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Table of Contents

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Overview

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Panel

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title	When you configure your Snap Account...

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.

Parameter

Field Dependency

Description

Label*

None.

Required. Unique user-provided label for the account.

Default Value: N/A

Example: ELT DLP Azure Account AD ON

Account Properties*

Use this field set to configure the information required to establish a JDBC connection with the account.

This field set consists of the following fields:

Database Type
Download JDBC Driver Automatically
JDBC JAR(s) and/or ZIP(s)
JDBC Driver Class
JDBC URL
Username
Password
Default Database Name

Database Type*

None.

Select the target data warehouse into which the queries must be loaded, that is Databricks Lakehouse Platform. This activates the following fields:

Use Token Based Authentication
Username
Password
Default Database Name
DBFS Folder path (source for loading Databricks table)

Default Value: N/A

Example: Databricks Lakehouse Platform

Download JDBC Driver Automatically

None.

Select this checkbox to allow the Snap account to download the certified JDBC Driver for DLP. The following fields are disabled when this checkbox is selected.

JDBC JAR(s) and/or ZIP(s) : JDBC Driver
JDBC driver class

To use a JDBC Driver of your choice, clear this checkbox, upload (to SLDB), and choose the required JAR files in the JDBC JAR(s) and/or ZIP(s): JDBC Driver field.

Note

title	Use of Custom JDBC JAR version

You can use a different JAR file version outside of the recommended listed JAR file versions.

Note

title	Spark JDBC and Databricks JDBC

If you do not select this checkbox and use an older JDBC JAR file (older than version 2.6.25), ensure that you use:

The old format JDBC URL ( jdbc:spark:// ) instead of the new one ( jdbc:databricks:// )
- For JDBC driver prior to version 2.6.25, the JDBC URL starts with jdbc:spark://
- For JDBC driver version 2.6.25 or later, the JDBC URL starts with jdbc:databricks://
The older JDBC Driver Class com.simba.spark.jdbc.Driver instead of the new com.databricks.client.jdbc.Driver.

Default Value: Not Selected

Example: Selected

JDBC JAR(s) and/or ZIP(s): JDBC Driver

Required when the Download JDBC Driver Automatically checkbox is not selected.

Upload the JDBC driver and other JAR files that you want to use into SLDB. Click to add a new row. Add each JDBC JAR file in a separate row. See JDBC driver for more information about JDBC drivers and downloading the appropriate driver for your account. Use the latest version of Databricks JDBC Driver to avoid errors while adding properties.

Default Value: N/A

Example: SimbaSparkJDBC42-2.6.21.1021.jar

JDBC driver class*

Required when the Download JDBC Driver Automatically checkbox is not selected.

Specify the driver class to use for your application. We recommend that you use com.simba.spark.jdbc.Driver for DLP, as other classes and methods may change due to future enhancements.

Default Value: N/A

Example: com.simba.spark.jdbc.Driver

JDBC URL*

None.

Enter the JDBC driver connection string that you want to use in the syntax provided below, for connecting to your DLP instance. See Microsoft's JDBC and ODBC drivers and configuration parameters for more information.

jdbc:spark://dbc-ede87531-a2ce.cloud.databricks.com:443/default;transportMode=http;ssl=1;httpPath=
sql/protocolv1/o/6968995337014351/0521-394181-guess934;AuthMech=3;UID=token;PWD=<personal-access-token>

Note

title	Spark JDBC and Databricks JDBC

If you do not choose the Download JDBC Driver Automatically checkbox and use an older JDBC JAR file (older than version 2.6.25), ensure that you use:

The old format JDBC URL ( jdbc:spark:// ) instead of the new one ( jdbc:databricks:// )
- For JDBC driver prior to version 2.6.25, the JDBC URL starts with jdbc:spark://
- For JDBC driver version 2.6.25 or later, the JDBC URL starts with jdbc:databricks://
The older JDBC Driver Class com.simba.spark.jdbc.Driver instead of the new com.databricks.client.jdbc.Driver.

Alternatively, you can make use of the Username, Password, and Database Name fields, along with the Advanced Properties > URL Properties field set to provide the parameters required for building your JDBC URL. See Passing your JDBC URL for more information.

Note

title	Avoid passing Password inside the JDBC URL

If you specify the password inside the JDBC URL, it is saved as it is and is not encrypted. We recommend passing your password using the Password field provided, instead, to ensure that your password is encrypted.

Default Value: N/A

Example: jdbc:spark://adb-2409532680880038.18.azuredatabricks.net:443/default;transportMode=http;ssl=1;httpPath=sql/protocolv1/o/2409532680880038/0326-212833-drier754;AuthMech=3;

Use Token Based Authentication

Database Type is Databricks Lakehouse Platform

Select this checkbox to use token-based authentication for connecting to the target database (DLP) instance. Activates the Token field.

Default value: Selected

Example: Not selected

Token*

When Use Token Based Authentication checkbox is selected.

Enter the token value for accessing the target database/folder path.

Default value: N/A

Example: <Encrypted>

Default Database Name*

None.

Enter the name of the database to use by default. This database is used if you do not specify one in the ELT Select, ELT Insert-Select, or ELT Merge Into Snaps.

Default value: N/A

Example: EMPLOYEEDB

DBFS Folder path (source for loading Databricks table)

Database Type is Databricks Lakehouse Platform

Required for ELT Load Snap.

Specify the fully qualified path to a target folder in your DBFS instance. It should begin with / which denotes the DBFS Root folder.

Note

If DBFS path has a mount point included (to a folder in S3 or Azure Blob Storage or ADLS Gen2 Storage), this field assumes that the specified external data folder is mounted on to DBFS path already. For example, if /mnt/mnt_point _to_s3/csv is the specified path to DBFS folder, this field assumes that you have already created mnt_point_to_s3 using DBFS mount command in Databricks Python Notebook.

Default value: N/A

Example: /my_DBFS/mounted0408_folder

Optimize Write

Database Type is Databricks Lakehouse Platform.

Select this checkbox to dynamically optimize the Apache Spark partition sizes based on the actual data. If selected, DLP attempts to write 128 MB files (this is an approximate size and can vary depending on dataset characteristics) for each table partition.

Default Value: Not Selected

Example: Selected

Auto Compact

Database Type is Databricks Lakehouse Platform.

Select this checkbox to allow DLP to compact small files being written into a Delta table.

Default Value: Not Selected

Example: Selected

Use Mounts

Checkbox

If this checkbox is selected, it provides three different sources to mount DBFS.

S3 Bucket Mounts

Use this field set to provide the information required to configure the S3 Database account. It consists of the following fields:

S3 Access Key
S3 Secret Key
S3 Bucket
Mount Point

S3 Access Key

String

Specify the S3 access key ID that you want to use for AWS authentication.

Default Value: N/A
Example: NAVRGGRV7EDCFVLKJH

S3 Secret Key

String

Specify the S3 secret key associated with the S3 Access-ID key listed in the S3 Access-key ID field.

Default Value: N/A
Example: 2RGiLmL/6bCujkKLaRuUJHY9uSDEjNYr+ozHRtg

S3 Bucket

String

Specify the name of the S3 bucket that you want to use for staging data to Snowflake.

Default Value: N/A
Example: sl-bucket-ca

Mount Point

String

The directory within DBFS to mount the source. Data written to mount point paths (/mnt) is stored outside of the DBFS root, for example in GCS buckets that are mounted as DBFS paths. Mounts are performed during Pipeline Validation and Pipeline Execution. It will not fail as the path is already mounted.

In the case of ELT Load Snap (DataBricks only) the priority of the loading from paths is as follows:
Cloud Storage Path (ELT Load snap) → DBFS Folder path (source for loading Databricks table) → First non-empty mount provided under Use Mounts in ELT DLP Account.

Note
All DBFS mount points should start with /mnt/ dir within the DBFS (example: /mnt/s3/some_bucket)

Learn more at Defining mount points.

Blob Storage Mounts

Use this field set to provide the information required to configure the blob storage mounts. It consists of the following fields:

Azure Blob Account Name
Azure Blob Storage Key
Container Name
Mount Point

Azure Blob Account Name

Database Type is Databricks Lake Platform. External Location is Blob Storage or Azure Data Lake Gen2.

Enter your Account name to access the selected Azure external storage location. You must configure this field if you want to use the ELT Load Snap.

Default value: N/A

Example: adlsgen2v02

Azure Blob Storage Key

Database Type is Databricks Lake Platform. External Location is Blob Storage or Azure Data Lake Gen2. Azure Auth Type is Shared Access Signature, Managed Identity, or Service Principals.

Enter the storage access key value corresponding to the Azure account mentioned in Storage Account field. See COPY INTO (Transact-SQL) in Microsoft Docs for more information on the expected values for this field.

This field is needed only when you want to define the File Name Pattern in the ELT Load Snap to locate and read the file/s from the Azure Folder path. Else, leave it blank.

Default value: N/A

Example: ufv!befDIuf#fnb$KH&_hweuf

Container Name

Database Type is Databricks Lake Platform. External Location is Blob Storage or Azure Data Lake Gen2.

Enter the name of the container in the Azure storage account.

Azure Container is not applicable to does not apply to the Redshift database.

Default value: N/A

Example: hrdepartment

Mount Point

String

The directory within DBFS to mount the source. Data written to mount point paths (/mnt) is stored outside of the DBFS root, for example in GCS buckets that are mounted as DBFS paths. Mounts are performed during Pipeline validation and Pipeline execution. It will not fail as the path is already mounted.

In the case of ELT Load Snap (DataBricks Databricks only) the priority of the loading from paths is followingas follows:
Cloud Storage Path (ELT Load snap) → DBFS Folder path (source for loading Databricks table) → First non-empty mount provided under Use Mounts in ELT DLP Account.

Note
All DBFS mountpoints mount points should start with /mnt/ dir within the DBFS (example: /mnt/s3/some_bucket)

Learn more at Defining mount points.

Data Lake Storage Gen2 Mounts

Use this field set to provide the information required to configure the Data Lake Storage Gen2 Mounts. It consists of the following fields:

Application Id
ADLS Gen2 Oauth2 Secret Key
ADLS Gen2 Tenant Id
ADLS Gen2 Account Name
ADLS Gen2 Container Name
Mount Point

Application Id

Database Type is Databricks Lake Platform. External Location is Blob Storage or Azure Data Lake Gen2.

Enter the name of theapplication (client) ID for the Azure Active Directory application that you want to configure.

Default value: N/A

Example: 635f1f0e-2378bd-412683734777efa9ef

ADLS Gen2 Oauth2 Secret Key

Database Type is Databricks Lake Platform. External Location is Blob Storage or Azure Data Lake Gen2.

Enter the Oauth2 secret key which is part of ADLS Gen2 account authentication in the Azure portal.

Default value: N/A

Example: 7ed4587-a77f-43bf-94c5dfg

ADLS Gen2 Tenant Id

Database Type is Databricks Lake Platform. External Location is Blob Storage or Azure Data Lake Gen2.

Enter the Tenant ID obtained after creating the application in the Azure portal.

Default value: N/A

Example: 2060aaafe-89d9-9514-eac46338ec05

ADLS Gen2 Account Name

Database Type is Databricks Lake Platform. External Location is Blob Storage or Azure Data Lake Gen2.

Enter the name of the account.

Default value: N/A

Example: Gen2 account name

ADLS Gen2 Container Name

Database Type is Databricks Lake Platform. External Location is Blob Storage or Azure Data Lake Gen2.

Enter the name of the container in the Azure storage account.

Azure Container is not applicable to does not apply to the Redshift database.

Default value: N/A

Example: hrdepartment

Mount Point

String

The directory within DBFS to mount the source. Data written to mount point paths (/mnt) is stored outside of the DBFS root, for example in GCS buckets that are mounted as DBFS paths. Mounts are performed during Pipeline Validation and Pipeline Execution. It will not fail as the path is already mounted.

In the case of ELT Load Snap (DataBricks only) the priority of the loading from paths is

following

as follows:
Cloud Storage Path (ELT Load snap) → DBFS Folder path (source for loading Databricks table) → First non-empty mount provided under Use Mounts in ELT DLP

ELT

Account.

Note
All Dbfs mountpoints should start with /mnt/ dir within dbfs (example /mnt/s3/some_bucket)

Learn more at Defining mount points.

Advanced Properties

Other parameters that you want to specify as URL properties. This field set consists of the following fields:

URL Properties
- URL Property Name
- URL Property Value

URL Properties

None.

The account parameter's name and its corresponding value. Click + to add more rows. Add each URL property-value pair in a separate row. Specify the name of the parameter in the URL Property Name field and its value in the URL Property Value field.

Default value: N/A

Example: MAX_CONCURRENCY_LEVEL: 3

Click Validate after entering the required details to ensure that all fields have been filled accurately. Click Apply to save the settings.

Note

title	Account Validation when using Pipeline parameters

If you have used Pipeline parameters or expressions to define values for the account fields above, the account validation (done by clicking the Validate button) is not supported. However, the Snaps that use this account may connect to the endpoint successfully depending on the accuracy and validity of the expressions and parameters used.

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The following image depicts the Spark configuration properties defined for a sample DLP instance.

Defining mount points

You can use the following external (cloud storage) buckets/containers and their folder paths for reading, writing, and loading Databricks delta tables.

AWS S3 (accessed using S3A protocol)
Azure Blob Storage (accessed using WASBS protocol)
ADLS Gen 2 (accessed using ABFSS protocol)

To enable Databricks runtime to access externally located data files from the Databricks cluster, you need to mount your bucket/container on to DBFS path. Once the ‘mount point’ is created through a cluster, the corresponding DBFS path allows accessing the external data location (cloud storage).

Security considerations

The mount point is only a pointer to your cloud storage location, so the data is never synced locally (i.e data is never moved into DBFS)
Once a mount point is created through a Python Notebook attached to a cluster, users of that cluster can immediately access the mount point.
To use the mount point in another running cluster, you must run dbutils.fs.refreshMounts() on that running cluster to make the newly created mount point available.
All users have read and write access to all the objects in the buckets/containers mounted to DBFS.
Azure Blob storage supports three blob types: block, append, and page. You can only mount block blobs to DBFS.

Mount an S3 Bucket on DBFS Path

Use Databricks Python Notebook to run the fsutils.mount() command with your access credentials, as follows:

Paste code macro

language	sql
title	Mounting an S3 bucket on DBFS

access_key = 'XXXX...'
secret_key = 'abcd...'
mount_name = 'my_s3_mnt_point'
s3_bucket_name = 'my-s3-bucket' 
encoded_secret_key = secret_key.replace('/', '%2F')
dbutils.fs.mount('s3a://%s:%s@%s' % (access_key, encoded_secret_key, s3_bucket_name), '/mnt/%s' % mount_name)

Verify that the mount point is created by running the display(dbutils.fs.mounts()) command.

Mount an Azure Blob Storage’s Container on DBFS Path

Use Databricks Python Notebook to run the fsutils.mount() command with your access credentials, as follows:

Paste code macro

language	sql
title	Mounting an Azure Blob Storage’s Container on DBFS

storage_account_name = '<SA_name>' 
storage_account_access_key = '<XXX...>'
container_name = '<container_name>'
mount_name = '<my_wasb_mnt_point>'
dbutils.fs.mount( 
					source = 'wasbs://%s@%s.blob.core.windows.net/' % (container_name, storage_account_name)
					mount_point = '/mnt/%s' % mount_name,
					extra_configs = {'fs.azure.account.key.%s.blob.core.windows.net' % storage_account_name : '%s' % storage_account_access_key}
				)

Verify that the mount point is created by running the display(dbutils.fs.mounts()) command.

Mount an ADLS Gen2 Container on DBFS Path

The steps below assume you have created the OAuth2 application to access your containers.

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Versions Compared

Old Version 15

New Version Current

Key

Overview

Defining mount points

Security considerations

Mount an S3 Bucket on DBFS Path

Mount an Azure Blob Storage’s Container on DBFS Path

Mount an ADLS Gen2 Container on DBFS Path

Page Comparison

Versions Compared

Old Version 15

New Version Current

Key

Overview

Defining mount points

Security considerations

Mount an S3 Bucket on DBFS Path

Mount an Azure Blob Storage’s Container on DBFS Path

Mount an ADLS Gen2 Container on DBFS Path