Storage Blob Data Contributor role. For more information, read User assigned with the Storage Blob Data Contributor role.
For Snowflake projects, this component supersedes the HubSpot Query component, which is no longer available for new pipelines.Existing pipelines with the HubSpot Query component will continue to work as expected.Databricks and Amazon Redshift projects should continue to use the HubSpot Query component.
Properties
Reference material is provided below for the Connect, Configure, Destination, and Advanced Settings properties.A human-readable name for the component.
Connect
Choose OAuth 2.0 Authorization Code or API key as the authentication method.
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.
This parameter is only available when you choose API Key as the authentication method.Choose the secret definition that represents your credentials for this connector.If you have not already saved your credentials for this connector as a secret definition, click Add secret to create a secret definition representing these credentials. Read Secrets and secret definitions for details about creating a secret definition.Read HubSpot authentication guide to learn how to obtain a Private App token.
- 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.
Configure
- Full Load: Load all data from the data source on every pipeline run.
- Incremental Load: Load only new or updated data since the last successful pipeline run using a high-water mark column.
- Basic: Configure data selection using the Data Source, Data Selection, Data Source Filter, Combine Filters, and Row Limit properties.
- Advanced: Write your own SQL query using the SQL Query property.
- Incremental: Use incremental data loading with a high-water mark column. Available when Load Type is set to Incremental Load.
Write an SQL query to retrieve your data. Only available for Snowflake destinations when Mode is set to Advanced.
Select the schema to use when browsing available data sources. Only available for Snowflake destinations.
Select the data source to query from. Available when Mode is set to Basic.
Select the columns to include from the data source. Available when Mode is set to Basic.
Filter the rows returned from the data source. Available when Mode is set to Basic.
Choose how to combine multiple filters. Select AND to return rows matching all filters, or OR to return rows matching any filter. Available when Mode is set to Basic.
The maximum number of rows to return from the data source. Leave blank to return all rows. Available when Mode is set to Basic.
Select the column to use as the high-water mark for incremental loading. Available when Load Type is set to Incremental Load.
Destination
- Snowflake
- Google BigQuery
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.
- Snowflake
- Cloud Storage
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.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.The Snowflake schema. The special value
[Environment Default] uses the schema defined in the environment. Read Database, Schema, and Share DDL to learn more.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.
Define what happens if the table name already exists in the specified Snowflake database and schema. Only available when Full Load is selected.
- 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.
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.
- 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. 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.
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.
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.
Select your named stage. Read Creating a named stage to learn how to create a new named stage.
Advanced Settings
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.
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.
- Will log the query, the number of rows returned by it, the start of execution and 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 additionally log the body of the request and the response.
- Will additionally log transport-level communication with the data source. This includes SSL negotiation.
- Will additionally log communication with the data source, as well as additional details that may be helpful in troubleshooting problems. This includes interface commands.
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.
Currently, this property is only applicable when using Snowflake as your destination.
When Yes, remove leading and trailing characters from a string column. The default is No.
Data model
The JDBC driver for this component models HubSpot 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 optionSupportEnhancedSQL is set to true by default and typically circumvents most API limitations.
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.