Table Input - Maia Documentation

The Table Input transformation component lets you select columns from a table in your cloud data warehouse, including external tables, and makes the data available for use by the pipeline’s downstream components. You can add this component to your canvas by clicking the Add component icon and using the Warehouse data tab. In SQL terms, the component performs a SELECT FROM on the designated table. No filtering is performed as part of the Table Input, but the output can be passed to other components for further transformation operations. Table Input supports Snowflake Dynamic tables and Databricks Delta Live tables as input sources, in addition to regular tables. No specific additional configuration is needed to use these table types in the component.

Use case

Some common uses for Table Input include:

Extracting source data for transformation. Table Input reads data directly from the specified table and acts as the starting point of your transformation pipeline.
Joining data from multiple tables. Multiple Table Input components can feed into a Join component to combine the data into a single output. Note that in some scenarios it may be more efficient to do this with a Multi Table Input component rather than multiple Table Input components.

Properties

Snowflake
Databricks
Amazon Redshift

Name

string

required

A human-readable name for the component.

Database

drop-down

required

The Snowflake database. The special value [Environment Default] uses the database defined in the environment. Read Databases, Tables and Views - Overview to learn more.

Schema

drop-down

required

The Snowflake schema. The special value [Environment Default] uses the schema defined in the environment. Read Database, Schema, and Share DDL to learn more.

Target Table

drop-down

required

The name of the input table or view. The tables and views found in the currently selected environment are provided to choose from.

Column Names

dual listbox

required

The columns to include in the input. If you wish to rename the column names, consider using the Rename component to change them.

Time Offset

integer

Offsets the table contents by the number of specified seconds. This is a function of Snowflake’s Time Travel feature, allowing you to see a table as it was X seconds ago.

Name

string

required

A human-readable name for the component.

Catalog

drop-down

required

Select a Databricks Unity Catalog. The special value [Environment Default] uses the catalog defined in the environment. Selecting a catalog will determine which databases are available in the next parameter.

Schema Database

drop-down

required

The Databricks schema. The special value [Environment Default] uses the schema defined in the environment. Read Create and manage schemas to learn more.

Target Table

drop-down

required

The name of the input table or view. The tables and views found in the currently selected environment are provided to choose from.

Column Names

dual listbox

required

The columns to include in the input. If you wish to rename the column names, consider using the Rename component to change them.

Offset Type

drop-down

required

Select how the table contents will be offset. Options are:

None: No offset.
Timestamp: Unlocks the Timestamp Offset property.
Version: Unlocks the Version Offset property.

Timestamp Offset

integer

required

Enter a number of seconds to offset the table by. This is a function of Databricks Delta Lake’s Time Travel feature, allowing you to see a table as it was X seconds ago.

Version Offset

string

required

Enter the table version to offset to. This is a function of Databricks Delta Lake’s Time Travel feature, allowing you to see a table as it was at a defined version.

Name

string

required

A human-readable name for the component.

Database

drop-down

Cross-query two databases. Specify a separate database to the one selected in the connection (that uses the environment default database). For more information, read Querying data across databases. There are limitations as to whether an additional database can be queried. For more information, read Considerations and Limitations. These limitations will prevent cross database connections, and any deviation from the environment default database will fail.

You will need to be connected to either an RA3 or Serverless Redshift cluster type for a cross database connection to work.
On RA3 or Serverless Redshift clusters, you will also need the necessary permissions to view schemas and tables outside of your environment’s default database.

Schema

drop-down

required

Select the table schema. The special value [Environment Default] uses the schema defined in the environment. For more information on using multiple schemas, read Schemas.

Target Table

drop-down

required

The name of the input table or view. The tables and views found in the currently selected environment are provided to choose from.

Column Names

dual listbox

required

The columns to include in the input. If you wish to rename the column names, consider using the Rename component to change them.

Trim Columns

drop-down

required

Wraps the column names in a BTRIM function, which will strip out all the leading and trailing spaces. Read the Amazon Redshift documentation for details.

Test

​Use case

​Properties

Use case

Properties