Setting up your Data for import

HockeyStack has three primary types of data. When importing data to HockeyStack from your data warehouse, aligning your source data to our internal data models helps us properly connect what you're importing to data that already exists in HockeyStack.

HockeyStack DataSync Import data types:

Type	Description
Website Actions	Each row represents a website action taken by a user or company
Metadata	General metrics that can't be connected to a single person or a company. Most prominent example is Campaign / Ad data, but this can also include other marketing activity. Each row is a timestamp and the combination of campaign, ad group, ad, and UTM information. There are three metrics that can be tracked for the timestamp: spend, clicks, and impressions.
Properties	Object properties, usually relating to companies or specific users. Each row is an object type, object value, and corresponding property. For example: object type: Company ID object value: that company's Salesforce Account ID property name: Company Name property value: 'Acme.inc'

How to best configure your data for import on HockeyStack

Each import has a schema and required fields to ensure that the data you import can be properly connected to a user or company if necessary. After choosing an import type, please ensure that the schema within your data warehouse matches the schema outlined in one of the import types listed below:

Import Website ActionsImport MetadataImport Properties

Indexing your Data

Recurring imports require a column specifically called added_at so that it can be clear what rows are newly added to your data source.

Further, table-based Data Warehouses (Snowflake or BigQuery) require this column to help with optimal indexing. (aka: How do we go over your data in the most cost-efficient fashion?)

Column Name	Column Value
added_at	This should be the date that this row was added to your data source. This may be the same value as date, if applicable.