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What are data models?

Cargo plays are powered by underlying data models, which are structured tables containing organized data. Data from these models can be imported into a play to create runs.
Cargo leverages a data warehouse infrastructure to host these models.
Cargo enables users to create data models from a variety of external and internal sources, including API endpoints, webhook events, and SQL queries on warehouse tables. This integration allows users to merge multiple data sources into a unified, cohesive model. By supporting data model relationships and custom SQL models, the platform offers a flexible approach to consolidating diverse datasets under a common schema.

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Data connectors

Among all the integrations available in Cargo, those available as storage connectors enable users to bring external data into a data model using a data loader. There are three types of data loaders available in Cargo: Native integrations: These loaders enable you to connect to a supported external API endpoint, fetch data, and import it into a data model. Webhook data loader: This loader allows you to listen for events from an external source and import the data into a data model. SQL data loader: This loader allows you to run SQL queries on tables in the data warehouse and import the results into a data model.

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Advanced features

Models support several advanced features, such as: Setting up a system of record: By default, Cargo will offer a Snowflake instance at the back to power your data models. You also have the option to use your warehouse instance as a system of record accessible by Cargo. Creating segments: Apply filters to data models to form segments, allowing for targeted plays based on specific criteria. Change-based triggers: Enable plays to respond in real-time to updates, additions, or deletions in the data, ensuring action based on the most pertinent signals. Set enrolment conditions: Track records to prevent players from processing the same data multiple times. Model relationships: Useful for creating a comprehensive view of your data. By connecting different models using common identifiers, you can create merged views of your customer data from different sources. Additional columns: Lets you add calculated fields or tags to existing rows in a model.
  • Custom columns: These columns are free-form columns that you can upsert data into from within a play.
  • Computed columns: Calculate values based on other columns in the model.
  • Metrics columns: Store aggregated data, such as sums, averages, or counts, based on the values in other columns in other models

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Use cases

Data models serve various business needs across different domains: CRM data loaders: Import customer information, deal pipelines, and sales activities from CRMs like Salesforce or HubSpot to create unified customer profiles. Intent signals data loaders: Capture website behavior, form submissions, and engagement metrics to identify prospects showing buying intent. List building data loaders: Aggregate contact lists from marketing campaigns, events, and lead generation activities for targeted outreach. Product data loaders: Query product catalogs, inventory levels, and usage analytics from your data warehouse to inform customer interactions and recommendations.