When receiving data it is sometimes beneficial to know the types of values you can expect. This also may enable some more advanced use of the data after it's loaded into your cluster. This video examines setting up mappings of data types for log data.
- [Narrator] When it comes to data types in ElasticSearch,…there are four main categories I want to cover.…First, we have our core data types,…and these are the ones you might be familiar with,…such as text, numeric, Boolean, binary, and range.…In many cases, these data types cover all of the data…that you'll come across.…However, there are some additional types,…such as complex data types.…A complex data type is typically represented…as an array or an object,…or even a nested array of JSON objects.…
If you're familiar with working with data…in Hadoop and Hive,…you'll at least be familiar because these data types…show up all over the place in the big data world.…ElasticSearch also provides…Geo-point and Geo-shape data types.…The Geo-point data is good…for storing latitude and longitude,…while the Geo-shape is good for storing polygons,…the actual points and architectures…that you'll need to draw a shape.…There are also some specialized data types…in ElasticSearch for handling things such as IP addresses,…auto-complete suggestions on a website,…
In this course, join Ben Sullins as he dives into the inner workings of Elasticsearch combined with Kibana. Ben provides an overview of the architecture, and then goes over the different deployment methods, and how to best structure your data. From there, he demonstrates how to query data, and how to work with Kibana to present your insights.
- Reviewing key Elasticsearch concepts
- Running Elasticsearch in the cloud and locally
- Bulk loading data
- Setting up mappings of data types
- Querying data
- Running basic aggregations
- Creating visualizations and dashboards in Kibana