Mailchimp
The Mailchimp component uses the Mailchimp API to retrieve data and load it into a table—this stages the data, so the table is reloaded each time. Users can then transform their data with transformation components.
If the component requires access to a cloud provider, it will use the cloud credentials associated with your environment to access resources.
Properties
Reference material is provided below for the Connect, Configure, Destination, and Advanced Settings properties.
Connect
Authentication Type = drop-down
Currently only supports API key.
API Key = drop-down
Drop-down list of secret definitions. Create a secret definition to reference your corresponding Mailchimp API key.
To create a secret definition:
- Click Manage to open the Secret definitions tab in a new browser tab.
- Click Add secret definition.
- Name your secret definition. Names must be unique.
- Add a description if you wish. This is optional.
- Toggle Yes if using a multi-line input password. Otherwise, keep the toggle set to No.
- Add your Mailchimp API key to the Secret value field.
- Click Create secret. You will return to the Secret definitions tab.
- Close the browser tab to return to Designer.
- Select your new secret definition from the drop-down menu. If the secret has not propagated, close the dialog and try again.
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.
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.
Configure
Mode = drop-down
- Basic: This mode will build a query for you using settings from the Schema, 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.
Note
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.
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.
Data Source = drop-down
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.
Data Selection = dual listbox
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, tick the Use Grid Variable checkbox at the bottom of the Data Selection dialog.
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". "Equal to" can match exact strings and numeric values, while other comparators, such as "Greater than" and "Less than", will work only with numerics. The "Like" operator allows the wildcard character
%to be used at the start and end of a string value to match a column. The Null operator matches only null values, ignoring whatever the value is set to. Not all data sources support all comparators, meaning that it is likely that only a subset of the above comparators will be available to choose from. - Value: The value to be compared.
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.
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.
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.
Destination
Select your cloud data warehouse.
Destination = drop-down
- Snowflake: Load your data into Snowflake. You'll need to set a cloud storage location for temporary staging of the data.
- Cloud Storage: Load your data directly into your preferred cloud storage location.
Click either the Snowflake or Cloud Storage tab on this page for documentation applicable to that destination type.
Warehouse = drop-down
The Snowflake warehouse used to run the queries. The special value, [Environment Default], will use the warehouse defined in the environment. Read Overview of Warehouses to learn more.
Database = drop-down
The Snowflake database. The special value, [Environment Default], will use the database defined in the environment. Read Databases, Tables and Views - Overview to learn more.
Schema = drop-down
The Snowflake schema. The special value, [Environment Default], will use the schema defined in the environment. Read Database, Schema, and Share DDL to learn more.
Table Name = string
The name of the table to be created.
Load Strategy = drop-down
- Replace: If the specified table name already exists, that table will be destroyed and replaced by the table created during this pipeline run.
- Truncate and Insert: If the specified table name already exists, all rows within the table will be removed and new rows will be inserted per the next run of this pipeline.
- Fail if Exists: If the specified table name already exists, this pipeline will fail to run.
- Append: If the specified table name already exists, then the data is inserted without altering or deleting the existing data in the table. It's appended onto the end of the existing data in the table. If the specified table name doesn't exist, then the table will be created, and your data will be inserted into the table. For example, if you have a source holding 100 records, then on the first pipeline run, your target table will be created and 100 rows will be inserted. On the second pipeline run, those same 100 records will be appended to your existing target table, so now it holds 200 records. Third pipeline run will be 300 records in your table, and so on.
Clean Staged files = boolean
- 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.
Stage Platform = drop-down
Choose a data staging platform using the drop-down menu.
- 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.
Click one of the tabs below for documentation applicable to that staging platform.
Amazon S3 Bucket = drop-down
An AWS S3 bucket to stage data into. The drop-down menu will include buckets tied to the cloud provider credentials that you have associated with your environment.
Internal Stage Type = drop-down
A Snowflake internal stage type. Currently, only type User is supported.
Read Choosing an Internal Stage for Local Files to learn more about internal stage types and the usage of each.
Storage Account = drop-down
Select a storage account linked to your desired blob container to be used for staging the data. For more information, read Storage account overview.
Container = drop-down
Select a Blob container to be used for staging the data. For more information, read Introduction to Azure Blob storage.
Storage Integration= drop-down
Select the storage integration. Storage integrations are required to permit Snowflake to read data from and write to a cloud storage location. Integrations must be set up in advance of selecting them. Storage integrations can be configured to support Amazon S3, Google Cloud Storage, or Microsoft Azure Blob Storage, regardless of the cloud provider that hosts your Snowflake account.
GCS Bucket= drop-down
The drop-down menu will include Google Cloud Storage (GCS) buckets tied to the cloud provider credentials that you have associated with your environment.
Overwrite = boolean
Select whether to overwrite files of the same name when this pipeline runs. Default is Yes.
Load Strategy = drop-down (optional)
- Append Files in Folder: Appends files to storage folder. This is the default setting.
- Overwrite Files in Folder: Overwrite existing files with matching structure.
See the configuration table for how this parameter works with the Folder Path and File Prefix parameters:
| Configuration | Description |
|---|---|
| Append files in folder with defined folder path and file prefix. | Files will be stored under the structure uniqueID/timestamp-partX where X is the part number, starting from 1. For example, 1da27ea6-f0fa-4d15-abdb-d4e990681839/20240229100736969-part1. |
| Append files in folder without defined folder path and file prefix. | Files will be stored under the structure folder/prefix-timestamp-partX where X is the part number, starting from 1. For example, folder/prefix-20240229100736969-part1. |
| Overwrite files in folder with defined folder path and file prefix. | Files will be stored under the structure folder/prefix-partX where X is the part number, starting from 1. All files with matching structures will be overwritten. |
| Overwrite files in folder without defined folder path and file prefix. | Validation will fail. Folder path and file prefix must be supplied for this load strategy. |
Folder Path = string (optional)
The folder path of the written files.
File Prefix = string (optional)
A string of characters to include at the beginning of the written files. Often used for organizing database objects.
Storage = drop-down
A cloud storage location to load your data into for storage. Choose either Amazon S3, Azure Storage, or Google Cloud Storage.
Click the tab that corresponds to your chosen cloud storage service.
Amazon S3 Bucket = drop-down
An AWS S3 bucket to load data into. The drop-down menu will include buckets tied to the cloud provider credentials that you have associated with your environment.
Storage Account = drop-down
Select a storage account linked to your desired blob container to be used for staging the data. For more information, read Storage account overview.
Container = drop-down
Select a Blob container to be used for staging the data. For more information, read Introduction to Azure Blob storage.
GCS Bucket= drop-down
The drop-down menu will include Google Cloud Storage (GCS) buckets tied to the cloud provider credentials that you have associated with your environment.
Overwrite = boolean
Select whether to overwrite files of the same name when this pipeline runs. Default is Yes.
Destination = drop-down
- Databricks: Load your data into Databricks. You'll need to set a cloud storage location for temporary staging of the data.
- Cloud Storage: Load your data directly into your preferred cloud storage location.
Click either the Databricks or Cloud Storage tab on this page for documentation applicable to that destination type.
Catalog = drop-down
Select a Databricks Unity Catalog. The special value, [Environment Default], will use the catalog specified in the Data Productivity Cloud environment setup. Selecting a catalog will determine which schema are available in the next parameter.
Schema = drop-down
Select the Databricks schema. The special value, [Environment Default], will use the schema specified in the Data Productivity Cloud environment setup.
Table Name = string
The name of the table to be created.
Load Strategy = drop-down
- Fail if Exists: If the specified table name already exists, this pipeline will fail to run.
- Replace: If the specified table name already exists, that table will be destroyed and replaced by the table created during this pipeline run.
- Truncate and Insert: If the specified table name already exists, all rows within the table will be removed and new rows will be inserted per the next run of this pipeline.
- Append: If the specified table name already exists, then the data is inserted without altering or deleting the existing data in the table. It's appended onto the end of the existing data in the table. If the specified table name doesn't exist, then the table will be created, and your data will be inserted into the table. For example, if you have a source holding 100 records, then on the first pipeline run, your target table will be created and 100 rows will be inserted. On the second pipeline run, those same 100 records will be appended to your existing target table, so now it holds 200 records. Third pipeline run will be 300 records in your table, and so on.
Clean Staged Files = boolean
- 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.
Stage Platform = drop-down
Choose a data staging platform using the drop-down menu.
- Amazon S3: Stage your data on an AWS S3 bucket.
- Azure Storage: Stage your data in an Azure Blob Storage container.
Click one of the tabs below for documentation applicable to that staging platform.
Amazon S3 Bucket = drop-down
An AWS S3 bucket to stage data into. The drop-down menu will include buckets tied to the cloud provider credentials that you have associated with your environment.
Storage Account = drop-down
Select a storage account linked to your desired blob container to be used for staging the data. For more information, read Storage account overview.
Container = drop-down
Select a Blob container to be used for staging the data. For more information, read Introduction to Azure Blob storage.
Storage Integration= drop-down
Select the storage integration. Storage integrations are required to permit Snowflake to read data from and write to a cloud storage location. Integrations must be set up in advance of selecting them. Storage integrations can be configured to support Amazon S3, Google Cloud Storage, or Microsoft Azure Blob Storage, regardless of the cloud provider that hosts your Snowflake account.
GCS Bucket= drop-down
The drop-down menu will include Google Cloud Storage (GCS) buckets tied to the cloud provider credentials that you have associated with your environment.
Overwrite = boolean
Select whether to overwrite files of the same name when this pipeline runs. Default is Yes.
Load Strategy = drop-down (optional)
- Append Files in Folder: Appends files to storage folder. This is the default setting.
- Overwrite Files in Folder: Overwrite existing files with matching structure.
See the configuration table for how this parameter works with the Folder Path and File Prefix parameters:
| Configuration | Description |
|---|---|
| Append files in folder with defined folder path and file prefix. | Files will be stored under the structure uniqueID/timestamp-partX where X is the part number, starting from 1. For example, 1da27ea6-f0fa-4d15-abdb-d4e990681839/20240229100736969-part1. |
| Append files in folder without defined folder path and file prefix. | Files will be stored under the structure folder/prefix-timestamp-partX where X is the part number, starting from 1. For example, folder/prefix-20240229100736969-part1. |
| Overwrite files in folder with defined folder path and file prefix. | Files will be stored under the structure folder/prefix-partX where X is the part number, starting from 1. All files with matching structures will be overwritten. |
| Overwrite files in folder without defined folder path and file prefix. | Validation will fail. Folder path and file prefix must be supplied for this load strategy. |
Folder Path = string (optional)
The folder path of the written files.
File Prefix = string (optional)
A string of characters to include at the beginning of the written files. Often used for organizing database objects.
Storage = drop-down
A cloud storage location to load your data into for storage. Choose either Amazon S3, Azure Storage, or Google Cloud Storage.
Click the tab that corresponds to your chosen cloud storage service.
Amazon S3 Bucket = drop-down
An AWS S3 bucket to load data into. The drop-down menu will include buckets tied to the cloud provider credentials that you have associated with your environment.
Storage Account = drop-down
Select a storage account linked to your desired blob container to be used for staging the data. For more information, read Storage account overview.
Container = drop-down
Select a Blob container to be used for staging the data. For more information, read Introduction to Azure Blob storage.
GCS Bucket= drop-down
The drop-down menu will include Google Cloud Storage (GCS) buckets tied to the cloud provider credentials that you have associated with your environment.
Overwrite = boolean
Select whether to overwrite files of the same name when this pipeline runs. Default is Yes.
Destination = drop-down
- Redshift: Load your data into Amazon Redshift. You'll need to set a cloud storage location for temporary staging of the data.
- Cloud Storage: Load your data directly into your preferred cloud storage location.
Click either the Amazon Redshift or Cloud Storage tab on this page for documentation applicable to that destination type.
Schema = drop-down
Select the Redshift schema. The special value, [Environment Default], will use the schema defined in the environment. For information about using multiple schemas, read Schemas.
Table Name = string
The name of the table to be created.
Load Strategy = drop-down
- Replace: If the specified table name already exists, that table will be destroyed and replaced by the table created during this pipeline run.
- Fail if Exists: If the specified table name already exists, this pipeline will fail to run.
- Truncate and Insert: If the specified table name already exists, all rows within the table will be removed and new rows will be inserted per the next run of this pipeline.
- Append: If the specified table name already exists, then the data is inserted without altering or deleting the existing data in the table. It's appended onto the end of the existing data in the table. If the specified table name doesn't exist, then the table will be created, and your data will be inserted into the table. For example, if you have a source holding 100 records, then on the first pipeline run, your target table will be created and 100 rows will be inserted. On the second pipeline run, those same 100 records will be appended to your existing target table, so now it holds 200 records. Third pipeline run will be 300 records in your table, and so on.
Clean Staged Files = boolean
- 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.
Amazon S3 Bucket = drop-down
An AWS S3 bucket to stage data into. The drop-down menu will include buckets tied to the cloud provider credentials that you have associated with your environment.
Load Strategy = drop-down (optional)
- Append Files in Folder: Appends files to storage folder. This is the default setting.
- Overwrite Files in Folder: Overwrite existing files with matching structure.
See the configuration table for how this parameter works with the Folder Path and File Prefix parameters:
| Configuration | Description |
|---|---|
| Append files in folder with defined folder path and file prefix. | Files will be stored under the structure uniqueID/timestamp-partX where X is the part number, starting from 1. For example, 1da27ea6-f0fa-4d15-abdb-d4e990681839/20240229100736969-part1. |
| Append files in folder without defined folder path and file prefix. | Files will be stored under the structure folder/prefix-timestamp-partX where X is the part number, starting from 1. For example, folder/prefix-20240229100736969-part1. |
| Overwrite files in folder with defined folder path and file prefix. | Files will be stored under the structure folder/prefix-partX where X is the part number, starting from 1. All files with matching structures will be overwritten. |
| Overwrite files in folder without defined folder path and file prefix. | Validation will fail. Folder path and file prefix must be supplied for this load strategy. |
Folder Path = string (optional)
The folder path of the written files.
File Prefix = string (optional)
A string of characters to include at the beginning of the written files. Often used for organizing database objects.
Storage = drop-down
A cloud storage location to load your data into for storage. Choose either Amazon S3, Azure Storage, or Google Cloud Storage.
Click the tab that corresponds to your chosen cloud storage service.
Amazon S3 Bucket = drop-down
An AWS S3 bucket to load data into. The drop-down menu will include buckets tied to the cloud provider credentials that you have associated with your environment.
Storage Account = drop-down
Select a storage account linked to your desired blob container to be used for staging the data. For more information, read Storage account overview.
Container = drop-down
Select a Blob container to be used for staging the data. For more information, read Introduction to Azure Blob storage.
GCS Bucket= drop-down
The drop-down menu will include Google Cloud Storage (GCS) buckets tied to the cloud provider credentials that you have associated with your environment.
Overwrite = boolean
Select whether to overwrite files of the same name when this pipeline runs. Default is Yes.
Advanced Settings
Auto Debug = boolean
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.
Debug Level = drop-down
The level of detail you want to include in your debug logs. Select a level between 1 and 4:
- Will log the query, the number of rows returned by it, the start of execution, the time taken, and any errors.
- Will log everything included in Level 1, plus cache queries and additional information about the request, if applicable.
- 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.
- 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.
Parse 'Null' & Empty Strings as NULL = yes/no
Converts common strings that represent null into a null value. This is case-sensitive and works with the following strings: "", "NULL", "NUL", "Null", "null". Default is Yes.
Note
Currently, this property is only applicable when using Snowflake as your destination.
Data model
The JDBC driver for this component models Mailchimp APIs as relational tables, views, and stored procedures, which are documented in the data model. You'll also find API limitations and requirements. The connection option SupportEnhancedSQL is set to true by default and typically circumvents most API limitations.
View the Mailchimp data model to learn more.
Deactivate soft delete for Azure blobs (Databricks)
If you intend to set your destination as Databricks and your stage platform as Azure Storage, you must turn off the "Enable soft delete for blobs" setting in your Azure account for your pipeline to run successfully. To do this:
- Log in to the Azure portal.
- In the top-left, click ☰ → Storage Accounts.
- Select the intended storage account.
- In the menu, under Data management, click Data protection.
- Untick Enable soft delete for blobs. For more information, read Soft delete for blobs.
| Snowflake | Databricks | Amazon Redshift |
|---|---|---|
| ✅ | ✅ | ✅ |