Databricks
Big thanks to Evan Phillips and swishbi.com for contributing code, time, and a test environment.
Install dlt with Databricksโ
To install the dlt library with Databricks dependencies:
pip install "dlt[databricks]"
Set up your Databricks workspaceโ
To use the Databricks destination, you need:
- A Databricks workspace with a Unity Catalog metastore connected
- A Gen 2 Azure storage account and container
If you already have your Databricks workspace set up, you can skip to the Loader setup guide.
1. Create a Databricks workspace in Azureโ
Create a Databricks workspace in Azure
In your Azure Portal, search for Databricks and create a new workspace. In the "Pricing Tier" section, select "Premium" to be able to use the Unity Catalog.
Create an ADLS Gen 2 storage account
Search for "Storage accounts" in the Azure Portal and create a new storage account. Make sure it's a Data Lake Storage Gen 2 account by enabling "hierarchical namespace" when creating the account. Refer to the Azure documentation for further information.
Create a container in the storage account
In the storage account, create a new container. This will be used as a datastore for your Databricks catalog.
Create an Access Connector for Azure Databricks
This will allow Databricks to access your storage account. In the Azure Portal, search for "Access Connector for Azure Databricks" and create a new connector.
Grant access to your storage container
Navigate to the storage container you created earlier and select "Access control (IAM)" in the left-hand menu.
Add a new role assignment and select "Storage Blob Data Contributor" as the role. Under "Members" select "Managed Identity" and add the Databricks Access Connector you created in the previous step.
2. Set up a metastore and Unity Catalog and get your access tokenโ
Now go to your Databricks workspace
To get there from the Azure Portal, search for "Databricks", select your Databricks, and click "Launch Workspace".
In the top right corner, click on your email address and go to "Manage Account"
Go to "Data" and click on "Create Metastore"
Name your metastore and select a region. If you'd like to set up a storage container for the whole metastore, you can add your ADLS URL and Access Connector Id here. You can also do this on a granular level when creating the catalog.
In the next step, assign your metastore to your workspace.
Go back to your workspace and click on "Catalog" in the left-hand menu
Click "+ Add" and select "Add Storage Credential"
Create a name and paste in the resource ID of the Databricks Access Connector from the Azure portal. It will look something like this:
/subscriptions/<subscription_id>/resourceGroups/<resource_group>/providers/Microsoft.Databricks/accessConnectors/<connector_name>
Click "+ Add" again and select "Add external location"
Set the URL of your storage container. This should be in the form:
abfss://<container_name>@<storage_account_name>.dfs.core.windows.net/<path>
Once created, you can test the connection to make sure the container is accessible from Databricks.
Now you can create a catalog
Go to "Catalog" and click "Create Catalog". Name your catalog and select the storage location you created in the previous step.
Create your access token
Click your email in the top right corner and go to "User Settings". Go to "Developer" -> "Access Tokens". Generate a new token and save it. You will use it in your
dlt
configuration.
Loader setup guideโ
1. Initialize a project with a pipeline that loads to Databricks by running
dlt init chess databricks
2. Install the necessary dependencies for Databricks by running
pip install -r requirements.txt
This will install dlt with the databricks
extra, which contains the Databricks Python dbapi client.
4. Enter your credentials into .dlt/secrets.toml
.
This should include your connection parameters and your personal access token.
You can find your server hostname and HTTP path in the Databricks workspace dashboard. Go to "SQL Warehouses", select your warehouse (default is called "Starter Warehouse"), and go to "Connection details".
Example:
[destination.databricks.credentials]
server_hostname = "MY_DATABRICKS.azuredatabricks.net"
http_path = "/sql/1.0/warehouses/12345"
access_token = "MY_ACCESS_TOKEN"
catalog = "my_catalog"
See staging support for authentication options when dlt
copies files from buckets.
Write dispositionโ
All write dispositions are supported.
Data loadingโ
Data is loaded using INSERT VALUES
statements by default.
Efficient loading from a staging filesystem is also supported by configuring an Amazon S3 or Azure Blob Storage bucket as a staging destination. When staging is enabled, dlt
will upload data in parquet
files to the bucket and then use COPY INTO
statements to ingest the data into Databricks.
For more information on staging, see the staging support section below.
Supported file formatsโ
- insert-values is used by default.
- jsonl supported when staging is enabled (see limitations below).
- parquet supported when staging is enabled.
The jsonl
format has some limitations when used with Databricks:
- Compression must be disabled to load jsonl files in Databricks. Set
data_writer.disable_compression
totrue
in the dlt config when using this format. - The following data types are not supported when using the JSONL format with
databricks
:decimal
,json
,date
,binary
. Useparquet
if your data contains these types. - The
bigint
data type with precision is not supported with thejsonl
format.
Staging supportโ
Databricks supports both Amazon S3, Azure Blob Storage and Google Cloud Storage as staging locations. dlt
will upload files in parquet
format to the staging location and will instruct Databricks to load data from there.
Databricks and Amazon S3โ
Please refer to the S3 documentation for details on connecting your S3 bucket with the bucket_url and credentials.
Example to set up Databricks with S3 as a staging destination:
import dlt
# Create a dlt pipeline that will load
# chess player data to the Databricks destination
# via staging on S3
pipeline = dlt.pipeline(
pipeline_name='chess_pipeline',
destination='databricks',
staging=dlt.destinations.filesystem('s3://your-bucket-name'), # add this to activate the staging location
dataset_name='player_data',
)
Databricks and Azure Blob Storageโ
Refer to the Azure Blob Storage filesystem documentation for details on connecting your Azure Blob Storage container with the bucket_url and credentials.
Databricks requires that you use ABFS URLs in the following format: abfss://container_name@storage_account_name.dfs.core.windows.net/path
dlt
is able to adapt the other representation (i.e., az://container-name/path), but we recommend that you use the correct form.
Example to set up Databricks with Azure as a staging destination:
# Create a dlt pipeline that will load
# chess player data to the Databricks destination
# via staging on Azure Blob Storage
pipeline = dlt.pipeline(
pipeline_name='chess_pipeline',
destination='databricks',
staging=dlt.destinations.filesystem('abfss://dlt-ci-data@dltdata.dfs.core.windows.net'), # add this to activate the staging location
dataset_name='player_data'
)
Databricks and Google Cloud Storageโ
In order to load from Google Cloud Storage stage you must set-up the credentials via named credential. See below. Databricks does not allow to pass Google Credentials explicitly in SQL Statements.
Use external locations and stored credentialsโ
dlt
forwards bucket credentials to the COPY INTO
SQL command by default. You may prefer to use external locations or stored credentials instead that are stored on the Databricks side.
If you set up an external location for your staging path, you can tell dlt
to use it:
[destination.databricks]
is_staging_external_location=true
If you set up Databricks credentials named, for example, credential_x, you can tell dlt
to use it:
[destination.databricks]
staging_credentials_name="credential_x"
Both options are available from code:
import dlt
bricks = dlt.destinations.databricks(staging_credentials_name="credential_x")
dbt supportโ
This destination integrates with dbt via dbt-databricks.
Syncing of dlt
stateโ
This destination fully supports dlt state sync.
Additional Setup guidesโ
- Load data from AWS S3 to Databricks in python with dlt
- Load data from Google Cloud Storage to Databricks in python with dlt
- Load data from Looker to Databricks in python with dlt
- Load data from CircleCI to Databricks in python with dlt
- Load data from MySQL to Databricks in python with dlt
- Load data from Salesforce to Databricks in python with dlt
- Load data from Google Analytics to Databricks in python with dlt
- Load data from Adobe Analytics to Databricks in python with dlt
- Load data from Coinbase to Databricks in python with dlt
- Load data from Klaviyo to Databricks in python with dlt