Interana analyzes event data. The events are described by a combination of actions and contexts.
You can easily understand what is being done in each event by referring to a specific attribute (the action). You can define actions from an existing attribute in the raw data or you can define relevant actions from across multiple columns in unstructured raw data through the use of contexts.
You can access your data (like actors and event attributes) with cleaned up names and values that are familiar to you when you compose queries. You can then align the resulting names of your data to how you refer to it day-to-day in your business, rather than the technical names represented in the code. This makes it easier for everyone (both novices and power users) who aren't familiar with every piece of data in the system.
You can build contexts (event attributes) that clean up your data by transforming an attribute’s names and existing values post-ingest. The ability to do this after ingesting data alleviates the need to anticipate and address all problems at ingest, and means that you don't have to re-ingest data to make changes.
This data transformation sometimes necessitates bucketing various values into a new value or performing an arithmetic function on a value. When constructing these contexts, you can see examples from the underlying raw data (this feature will be added in the future) and the expected values output.
After building these new, cleaned up contexts, you can hide other contexts so the incomplete data isn't used in a query or object definition. This helps ensure that other users always refer to the correct data.
Raw and manual contexts
We differentiate between two types of contexts:
- A raw context is any information in your raw data that adds more definition to actions.
- A manual context is a context that you have created through a defined value and function.