> ## Documentation Index > Fetch the complete documentation index at: https://docs.maia.ai/llms.txt > Use this file to discover all available pages before exploring further. # Open Exchange Rates export const maia = "Maia"; export const designer = "Designer"; export const ComponentMetadata = ({warehouses, unsupportedWarehouses = [], componentType, connectionInputs, connectionOutputs}) => { const allWarehouses = [...warehouses.map(w => ({ name: w, supported: true })), ...unsupportedWarehouses.map(w => ({ name: w, supported: false }))]; return

Project Availability	{allWarehouses.map((w, i) => {w.name} {w.supported ? '✅' : '❌'} )}
Component Type	{componentType}
Connection Inputs	{connectionInputs}
Connection Outputs	{connectionOutputs}

; }; This page describes how to configure the Open Exchange Rates connector component as part of a data pipeline within {designer}. The Open Exchange Rates component uses the Connect and Configure parameters to create a table of Open Exchange Rates data, which is then stored in your preferred storage location (Snowflake, Databricks, Amazon Redshift, or cloud storage). You *do not* need to use the Create Table component when using this connector, as the Open Exchange Rates component will create a new table or replace an existing table for you using the Destination parameters you define. The Open Exchange Rates connector is a Flex connector. In {maia}, Flex connectors let you connect to a curated set of endpoints to load data. You can use the Open Exchange Rates connector in its preconfigured state, *or* you can edit the connector by adding or amending available Open Exchange Rates endpoints as per your use case. You can edit Flex connectors in the [Custom Connector](/docs/guides/custom-connector-setup) user interface. For detailed information about authentication, specific endpoint parameters, pagination, and more aspects of the Open Exchange Rates API, read the [Open Exchange Rates API documentation](https://docs.openexchangerates.org/reference/api-introduction). *** ## Properties Reference material is provided below for the Connect, Configure, Destination, and Advanced Settings properties. A human-readable name for the component. ### Connect The data source to load data from in this pipeline. The drop-down menu lists the Open Exchange Rates API endpoints available in the connector. For detailed information about specific endpoints, read the [Open Exchange Rates API documentation](https://docs.openexchangerates.org/reference/api-introduction). | Endpoint | Method | Reference | | ------------------------- | ------ | ------------------------------------------------------------------------------------------------------------------------- | | Latest Exchange Rates | GET | [Retrieve the latest exchange rates](https://docs.openexchangerates.org/reference/latest-json) | | Historical Exchange Rates | GET | [Retrieve historical exchange rates for any date available](https://docs.openexchangerates.org/reference/historical-json) | | Currencies | GET | [Retrieve a JSON list of all currency symbols available](https://docs.openexchangerates.org/reference/currencies-json) | | App Usage | GET | [Retrieve basic plan information and usage statistics](https://docs.openexchangerates.org/reference/usage-json) | {/* */} The authentication method to authorize access to your Open Exchange Rates data. Currently supports API key. {/* */} The key of a working API key:value pair. All endpoints require an API key to authenticate with Open Exchange Rates. Use the **app\_id** query parameter, and enter `app_id` in the **Key** field. {/* */} Use the drop-down menu to select the corresponding secret definition that denotes the value of a working API key:value pair. Read [Secrets and secret definitions](/docs/guides/secrets-and-secret-definitions) to learn how to create a new secret definition. Read the [Open Exchange Rates API documentation](https://docs.openexchangerates.org/reference/authentication) to learn how to acquire an API key. ### Configure * **Parameter Name:** The name of a URI parameter. * **Parameter Value:** The value of the corresponding parameter. {/* */} * **Parameter Name:** The name of a query parameter. * **Parameter Value:** The value of the corresponding parameter. | Required parameter | Endpoints | Description | | ------------------ | ------------------------- | ------------------------------------------ | | date | Historical Exchange Rates | The requested date in `YYYY-MM-DD` format. | {/* */} * **Parameter Name:** The name of a header parameter. * **Parameter Value:** The value of the corresponding parameter. {/* */} A JSON body to include as part of a POST request. Use [Custom Connector](/docs/guides/custom-connector-setup) to test your endpoints work as expected before moving to Designer pipelines. You should also consult the developer documentation for the API you're connecting to—as the developer portal may provide additional information about endpoints and requests. {/* */} A numeric value to limit the maximum number of records per page. ### Destination Select your cloud data warehouse. * **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. {/* */} The Snowflake warehouse used to run the queries. The special value `[Environment Default]` uses the warehouse defined in the environment. Read [Overview of Warehouses](https://docs.snowflake.com/en/user-guide/warehouses-overview.html) to learn more. {/* */} {/* */} The Snowflake database. The special value `[Environment Default]` uses the database defined in the environment. Read [Databases, Tables and Views - Overview](https://docs.snowflake.com/en/guides-overview-db) to learn more. {/* */} {/* */} The Snowflake schema. The special value `[Environment Default]` uses the schema defined in the environment. Read [Database, Schema, and Share DDL](https://docs.snowflake.com/en/sql-reference/ddl-database.html) to learn more. {/* */} {/* */} The name of the table to be created. {/* */} {/* */} * **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:** Each time the pipeline runs, two operations are performed: first, the table is truncated, meaning all existing rows are deleted. Then, your new rows are inserted. The table itself is never destroyed and recreated. * **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. {/* */} {/* */} * **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. {/* */} {/* */} Select the stage access strategy. The strategies available depend on the cloud platform you select in **Stage Platform**. * **Credentials:** Connects to the external stage (AWS, Azure) using your configured [cloud provider credentials](/docs/guides/cloud-credentials). Not available for Google Cloud Storage. * **Storage Integration:** Use a Snowflake storage integration to grant access to Snowflake to read data from and write to a cloud storage location. This will reveal the **Storage Integration** property, through which you can select any of your existing Snowflake storage integrations. {/* */} {/* */} 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. {/* */} {/* */} 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. {/* */} {/* */} An AWS S3 bucket to stage data into. The drop-down menu will include buckets tied to the [cloud provider credentials](/docs/guides/cloud-credentials) that you have associated with your [environment](/docs/guides/environments). {/* */} {/* */} 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](https://docs.snowflake.com/en/user-guide/data-load-local-file-system-create-stage#user-stages) 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](https://docs.snowflake.com/en/user-guide/data-load-local-file-system-create-stage#named-stages) 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. {/* */} {/* */} Select your named stage. Read [Creating a named stage](https://docs.snowflake.com/en/user-guide/data-load-local-file-system-create-stage#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. {/* */} {/* */} 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. {/* */} {/* */} Select a storage account linked to your desired blob container to be used for staging the data. For more information, read [Storage account overview](https://learn.microsoft.com/en-us/azure/storage/common/storage-account-overview). {/* */} {/* */} Select a Blob container to be used for staging the data. For more information, read [Introduction to Azure Blob storage](https://learn.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction). {/* */} {/* */} Select the stage access strategy. The strategies available depend on the cloud platform you select in **Stage Platform**. * **Storage Integration:** Use a Snowflake storage integration to grant access to Snowflake to read data from and write to a cloud storage location. This will reveal the **Storage Integration** property, through which you can select any of your existing Snowflake storage integrations. {/* */} {/* */} 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. {/* */} {/* */} The drop-down menu will include Google Cloud Storage (GCS) buckets tied to the [cloud provider credentials](/docs/guides/cloud-credentials) that you have associated with your [environment](/docs/guides/environments). {/* */} {/* */} Select whether to overwrite files of the same name when this pipeline runs. Default is Yes. {/* */} {/* */} * **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. | | 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. | | 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. | {/* */} {/* */} The folder path of the written files. {/* */} {/* */} A string of characters to include at the beginning of the written files. Often used for organizing database objects. {/* */} {/* */} 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. {/* */} {/* */} An AWS S3 bucket to load data into. The drop-down menu will include buckets tied to the [cloud provider credentials](/docs/guides/cloud-credentials) that you have associated with your [environment](/docs/guides/environments). {/* */} {/* */} Select a storage account linked to your desired blob container to be used for staging the data. For more information, read [Storage account overview](https://learn.microsoft.com/en-us/azure/storage/common/storage-account-overview). {/* */} {/* */} Select a Blob container to be used for staging the data. For more information, read [Introduction to Azure Blob storage](https://learn.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction). {/* */} {/* */} The drop-down menu will include Google Cloud Storage (GCS) buckets tied to the [cloud provider credentials](/docs/guides/cloud-credentials) that you have associated with your [environment](/docs/guides/environments). {/* */} {/* */} Select whether to overwrite files of the same name when this pipeline runs. Default is Yes. {/* */} * **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. {/* */} Select a [Databricks Unity Catalog](https://docs.databricks.com/en/data-governance/unity-catalog/index.html). 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. {/* */} {/* */} The Databricks schema. The special value `[Environment Default]` uses the schema defined in the environment. Read [Create and manage schemas](https://docs.databricks.com/en/data-governance/unity-catalog/create-schemas.html) to learn more. {/* */} {/* */} The name of the table to be created. {/* */} {/* */} * **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:** Each time the pipeline runs, two operations are performed: first, the table is truncated, meaning all existing rows are deleted. Then, your new rows are inserted. The table itself is never destroyed and recreated. * **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. {/* */} {/* */} * **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. {/* */} {/* */} 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. {/* */} {/* */} An AWS S3 bucket to stage data into. The drop-down menu will include buckets tied to the [cloud provider credentials](/docs/guides/cloud-credentials) that you have associated with your [environment](/docs/guides/environments). {/* */} {/* */} Select a storage account linked to your desired blob container to be used for staging the data. For more information, read [Storage account overview](https://learn.microsoft.com/en-us/azure/storage/common/storage-account-overview). {/* */} {/* */} Select a Blob container to be used for staging the data. For more information, read [Introduction to Azure Blob storage](https://learn.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction). {/* */} {/* */} * **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. | | 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. | | 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. | {/* */} {/* */} The folder path of the written files. {/* */} {/* */} A string of characters to include at the beginning of the written files. Often used for organizing database objects. {/* */} {/* */} 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. {/* */} {/* */} An AWS S3 bucket to load data into. The drop-down menu will include buckets tied to the [cloud provider credentials](/docs/guides/cloud-credentials) that you have associated with your [environment](/docs/guides/environments). {/* */} {/* */} Select a storage account linked to your desired blob container to be used for staging the data. For more information, read [Storage account overview](https://learn.microsoft.com/en-us/azure/storage/common/storage-account-overview). {/* */} {/* */} Select a Blob container to be used for staging the data. For more information, read [Introduction to Azure Blob storage](https://learn.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction). {/* */} {/* */} The drop-down menu will include Google Cloud Storage (GCS) buckets tied to the [cloud provider credentials](/docs/guides/cloud-credentials) that you have associated with your [environment](/docs/guides/environments). {/* */} {/* */} Select whether to overwrite files of the same name when this pipeline runs. Default is Yes. {/* */} * **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. {/* */} The Amazon Redshift schema. The special value `[Environment Default]` uses the schema defined in the environment. Read [Schemas](https://docs.aws.amazon.com/redshift/latest/dg/r_Schemas_and_tables.html) to learn more. For more information on using multiple schemas, read [Schemas](https://docs.aws.amazon.com/redshift/latest/dg/r_Schemas_and_tables.html). {/* */} {/* */} The name of the table to be created. {/* */} {/* */} * **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:** Each time the pipeline runs, two operations are performed: first, the table is truncated, meaning all existing rows are deleted. Then, your new rows are inserted. The table itself is never destroyed and recreated. * **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. {/* */} {/* */} * **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. {/* */} {/* */} An AWS S3 bucket to stage data into. The drop-down menu will include buckets tied to the [cloud provider credentials](/docs/guides/cloud-credentials) that you have associated with your [environment](/docs/guides/environments). {/* */} {/* */} * **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. | | 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. | | 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. | {/* */} {/* */} The folder path of the written files. {/* */} {/* */} A string of characters to include at the beginning of the written files. Often used for organizing database objects. {/* */} {/* */} 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. {/* */} {/* */} An AWS S3 bucket to load data into. The drop-down menu will include buckets tied to the [cloud provider credentials](/docs/guides/cloud-credentials) that you have associated with your [environment](/docs/guides/environments). {/* */} {/* */} Select a storage account linked to your desired blob container to be used for staging the data. For more information, read [Storage account overview](https://learn.microsoft.com/en-us/azure/storage/common/storage-account-overview). {/* */} {/* */} Select a Blob container to be used for staging the data. For more information, read [Introduction to Azure Blob storage](https://learn.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction). {/* */} {/* */} The drop-down menu will include Google Cloud Storage (GCS) buckets tied to the [cloud provider credentials](/docs/guides/cloud-credentials) that you have associated with your [environment](/docs/guides/environments). {/* */} {/* */} Select whether to overwrite files of the same name when this pipeline runs. Default is Yes. {/* */} Select the destination for your data: * **Google BigQuery:** Load your data into a table in Google BigQuery. * **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. Check the output to determine the format of the data. Select the Google BigQuery project to load data into. The special value `[Environment Default]` uses the project defined in the environment. Select the Google BigQuery dataset to load data into. The special value `[Environment Default]` uses the dataset defined in the environment. The name of the table to be created in your Google BigQuery project. You can use a [Table Input](/docs/components/table-input) component in a transformation pipeline to access and transform this data after it has been loaded. * **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:** Each time the pipeline runs, two operations are performed: first, the table is truncated, meaning all existing rows are deleted. Then, your new rows are inserted. The table itself is never destroyed and recreated. * **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. In the tree structure representing the source data, select the checkboxes next to the fields that you want to output from this connector, then click **Save**. Unselected fields will not be included in the output from this connector. For each field you select, you can click the **Edit Alias** icon to change the name used for this field in all downstream components in this pipeline. Select one or more columns to be designated as the table's primary key. * **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. Select the time period to use to partition the data loaded into your table, for example **Day** or **Month**. Select the Google Cloud Storage (GCS) bucket linked to your Google Cloud Platform account. Select whether to overwrite files of the same name when this pipeline runs. Default is **Yes**. * **Append Files in Folder:** Appends files to storage folder. This is the default setting. * **Overwrite Files in Folder:** Overwrite existing files with matching structure. The folder path for the files to be written to. Note that this path follows, but does not include, the bucket or container name. A string of characters that precedes the name of the written files. This can be useful for organizing database objects. A cloud storage location to load your data into files for storage. Choose either Amazon S3, Azure Storage, or Google Cloud Storage. ### Advanced Settings Set the severity level of logging. Choose from Error, Warn, Info, Debug, or Trace. Logs can be found in the **Message** field of the [task details](/docs/guides/designer-ui-basics#task-history-tab) after the pipeline has been run. {/* */} Choose whether to return the entire payload or only selected data objects. Read [Structure](/docs/guides/custom-connector-setup#structure) to learn how to select which data objects to include in your API response. * **No:** Will return the entire payload. This is the default setting. * **Yes:** Will return only the objects in Custom Connector that are marked as **Selected Data** in the **Structure** setting. ## 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: 1. In the [Azure portal](https://portal.azure.com/), navigate to your storage account. 2. In the menu, under **Data management**, click **Data protection**. 3. Clear the **Enable soft delete for blobs** checkbox. For more information, read [Soft delete for blobs](https://learn.microsoft.com/en-gb/azure/storage/blobs/soft-delete-blob-overview).