Create and Manage Data Products

This article provides you with a step-by-step guide on how to get started with Bobsled Sledhouse to create Data Products.

FEATURE IN PUBLIC PREVIEW:
• While in Public Preview, expect some limitations. Your feedback is crucial as we build the product.
• We look forward to your feedback and to helping you integrate Sledhouse into your data architecture. Feel free to reach out if you have any questions about setup, configuration, or advanced use cases. 

Prerequisites

• Bobsled Account: You must have access to Bobsled’s main application and the Sledhouse tab/feature enabled in your environment.

• To successfully create a Sledhouse Table, you must have at least one Data source and a Sledhouse Table preconfigured in Bobsled.

NOTE:
What you can see and do will differ based on your role and permissions.

Setup instructions

Step 1: Choose a method to define a Data Product

1. In the Bobsled Application, locate and select the Data Products Tab, then select the “Define Data Product” button

2. In the wizard, select the Sledhouse Table you’d like to define a Data Product on and click “continue.”

3. Choose a name for your Data Product (a human-friendly, internal name), an alias (a short, SQL-safe identifier), and a target name (the name of the object whenever it is shared). On this page, Bobsled offers two ways to define Data Products:

Option 1: Via Query Designer (no code)

In Interactive mode, a user can choose to include or exclude certain columns from a sledhouse Table in a data product and use a no-code query build to control the rows returned in the data product. This enables fulfillment teams to control data entitlements without writing any code. In the “Interactive mode” tab, two configuration options are available: columns in the data product and applying row filters.

1. Column selection

   • Column selection provides the following options for selecting which columns are available in a given data product.

   • Included / Excluded Columns:

     • Select which columns should be included or excluded from the data product. By default, all columns are included, and the user must select which columns to exclude. The buttons in the middle allow columns to be moved quickly between sections. Columns are also able to be dragged and dropped between the two squares.

   • Schema evolution:

     • Turning on schema evolution allows for automatically flowing new columns in a Sledhouse Table down to the data product. This allows for simpler management of data products where schemas are often evolving and new columns need to pass through to customers.

2. Row filtering:

   • Row filtering allows for using an easy-to-use query builder that allows for defining filters (predicates in an SQL statement) that control what data is returned. This feature will let you quickly define filters like “event_date is in the past 30 days”.

   • Multiple filters can be combined using AND or OR semantics to create almost any data product definition required. For more complex data products, the SQL builder and co-pilot (See Option 2) can be used.

Option 2: Via SQL Editor

1. In the “SQL editor” tab, a built-in SQL editor lets you write transformations using Snowflake-compatible syntax.

   • Reference your Sledhouse Tables by their aliases using {{ sht('alias') }} syntax.

   • Example:  

     sql
     Copy
     SELECT *
     FROM {{sht('sht_alias'}}
     WHERE state = 'CA'

2. Optionally use the AI-Assistant (Copilot), you can type a natural-language request, and Sledhouse will suggest SQL statements. You can edit or refine these suggestions as needed

3. Once happy with your SQL, and no errors are observed, click “Save“, and the Data Product is ready to share

TIP:
• Hovering on a Sledhouse Table will display a pop-over window with extra details the source object—including metadata.

• Selecting the ‘more’ icon will provide shortcuts to copy information (such as alias, or all columns).

QA Data Products

In both the Query Designer and SQL Studio, Bobsled provides a “Run” button alongside “Save” when creating Data Products. This allows users to preview a sample of up to 100 rows and see the total row count. The sample reflects the current Data Product definition—including selected columns and filters—enabling you to validate the configuration before saving.

Managing Data Products

On the Data Product page:

• Settings: This lets you revisit the configuration (e.g., the SQL and source Sledhouse Table) and make changes

• Schema: Displays all columns set for this Data Product

NOTE:
• Schema for SQL-generated Data Products is not available yet.

Delete a Data Product

1. Locate the Data Product you wish to delete, and click on the name to enter its detail page.

2. In the ‘settings’ tab, scroll down.

3. Click on the ‘Delete Data Product’ button at the bottom.

NOTE:
• Bobsled prevents the deletion of any Data Product that has any fulfilled Data Consumers by it.

• To proceed with deletion, all related fulfilled Data Products must first be unshared.

• For your convenience, Bobsled lists and links all dependencies to help you manage this process efficiently.

Schema Evolution

• Additive Schema Migration: With the query designer mode, data product creators can enable automatic schema migrations to add new columns to downstream data products.

Data Product successfully defined! You can now share it with a Data Consumer or set one up to begin fulfilling your data.