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For Snowflake projects, the Facebook Load connector supersedes the Facebook Query connector, which is no longer available for new pipelines. Existing pipelines that use the Facebook Query connector will continue to work as expected.Databricks and Amazon Redshift projects should continue to use the Facebook Query connector.
The Facebook Load orchestration component uses the Connect and Configure parameters to create a table of Facebook data, which is then stored in your preferred storage location—this can be your connected cloud data warehouse (Snowflake or Google BigQuery) or a cloud storage location (Amazon S3, Azure Blob Storage, or Google Cloud Storage). Executing this component performs one of the following actions in your specified destination: creates a new table or replaces an existing table.
Meta has rebranded its developer resources and API documentation. While the component is titled Facebook Ads Load in , it is part of the Meta suite of connectors.
The Meta API (formerly Facebook) is rate limited and may cause issues when querying large amounts of data. Consider using a Python Script or Bash Pushdown component to sleep your pipeline between data loads to remedy this.
If the component requires access to a cloud provider (AWS, Azure, or Google Cloud), it will use the cloud credentials associated with your environment to access resources. To stage data to Azure Blob Storage, the Azure credentials associated with your environment must be assigned the Storage Blob Data Contributor role. For more information, read User assigned with the Storage Blob Data Contributor role.
The Facebook Marketing API v17.0 expired on May 14, 2024. The API version is set to v19.0 by default. You can use the Connection Options property to manually set the API version to a different, current version—for example, v18.0.To do this:
  1. Open the Connection Options property.
  2. Click +.
  3. Set Parameter to “Other”.
  4. Set Value to, for example, “MarketingAPIVersion=18.0” or “GraphAPIVersion=18.0”.
Read Available Marketing API Versions for more information.
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 Type
drop-down
required
Currently supports OAuth 2.0 Authorization Code.
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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.OAuth tokens for Facebook will eventually expire and require remaking. It is outside of our control to remedy this. You can, however, extend and refresh existing tokens.
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Connection options
column editor
  • Parameter: A JDBC parameter supported by the database driver. The available parameters are explained in the data model. Manual setup is not usually required, since sensible defaults are assumed.
  • Value: A value for the given parameter.
Toggle Text mode on at the bottom of the dialog to open a multi-line editor that lets you add items in a single block. For more information, read Text mode.

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Configure

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Load Type
drop-down
required
  • Full Load: Select this option to load your entire dataset.
  • Incremental Load: Select this option to only load new and updated records from your dataset.
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Mode
drop-down
required
  • Basic: This mode will build a query for you using settings from the Schema, Data Source, Data Selection, Data Source Filter, Combine Filters, and Row 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.
While the query is exposed in an SQL-like language, the exact semantics can be surprising, for example, filtering on a column can return more data than not filtering on it. This is an impossible scenario with regular SQL.
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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, toggle Use Grid Variable on. For more information, read Grid variables.
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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.
Toggle Text mode on at the bottom of the 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, toggle Use Grid Variable on. For more information, read Grid variables.
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SQL Query
code editor
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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Combine Filters
drop-down
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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High-Water Mark Selection
drop-down
required
When Incremental Load is selected, select a datetime field from your dataset that is always updated when your data changes, such as dateModified. The connector will record the maximum value of this field each time you run this pipeline. On subsequent runs when Incremental Load is selected, only data with a higher value in this field will be loaded.
If no rows in the source have a higher value than the stored high-water mark, the task will complete with a “task is skipped” message. This is expected behavior, indicating that no new or updated data was found since the last successful run.

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Destination

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Destination
drop-down
required
Select the destination for your data. This is either in Snowflake as a table or as files in cloud storage.
  • Snowflake: Load your data into a table in Snowflake. The data must first be staged via Snowflake or a cloud storage solution.
  • Cloud Storage: Load your data directly into files in your preferred cloud storage location. The format of these files can differ between source systems and will not have a file extension so we suggest inspecting the output to determine the format of the data.
When Incremental Load is selected, Cloud Storage is not supported as a destination. Only warehouse destinations (Snowflake or Google BigQuery) are available for incremental loads.
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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 to access. 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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Table Name
string
required
The name of the table to be created in your Snowflake database. 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
Choose one or more columns to be designated as the table’s primary key.When using Incremental Load, if you select a primary key, the loaded data will be merged with your existing data. If you don’t select a primary key, the loaded data will be appended to your existing data.
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Clean Staged files
boolean
required
  • Yes: Staged files will be destroyed after data is loaded. This is the default setting.
  • No: Staged files are retained in the staging area after data is loaded.
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Stage Platform
drop-down
required
Use the drop-down menu to choose where the data is staged before being loaded into your Snowflake table.
  • Amazon S3: Stage your data on an AWS S3 bucket.
  • Snowflake: Stage your data on a Snowflake internal stage.
  • Azure Storage: Stage your data in an Azure Blob Storage container.
  • Google Cloud Storage: Stage your data in a Google Cloud Storage bucket.
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Internal Stage Type
drop-down
required
Select the Snowflake internal stage type. Use the Snowflake links provided to learn more about each type of stage.
  • User: Each Snowflake user has a user stage allocated to them by default for file storage. You may find the user stage convenient if your files will only be accessed by a single user, but need to be copied into multiple tables.
  • Named: A named stage provides high flexibility for data loading. Users with the appropriate privileges on the stage can load data into any table. Furthermore, because the stage is a database object, any security or access rules that apply to all objects will apply to the named stage.
Named stages can be altered and dropped. User stages cannot.
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Named Stage
drop-down
required
Select your named stage. Read Creating a named stage to learn how to create a new named stage.
There is a known issue where named stages that include special characters or spaces are not supported.

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Advanced Settings

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Output Encoding
drop-down
Set the file format used to stage extracted records from this component before loading. The default is CSV. CSV is compact and widely compatible, while JSON Lines preserves the difference between NULL and empty-string values.
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Key Sanitization Strategy
drop-down
Choose how to change the column names in your data so that they conform to the destination warehouse’s identifier rules:
  • None: (Default) Do not change the source column names.
  • Alphanumeric (lower case): Replace non-alphanumeric characters with underscores, change the name to lowercase, and add an underscore at the start of the name if it begins with a digit.
  • Alphanumeric (upper case): Replace non-alphanumeric characters with underscores, change the name to uppercase, and add an underscore at the start of the name if it begins with a digit.
If two source column names sanitize to the same result, the load fails with an error rather than dropping data.
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Auto Debug
boolean
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. This property is set to No by default. Turning this on will override any debugging Connection Options you may have set.
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Debug Level
drop-down
required
The level of detail you want to include in your debug logs. Select a level between 1 and 4:
  1. Will log the query, the number of rows returned by it, the start of execution, 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 log everything included in Levels 1 and 2, and additionally log the body of the request and the response. This is the default logging level when debug logging is activated.
  4. Will log everything included in Levels 1, 2, and 3, and additionally log transport-level communication with the data source. This includes SSL negotiation.
Levels above 1 can log huge amounts of data and result in slower query execution.
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Parse 'Null' & Empty Strings as NULL
boolean
required
Converts common strings that represent null into a null value. This is case-sensitive and works with the following strings: "", “NULL”, “NUL”, “Null”, “null”. The default is No.
This parameter is only applicable when using Snowflake as your destination.
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Trim String Columns
boolean
required
When Yes, remove leading and trailing characters from a string column. The default is No.
This parameter is only applicable when using Snowflake as your destination.

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Data model

The JDBC driver for this component models Meta (formerly Facebook) APIs as relational tables, views, and stored procedures, which are documented in the data model. You’ll also find API limitations and requirements.