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When setting up an object, you can mark specific attributes as Measures or Dimensions to enhance query efficiency and help Ana understand your data model better.
  • Measures - Numeric columns that contain quantitative data to be aggregated. For example, total_revenue, order_count, average_rating
  • Dimensions - Categorical columns used to slice, filter, or group data. For example, region_name, customer_age_group, product_category

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Marking Attributes

To mark attributes as measures or dimensions:
  1. Click on an object to open the Object Sidebar
  2. Go to the “Attributes” tab
  3. Click the “meas” button next to an attribute to mark it as a measure
  4. Click the “dim” button next to an attribute to mark it as a dimension
  5. Changes are saved automatically
The buttons are highlighted when active.

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Benefits of Defining Metrics and Dimensions

Specifying metrics and dimensions improves the performance and usability of queries by enabling faster identification of relevant data areas. For instance, if a user asks, “Split total orders by region and plot on a map,” Ana can quickly identify:
  • total_orders as the measure to aggregate
  • region_name as the dimension to group by
When metrics and dimensions are pre-defined, query performance improves significantly, particularly for large datasets.

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Best Practices for Large Datasets

For datasets with over 10 million rows, defining metrics and dimensions is strongly recommended. This approach:
  • Increases the reliability of Ana
  • Reduces message latency
  • Allows Ana to load more data efficiently into the Python sandbox
By structuring datasets with clear metrics and dimensions, you can streamline data exploration and enhance the overall query experience.