Mailchimp
The Mailchimp Query 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.
Properties
Name = string
A human-readable name for the component.
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.
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 Mailchimp. 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. Treat collections as table names, and fields as columns. Only available in Advanced mode.
Data Source = drop-down
Select a data source.
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 check box at the bottom of the Data Selection dialog.
Data Source Filter = column editor
- 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
Select whether to use the defined filters in combination with one another according to either And or Or.
Limit = integer
Set a numeric value to limit the number of rows that are loaded.
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.
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.
- 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.
Load Strategy = drop-down
- 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
The folder path of the written files.
File Prefix = string
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.
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 (Database) = drop-down
The Databricks schema. The special value, [Environment Default], will use the schema defined in the environment. Read Create and manage schemas 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.
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.
- 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.
Load Strategy = drop-down
- 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
The folder path of the written files.
File Prefix = string
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.
Destination = drop-down
- Amazon 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.
- 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.
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
- 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
The folder path of the written files.
File Prefix = string
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.
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.
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 (preview) | Amazon Redshift |
|---|---|---|
| ✅ | ✅ | ✅ |