Intro to input tables


Input tables are currently supported by Snowflake and Databricks connections only.

Input tables are dynamic workbook elements that support structured data entry. They allow you to integrate new data points into your analysis and augment existing data from your data platform to facilitate rapid prototyping, advanced modeling, forecasting, what-if analysis, and more—without overwriting source data.

Use input tables as sources for tables, pivot tables, and visualizations, or incorporate the data using lookups and joins. And when you create warehouse views for input tables, you can reuse the manually entered data across your broader data ecosystem.

This document introduces empty, CSV, and linked input tables, which support a variety of use cases for ad hoc data entry. For information about creating and interacting with input tables, see Create new input tables and Edit existing input table columns.

Input tables overview

Input tables enable you to do the following:

  • Add new rows (empty and CSV input tables only)
  • Add new columns (including data entry, computed, row edit history, and system columns)
  • Upload and edit CSV data (max 200 MB, UTF-8 only)
  • Input values through keyboard entry
  • Paste values in up to 12,500 cells at once (500 rows and 25 columns)
  • Configure data entry permissions
  • Configure data validation
  • Protect columns to prevent edits

For information about using this functionality, see Create new input tables and Configure data governance options in input tables.

Empty input tables

Empty input tables are blank tables that support data entry in standalone tables independent of existing data. You can edit data at the cell level and add editable rows and columns to construct your table as you see fit.

CSV input tables (Beta)

CSV input tables also support data entry in standalone tables; however, they allow you to pre-populate the table with uploaded CSV data (max 200 MB, UTF-8 only). You can then edit the uploaded data at the cell level and add other editable rows and columns to construct your table as you see fit.

Linked input tables

Linked input tables support data entry alongside existing data from other elements in the same workbook.

As a child element, a linked input table includes one or more linked columns that reference data in the parent element. This includes a primary key column containing row identifiers that establish the table’s granularity. You can then add other columns to augment the linked data sourced from the parent element.

To maintain the data relationship between the input table rows and source data in the parent element, the primary key column must reference static values. All other linked columns can reference variable data, which is continually updated in the input table to reflect live data from the source.

Types of input table columns

All input tables support the following types of columns:

TypeDescriptionAvailable columns
Data entry columnSupports direct user input at the individual cell levelText, Number, Date, Checkbox
Computed columnGenerates values based on a user-defined formula or lookupCalculation, Via lookup
Row edit history columnDisplays system-generated metadata related to row editsLast updated at (timestamp), Last updated by (user)
System columnDisplays system-generated metadata related to table componentsRow ID

Frequently asked questions

Can I use input tables with data platform connections other than Snowflake and Databricks?

Input tables are currently compatible with Snowflake and Databricks connections only. Sigma plans to add support for BigQuery, Postgres, and Redshift connections.

Can I change the write-to location where input table data is stored?

No. Sigma currently writes input table data to Snowflake and Databricks in a predefined schema identified in the connection details (Admin > Connections page).

Can linked input tables overwrite data in existing database tables?

No. Sigma protects the integrity of your existing data and stores input table data in a separate schema and database table. As a result, input tables can never overwrite source data.

Why isn’t there a view of published input tables in the connection panel (in the Sigma interface) that displays all database and schemas associated with my Snowflake or Databricks account?

When Sigma writes input table data to Snowflake or Databricks, the corresponding database table is created in a write-to schema not directly accessible from the Sigma interface. To identify the destination schema for input table data in Snowflake or Databricks, reference the connection details in the Admin > Connections page.

Can I query the input table data from my data platform?

While you cannot query the database table directly, you can create a warehouse view for the input table and query that to retrieve the data stored in Snowflake or Databricks. For more information, see Create and manage workbook warehouse views.

How long is input table data retained in a Snowflake or Databricks database table after I delete it in Sigma?

Sigma doesn’t delete input table data from a connected data platform. To remove input table data from Snowflake or Databricks, you must delete it directly in the data platform.