In this article

Overview

You can use this Snap to execute a Databricks SQL DELETE statement based on specific conditions. Ensure adequate discretion while using this Snap as it can truncate the table if run without specifying a WHERE condition for the DELETE statement.

Snap Type

Databricks - Delete Snap is a write-type Snap that deletes rows from a target DLP table.

Prerequisites

Valid access credentials to a DLP instance.
Valid access to the external source data in one of the following: Azure Blob Storage, ADLS Gen2, DBFS, GCP, AWS S3, or another database (JDBC-compatible).

Support for Ultra Pipelines

Works in Ultra Pipelines.

Limitations and Known Issues

None.

Snap Views

Type

Format

Number of Views

Examples of Upstream and Downstream Snaps

Description

Input

Document

Min: 0
Max: 1

JSON Generator
Copy
Databricks - Select

A JSON document containing the reference to the table, rows to be deleted.

Output

Document

Min: 0
Max: 1

Databricks - Select
JSON Parser

A JSON document containing the result of the delete operation on the target table.

Error

Error handling is a generic way to handle errors without losing data or failing the Snap execution. You can handle the errors that the Snap might encounter while running the Pipeline by choosing one of the following options from the When errors occur list under the Views tab. The available options are:

Stop Pipeline Execution: Stops the current pipeline execution when the Snap encounters an error.
Discard Error Data and Continue: Ignores the error, discards that record, and continues with the rest of the records.
Route Error Data to Error View: Routes the error data to an error view without stopping the Snap execution.

Learn more about Error handling in Pipelines.

Snap Settings

Asterisk ( * ): Indicates a mandatory field.
Suggestion icon (): Indicates a list that is dynamically populated based on the configuration.
Expression icon ( ): Indicates whether the value is an expression (if enabled) or a static value (if disabled). Learn more about Using Expressions in SnapLogic.
Add icon ( ): Indicates that you can add fields in the fieldset.
Remove icon ( ): Indicates that you can remove fields from the fieldset.

Field Name	Field Type	Description
Label* Default Value: Databricks - Delete Example: Db_Del_Duplicates	String	The name for the Snap. You can modify this to be more specific, especially if you have more than one of the same Snap in your Pipeline.
Database name Default Value: None. Example: Cust_DB	String/Expression/Suggestion	Enter your corresponding DLP database name for the DELETE statement to delete existing rows from the table.
Table name* Default Value: None. Example: Cust_List	String/Expression/Suggestion	Enter your table name for the DELETE statement to delete existing rows from.
Delete Condition (Truncates Table if empty) Default Value: Example: last_login_date < ‘2010-01-01’	String/Expression/Suggestion	Specify the condition for the DELETE statement to filter the rows to delete from the target table.
Snap Execution Default Value: Example: Validate & Execute	Dropdown list	Select one of the three modes in which the Snap executes. Available options are: Validate & Execute: Performs limited execution of the Snap, and generates a data preview during Pipeline validation. Subsequently, performs full execution of the Snap (unlimited records) during Pipeline runtime. Execute only: Performs full execution of the Snap during Pipeline execution without generating preview data. Disabled: Disables the Snap and all Snaps that are downstream from it.

Troubleshooting

Error	Reason	Resolution
Missing property value	You have not specified a value for the required field where this message appears.	Ensure that you specify valid values for all required fields.

Examples

Deleting Employee Information from DLP table

Consider the scenario where we want to delete information of certain employees from an intermediate data location that runs on DLP. We can achieve this through a Pipeline containing the Databricks - Delete Snap.

We configure this Snap (Pipeline) to delete the employee rows from the company_employees table in our DLP instance if their joining date is before Jan 01, 2010. It uses an appropriate account

Upon validation, the Pipeline deletes the rows satisfying the condition specified and returns the status of the operation in the Snap’s output.

Download this Pipeline.

Downloads

Download and import the Pipeline into SnapLogic.
Configure Snap accounts as applicable.
Provide Pipeline parameters as applicable.

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Snap Pack History

Release	Snap Pack Version	Date	Type	Updates
August 2024	main27765	21 Aug 2024	Stable	Upgraded the `org.json.json` library from v20090211 to v20240303, which is fully backward compatible.
May 2024	437patches27246	08 Aug 2024	Latest	Added Databricks - Run Job. This Snap executes a job, checks its status in Databricks, and, based on the job's status, completes or fails the pipeline.
May 2024	437patches26400	15 May 2024	Latest	Fixed an invalid session handle issue with the Databricks Snap Pack that intermittently triggered an error message when the Snaps failed to connect with Databricks to execute the SQL statement.
May 2024	main26341	08 May 2024	Stable	Updated the Delete Condition (Truncates a Table if empty) field in the Databricks - Delete Snap to Delete condition (deletes all records from a table if left blank) to indicate that all entries will be deleted from the table when this field is blank, but no truncate operation is performed.
February 2024	main25112	14 Feb 2024	Stable	Updated and certified against the current SnapLogic Platform release.
November 2023	main23721	08 Nov 2023	Stable	Updated and certified against the current SnapLogic Platform release.
August 2023	main22460	16 Aug 2023	Stable	Updated and certified against the current SnapLogic Platform release.
May 2023	433patches21630	28 Jun 2023	Latest	Enhanced the performance of the Databricks - Insert Snap to improve the amount of time it takes for validation.
May 2023	main21015	10 May 2023	Stable	Upgraded with the latest SnapLogic Platform release.
February 2023	main19844	09 Feb 2023	Stable	Upgraded with the latest SnapLogic Platform release.
November 2022	main18944	10 Nov 2022	Stable	The Databricks - Insert Snap now creates the target table only from the table metadata of the second input view when the following conditions are met: The Create table if not present checkbox is selected. The target table does not exist. The table metadata is provided in the second input view.
September 2022	430patches18305	29 Sep 2022	Latest	The name of the Databricks - Multi Execute Snap is simplified to Databricks - Execute Snap. The Use Result Query checkbox in the Databricks - Execute Snap enables you to include in the Snap's output the result of running (during validation) each SQL statement specified in the Snap. The Retry mechanism for the Databricks Snap Pack enables the following Databricks Snaps to repeatedly perform the selected operations for the specified number of times when the Snap account connection fails or times out. Databricks - Delete Databricks - Insert Databricks - Select Databricks - Execute Databricks - Bulk Load (when the Source Type is Input View) Databricks - Merge Into (when the Source Type is Input View) The following fields are added to each Databricks Snap as part of this enhancement: Number of Retries: The number of attempts the Snap should make to perform the selected operation when the Snap account connection fails or times out. Retry Interval (seconds): The time interval in seconds between two consecutive retry attempts.
September 2022	430patches17796	28 Sep 2022	Latest	The Manage Queued Queries property in the Databricks Snap Pack enables you to decide whether a given Snap should continue or cancel executing the queued Databricks SQL queries.
August 2022	main17386	11 Aug 2022	Stable	Upgraded with the latest SnapLogic Platform release.
4.29.2.0	42920rc17045	15 Jul 2022	Latest	A new Snap Pack for Databricks Lakehouse Platform (Databricks or DLP) introduces the following Snaps: Databricks - Select: Retrieves information from the target Databricks table. Databricks - Insert: Inserts new rows of data in the target Databricks table. Databricks - Delete: Deletes data from a target Databricks table. Databricks - Bulk Load: Loads millions of rows of data in the target table through a single load operation. Databricks - Unload: Unloads data from a target Databricks table through a single unload operation. Databricks - Merge Into: Updates millions of existing rows and inserts new rows in a target Databricks table through a single operation. Databricks - Multi Execute: Runs multiple SQL statements on the target Databricks instance.

Databricks - Delete