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  • Getting Started
    • What is Count?
    • Count FAQ
    • Intro to your workspace
    • Example canvases
    • Getting started guides
      • Set up your workspace and projects
        • 1. Review workspace settings
        • 2. Create and organise your projects
      • Canvas orientation
      • Your first ad hoc analysis
        • 1. Examples and templates
        • 2. Build your first queries
        • 3. Create visuals
        • 4. Caching, local cells and scheduling
        • 5. Collaborating with a stakeholder
      • Your first report
        • 1. Examples and templates
        • 2. Filters and control cells
        • 3. Sharing your report
        • 4. Alerts
  • Connect your data
    • Database connection overview
      • Athena
      • Azure Synapse
      • BigQuery
      • Databricks
      • Microsoft SQL Server
      • MySQL
      • PostgreSQL
      • Redshift
      • Snowflake
    • Refresh database schema
    • Upload CSV files
    • dbt integration
      • ☁️dbt Cloud integration
      • 👩‍💻dbt Core integration
  • Import & Export
    • Import from other tools
      • Import Miro files
      • Import SQL files
      • Import Google Sheets
      • Import Jupyter notebooks
    • Export code and results
      • Export compiled SQL and Jinja-SQL
      • Export CSV files
      • Export images and PDF files
  • THE CANVAS
    • Navigating the canvas
      • Canvas tool bar
      • Data sidebar
      • Customizing the canvas
    • Canvas objects
      • Cells
      • Text and markdown
      • Shapes and tools
      • Sticky notes
      • Frames
      • Images
      • Embeds
      • Stamps
      • Grouping objects
      • Object order and alignment
      • Locking objects
      • Scaling objects
      • Shared styles
    • Overviews
    • Templates
    • Count AI
    • Alerts and subscriptions
      • Slack integration
    • Keyboard shortcuts
  • Querying data
    • Cells overview
      • Dynamic query compilation engine
    • SQL cells
      • Referencing other cells
      • Jinja templating
      • SQL formatting
    • Python cells
    • Visual and low-code cells
      • Calculations in visuals and low-code cells
      • Joins in visuals and low-code cells
    • Control cells
      • Single and multiple selects
      • Date controls
      • Text, number, and boolean controls
      • Custom control cells
    • Local DuckDB cells
      • DuckDB on the server
    • Query caching and scheduling
    • Manage queries and results
    • Troubleshooting
  • Visualizing data
    • Visualization overview
    • Templated visuals
    • Custom visuals
      • Marks
      • Facet
      • Subplots
      • Style
      • Filters
    • Formatting a visual
      • Axes
        • Secondary Axis
      • Colors and labels
      • Legends
      • Tooltips
    • Column summaries
    • Dynamic text
    • Every Visual Under the Sun
  • Presenting and Reporting
    • Reports and Slides
  • Count Metrics
    • Intro to Count Metrics
    • Build and edit a catalog
    • Views
      • Creating views
      • Customizing views
    • Datasets
      • Creating datasets
    • Save changes to the catalog
      • Catalog validation
      • Version control
    • Exposing catalogs to the workspace
    • Caching in Count Metrics
    • Using the catalog
      • Explore from cell
  • Sharing and Permissions
    • Real-time collaboration
    • Comments
    • Sharing permissions
    • Shared links
    • Embedding canvases
  • History and Versions
    • Version control
    • Duplicating and merging
    • Data snapshots
  • Settings and administration
    • Workspace settings
      • Workspace members
      • Groups
      • Tags
      • Billing
      • Single sign-on (SSO)
        • Okta
        • Entra ID
        • JumpCloud
        • Google
        • Generic OIDC
      • Brand
    • Connection settings
    • Project settings
    • User settings
    • Roles and permissions
  • Quick guides
    • Interactive control guides
      • Date ranges
      • Date groupings
      • Search
      • Select All
  • Resources
    • Join the Slack community
  • Blog
  • Security overview
  • Terms of use
  • Pricing FAQ
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  • Innocuous change
  • Breaking change
  1. Count Metrics
  2. Save changes to the catalog

Catalog validation

Iterate your catalog safely with pre-commit validation.

PreviousSave changes to the catalogNextVersion control

Last updated 1 month ago

Count Metric's Validation Checker proactively identifies issues in reports before catalog changes are committed, ensuring that errors are flagged early and preventing faulty data from reaching users. By catching problems at the source, it helps maintain report accuracy, consistency, and reliability, reducing the need for last-minute fixes and building trust in the data.

When a change is committed, Count checks all connected cells for errors both before and after the update. The results are categorized into three types:

  • New - cells that will break as a result of the change.

  • Existing - cells that were already broken and remain broken.

  • Fixed - cells that were previously broken but are now resolved.

While the validation checker catches most issues, some errors, such as missing database tables, unavailable databases, or runtime issues (e.g., division by zero), can only be detected when queries are executed.

All errors across all canvases are reported, even those outside the user’s access. To assist with collaboration, a list of contributors for each affected canvas is provided

Innocuous change

Breaking change