work through the videos, but our favorite is toward the end where we will show how to use Kibana to envision the data in our Elasticsearch index. Our challenge is to use the index for the complete works of William Shakespeare we set up in early in the class and use Kibana to envision which plays have the highest number of lines in them.
You can extend this to explore Shakespeare in other ways, too - what words are most common in Shakespeare's plays? Which character has the most lines? There's a lot of observation in Kibana which can give you, and you'll see it's not just limited to analyzing weblogs and time series data.