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For Snowflake projects, this component is now deprecated and is superseded by the Marketo Load component, which offers both full and incremental loading.Existing pipelines using the Marketo Query component will continue to work as expected, but new pipelines must use the Marketo Load component instead. When opening a pipeline containing the Marketo Query component, it may appear grayed out. This indicates that you should replace the existing component with the new Marketo Load component.Databricks and Amazon Redshift projects should continue to use the Marketo Query component.
The Marketo Query component uses the Connect and Configure parameters to create a table of Marketo data, which is then stored in your preferred storage location (Snowflake, Databricks, or Amazon Redshift). You do not need to use the Create Table component when using this connector, as each time the Marketo Query component runs, the target table is recreated, dropping any existing table of the same name. Once the component has run once, you can use transformation pipelines to transform your data to fit your business requirements. If the component requires access to a cloud provider (AWS, Azure, or GCP), it will use credentials as follows:
  • If using Matillion Full SaaS: The component will use the cloud credentials associated with your environment to access resources.
  • If using Hybrid SaaS: By default the component will inherit the agent’s execution role (service account role). However, if there are cloud credentials associated to your environment, these will overwrite the role.
This component is potentially destructive. If the target table undergoes a change in structure, it will be recreated. Otherwise, the target table is truncated. Setting the load option Recreate Target Table to Off will prevent both recreation and truncation. Do not modify the target table structure manually.

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Properties

Reference material is provided below for the Connect, Configure, Destination, and Advanced Settings properties.
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Name
string
required
A human-readable name for the component.

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Connect

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Authentication
drop-down
required
Choose your OAuth connection from the drop-down menu.Click Manage to navigate to the OAuth connections list to review OAuth connections and to add new connections. Read OAuth to learn how to create an OAuth connection.Read Marketo authentication guide, which includes specific steps for acquiring Marketo credentials.
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Rest Endpoint
string
required
The REST endpoint for your Marketo instance.
This endpoint is provided by Marketo on the Admin page of the Marketo website.
Connection Options = column editor
  • Parameter: A JDBC parameter supported by the database driver. The available parameters are explained in data model. Manual setup is not usually required, since sensible defaults are assumed.
  • Value: A value for the given parameter.
Click the Text Mode toggle at the bottom of the Connection Options dialog to open a multi-line editor that lets you add items in a single block. For more information, read Text mode. To use grid variables, select the Use Grid Variable checkbox at the bottom of the Connection Options dialog.

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Configure

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Basic/Advanced Mode
drop-down
required
  • Basic: This mode will build a query for you using settings from the Data Source, Data Selection, Data Source Filter, Combine Filters, and Limit parameters. In most cases, this mode will be sufficient.
  • Advanced: This mode will require you to write an SQL-like query to call data from the service you’re connecting to. The available fields and their descriptions are documented in the data model.
There are some special pseudo columns that can form part of a query filter, but are not returned as data. This is fully described in the data model.
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SQL Query
code editor
required
This is an SQL-like SELECT query, written in the SQL accepted by your cloud data warehouse. Treat collections as table names, and fields as columns. Only available in Advanced mode.
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Data Source
drop-down
required
Select a single data source to be extracted from the source system and loaded into a table in the destination. The source system defines the data sources available. Use multiple components to load multiple data sources.
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Data Selection
dual listbox
required
Choose one or more columns to return from the query. The columns available are dependent upon the data source selected. Move columns left-to-right to include in the query.To use grid variables, select the Use Grid Variable checkbox at the bottom of the Data Selection dialog.
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Data Source Filter
column editor
Define one or more filter conditions that each row of data must meet to be included in the load.
  • Input Column: Select an input column. The available input columns vary depending upon the data source.
  • Qualifier:
    • Is: Compares the column to the value using the comparator.
    • Not: Reverses the effect of the comparison, so “Equals” becomes “Not equals”, “Less than” becomes “Greater than or equal to”, etc.
  • Comparator: Choose a method of comparing the column to the value. Possible comparators include: “Equal to”, “Greater than”, “Less than”, “Greater than or equal to”, “Less than or equal to”, “Like”, “Null”. Not all data sources support all comparators.
  • Value: The value to be compared.
Click the Text Mode toggle at the bottom of the dialog to open a multi-line editor. For more information, read Text mode.
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Combine Filters
drop-down
required
The data source filters you have defined can be combined using either And or Or logic. If And, then all filter conditions must be satisfied to load the data row. If Or, then only a single filter condition must be satisfied. The default is And.If you have only one filter, or no filters, this parameter is essentially ignored.
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Row Limit
integer
Set a numeric value to limit the number of rows that are loaded. The default is an empty field, which will load all rows.

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Destination

Select your cloud data warehouse.
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Type
drop-down
required
  • Standard: The data will be staged in your storage location before being loaded into a table. This is the only setting currently available.
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Warehouse
drop-down
required
The Snowflake warehouse used to run the queries. The special value [Environment Default] uses the warehouse defined in the environment. Read Overview of Warehouses to learn more.
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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.
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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.
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Target Table
string
required
The name of the table to be created in your Snowflake database. This table will be recreated and will drop any existing table of the same name.You can use a Table Input component in a transformation pipeline to access and transform this data after it has been loaded.
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Primary Keys
dual listbox
Select one or more columns to be designated as the table’s primary key.
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Stage
drop-down
required
Select a managed stage. The special value, [Custom], will create a stage “on the fly” for use solely within this component.
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Stage Platform
drop-down
required
Choose where the data is staged before being loaded into your Snowflake table using the drop-down menu.
  • Existing Amazon S3 Location: Activates the S3 Staging Area property, allowing users to specify a custom staging area on Amazon S3. The Stage Authentication property is also activated, letting users select a method of authenticating the data staging.
  • Existing Azure Blob Storage Location: Activates the Storage Account and Blob Container properties, allowing users to specify a custom staging location on Azure. The Stage Authentication property is also activated, letting users select a method of authenticating the data staging.
  • Existing Google Cloud Storage Location: Activates the Storage Integration and GCS Staging Area properties, allowing users to specify a custom staging area within Google Cloud Storage.
  • Snowflake Managed: Create and use a temporary internal stage on Snowflake for staging the data. This stage, along with the staged data, will cease to exist after loading is complete. This is the default setting.
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Stage Authentication
drop-down
required
Select an authentication method for data staging. Only available when Stage Platform is set to either Existing Amazon S3 Location or Existing Azure Blob Storage Location.
  • Credentials: Uses the credentials configured in the environment. If no credentials have been configured, an error will occur.
  • Storage Integration: Use a Snowflake storage integration to authentication data staging.
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Storage Integration
drop-down
required
Select a Snowflake storage integration from the drop-down list. Storage integrations are required to permit Snowflake to read data from and write to your cloud storage location and must be set up in advance of selection.
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S3 Staging Area
drop-down
required
Select an S3 bucket for temporary storage. Ensure your access credentials have S3 access and permission to write to the bucket. The temporary objects created in this bucket will be removed again after the load completes.
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Storage Account
drop-down
required
Select a storage account with your desired blob container to be used for staging the data. For more information, read Storage account overview.
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Blob Container
drop-down
required
Select a Blob container to be used for staging the data. For more information, read Introduction to Azure Blob storage.
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GCS Staging Area
drop-down
required
The URL and path of the target Google Cloud Storage bucket to be used for staging the queried data.

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Advanced Settings

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Load Options
multiple drop-downs
required
  • Clean Staged Files: Destroy staged files after loading data. Default is On.
  • String Null is Null: Converts any strings equal to null into a null value. This is case-sensitive and only works with entirely lower-case strings. Default is Off.
  • Recreate Target Table: Choose whether the component recreates its target table before the data load. If Off, the existing table will be used. Default is On.
  • File Prefix: Give staged file names a prefix of your choice. Default is empty (no prefix).
  • Trim String Columns: Remove leading and trailing characters from a string column. Default is On.
  • Compression Type: Set the compression type to either gzip (default) or None.
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Encryption
drop-down
required
Decide how the files are encrypted inside the S3 bucket. This property is available when using an existing Amazon S3 location for staging.
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KMS Key ID
drop-down
required
The ID of the KMS encryption key you have chosen to use in the Encryption property.
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Auto Debug
drop-down
required
Choose whether to automatically log debug information about your load. These logs can be found in the task history and should be included in support requests concerning the component. Turning this on will override any debugging connection options.
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Debug Level
drop-down
required
The level of verbosity with which your debug information is logged. Levels above 1 can log huge amounts of data and result in slower execution. These logs can be found in the Message field of the task details after pipeline execution and should be included in support requests concerning the component.
  1. Will log the query, the number of rows returned by it, the start of execution and the time taken, and any errors.
  2. Will log everything included in Level 1, plus cache queries and additional information about the request, if applicable.
  3. Will additionally log the body of the request and the response.
  4. Will additionally log transport-level communication with the data source. This includes SSL negotiation.
  5. Will additionally log communication with the data source, as well as additional details that may be helpful in troubleshooting problems. This includes interface commands.

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Data model

The JDBC driver for this component models Marketo APIs as relational tables, views, and stored procedures, which are documented in the data model. You’ll also find API limitations and requirements. This connector also allows you to query system tables in Advanced mode. To see the available system tables in the data model, read the System Tables section of the data model. For more information about using system tables, read our System tables guide.