Once your connector is set up, the next step is to select the source tables to ingest data into Kumo.

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Connecting a Table

  1. Navigate to Tables in the side menu and click Add Table.
  2. Next, select a Source:
    • Connector – Choose an existing data connector.
    • Local Upload – Upload a local CSV or Parquet file.

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Selecting a Connector Source

If you choose Connector as the source type:

  1. Select a connector from the drop-down.
  2. Kumo will load available tables in the Source Table drop-down.
  3. (For Native Databricks users) – Enter the Schema Name to populate the table list.

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Column Selection

For optimal results, you should ensure that any table columns you select for Kumo ingestion meet the following criteria:

  • Clean: be sure to remove fake/synthetic data, predictions from other ML models, data for which the column definition has constantly changed over time (especially if a particular attribute ID may point to different things over time), and data that is known to be otherwise unreliable or frequently inaccurate.
  • Relevant and Mutually Exclusive: the larger the graph size (i.e., the sum across the tables in a graph), the larger the compute cost; to optimize training costs, remove columns that provide similar/duplicated information, irrelevant information, and other extraneous data.
  • Complete: the column should cover the full history across the timeframe in question (e.g., the whole record of purchases/interactions versus a user’s first/last purchase, or a subscriber’s most recent interaction). If this results in an oversized data set, you can provide Kumo with a compressed version that indicates changes in aggregate metrics over time (e.g., per day/week/month).

🚧Using the wrong or unnecessary columns can lead to both degraded model performance (due to noisy pQuery inputs, or worse—due to data leakage), as well as increased pQuery training costs.

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Column Preprocessing

After selecting a table, you can define column types and preprocessing steps to ensure proper data handling. For details, see Column Preprocessing.

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Debugging & Data Validation

To verify a table’s schema and metadata:

  1. Navigate to the Tables page.
  2. Click on the table name to view details.
  3. Click on a column to the see detailes Stats.

This section provides useful insights, such as:

  • Column statistics (e.g., missing values, cardinality, distributions).
  • Sample rows for verification.

Kumo computes this information from a smaller sample of the dataset. Complete statistics are available after full data ingestion during model training.

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Handling Missing Data

  • Kumo treats blank entries as missing values.
  • It does not automatically recognize special strings ("NaN", "none", "N/A") as missing.
  • For numeric columns, missing values are often filled with placeholder values (e.g., -1). These should be cleared or left blank if you want them treated as missing.