Using data prepared by the Event Enrich process, the Visitor Enrich process applies functions to the data, joins lookups and writes the data into a visitor level dataset, which can be thought of as a event data aggregated at the visitor level. This data is then grouped by day to provide ability to partition, which provides the ability to more quickly and easily analyze across specific time periods. As the user, you will find the data configured where there is one record per visitor ID per day the visitor ID was present.
Much of the configuration is the same as the Event Enrich configuration.
The Visitor Enrich process includes four screens providing the ability to join multiple datasets, map to the schema, apply desired filters, and understand where the data is being written. Below are details of each screen and descriptions of each of the fields.
This section provides the information Syntasa needs if more than one set of data will be joined.
To create a join, click the green plus button.
- Dataset selector - The first dataset connected on the graph will appear by default, click the down arrow to select a different dataset.
- Alias - type a table alias if a different name is desired or required.
- Join Type - left or inner join
- Dataset selector - choose the dataset that will be joined with first dataset
- Alias - type a table alias if a different name is desired or required
- Left Value - choose the field from the first dataset that will provide a link with the joined dataset (i.e. customer ID if joining a CRM dataset)
- Operator - select how the left value should be compared with the right value, for joins this will typically be an = sign
- Right Value - select the joining dataset value that is being compared with the left value
The Mapping screen is where the event data fields are mapped into the Syntasa fields, desired functions get applied, and user-friendly labels get created.
No actions exist for Visitor Enrich process at this time.
The table is where the event data is defined and labeled into the Syntasa schema. Syntasa has a growing set of custom functions that can be applied along with any Hive functions perform data transformation. It is best to consult Syntasa consulting before applying functions.
- Field - fixed Syntasa table column labels
- Label - customizable user-friendly names
- Function - raw file fields are mapped into the Syntasa columns.
Filters provides the ability to filter the dataset (aka apply a Where clause).
To create a filter click the green plus button and the filter editor screen will appear. Multiple filters can be applied, ensure the proper (AND/OR) logic is applied.
The Outputs tab provides the ability to name tables and displayed names on the graph canvas, along with selecting whether to load to Big Query (BQ) if in the the Google Cloud Platform (GCP).
The expected output of the Visitor Enrich process are the below tables within the environment the data is processed (e.g. AWS, GCP, on-prem Hadoop):
- tb_visitor_daily - visitor level table using Syntasa defined column names
- vw_visitor_daily - view built off tb_event providing user-friendly labels
These tables can be queried directly using an enterprise provided query engine and the tables can serve as the foundation for building other datasets, such as custom built datasets.