Stores events in the internal data lake so that data can be visualized in the live dashboard or in the data explorer. Simply create a pipeline with a data lake sink, switch to one of the data exploration tool and start exploring your data!
This sink requires an event that provides a timestamp value (a field that is marked to be of type http://schema .org/DateTime.
The fields which will be stored as dimensional values in the time series storage. Dimensions are typically identifiers such as the ID of a sensor. Dimensions support grouping in the data explorer, but will be converted to a text-based field and provide less advanced filtering capabilities.
Be careful when modifying dimensions of existing pipelines! This might have impact on how you are able to view data in the data explorer due to schema incompatibilities.
The name of the measurement (table) where the events are stored.
The Schema Update Options dictate the behavior when encountering a measurement (table) with the same identifier.
Select fields which will be marked as dimensions. Dimensions reflect tags in the underlying time-series database. Dimensions support grouping operations and can be used for fields with a limited set of values, e.g., boolean flags or fields representing IDs. Dimensions are not a good choice for fields with a high number of different values since they slow down database queries.
By default, all fields which are marked as dimensions in the metadata are chosen and can be manually overridden with this configuration.
Data types which can be marked as dimensional values are booleans, integer, and strings.
Before writing an event to the time series storage, a duplicate check is performed. By activating this option, only fields having a different value than the previous event are stored. This setting only affects measurement fields, not dimensions.