Migrating data from Microsoft Dataverse to Databricks using CData SSIS Components.

Easily push Microsoft Dataverse data to Databricks using the CData SSIS Tasks for Microsoft Dataverse and Databricks.

Databricks is a unified data analytics platform that allows organizations to easily process, analyze, and visualize large amounts of data. It combines data engineering, data science, and machine learning capabilities in a single platform, making it easier for teams to collaborate and derive insights from their data.

The CData SSIS Components enhance SQL Server Integration Services by enabling users to easily import and export data from various sources and destinations.

In this article, we explore the data type mapping considerations when exporting to Databricks and walk through how to migrate Microsoft Dataverse data to Databricks using the CData SSIS Components for Microsoft Dataverse and Databricks.

Data Type Mapping

Databricks Schema	CData Schema
int, integer, int32	int
smallint, short, int16	smallint
double, float, real	float
date	date
datetime, timestamp	datetime
time, timespan	time
string, varchar	If length > 4000: nvarchar(max), Otherwise: nvarchar(length)
long, int64, bigint	bigint
boolean, bool	tinyint
decimal, numeric	decimal
uuid	nvarchar(length)
binary, varbinary, longvarbinary	binary(1000) or varbinary(max) after SQL Server 2000

Special Considerations

String/VARCHAR: String columns from Databricks can map to different data types depending on the length of the column. If the column length exceeds 4000, then the column is mapped to nvarchar (max). Otherwise, the column is mapped to nvarchar (length).
DECIMAL Databricks supports DECIMAL types up to 38 digits of precision, but any source column beyond that can cause load errors.

About Microsoft Dataverse Data Integration

CData provides the easiest way to access and integrate live data from Microsoft Dataverse (formerly the Common Data Service). Customers use CData connectivity to:

Access both Dataverse Entities and Dataverse system tables to work with exactly the data they need.
Authenticate securely with Microsoft Dataverse in a variety of ways, including Azure Active Directory, Azure Managed Service Identity credentials, and Azure Service Principal using either a client secret or a certificate.
Use SQL stored procedures to manage Microsoft Dataverse entities - listing, creating, and removing associations between entities.

CData customers use our Dataverse connectivity solutions for a variety of reasons, whether they're looking to replicate their data into a data warehouse (alongside other data sources)or analyze live Dataverse data from their preferred data tools inside the Microsoft ecosystem (Power BI, Excel, etc.) or with external tools (Tableau, Looker, etc.).

Getting Started

Prerequisites

Visual Studio 2022
SQL Server Integration Services Projects extension for Visual Studio 2022
CData SSIS Components for Databricks
CData SSIS Components for Microsoft Dataverse

Create the project and add components

Open Visual Studio and create a new Integration Services Project.
Add a new Data Flow Task to the Control Flow screen and open the Data Flow Task.
Add a CData Microsoft Dataverse Source control and a CData Databricks Destination control to the data flow task.

Configure the Microsoft Dataverse source

Follow the steps below to specify properties required to connect to Microsoft Dataverse.

Double-click the CData Microsoft Dataverse Source to open the source component editor and add a new connection.
In the CData Microsoft Dataverse Connection Manager, configure the connection properties, then test and save the connection.
You can connect without setting any connection properties for your user credentials. Below are the minimum connection properties required to connect.
- InitiateOAuth: Set this to GETANDREFRESH. You can use InitiateOAuth to avoid repeating the OAuth exchange and manually setting the OAuthAccessToken.
- OrganizationUrl: Set this to the organization URL you are connecting to, such as https://myorganization.crm.dynamics.com.
- Tenant (optional): Set this if you wish to authenticate to a different tenant than your default. This is required to work with an organization not on your default Tenant.
When you connect the Common Data Service OAuth endpoint opens in your default browser. Log in and grant permissions. The OAuth process completes automatically.
After saving the connection, select "Table or view" and select the table or view to export into Databricks, then close the CData Microsoft Dataverse Source Editor.

Configure the Databricks destination

With the Microsoft Dataverse Source configured, we can configure the Databricks connection and map the columns.

Double-click the CData Databricks Destination to open the destination component editor and add a new connection.
In the CData Databricks Connection Manager, configure the connection properties, then test and save the connection. To connect to a Databricks cluster, set the properties as described below.
Note: The needed values can be found in your Databricks instance by navigating to Clusters, selecting the desired cluster, and selecting the JDBC/ODBC tab under Advanced Options.
- Server: Set to the Server Hostname of your Databricks cluster.
- HTTPPath: Set to the HTTP Path of your Databricks cluster.
- Token: Set to your personal access token (this value can be obtained by navigating to the User Settings page of your Databricks instance and selecting the Access Tokens tab).
Other helpful connection properties
- QueryPassthrough: When this is set to True, queries are passed through directly to Databricks.
- ConvertDateTimetoGMT: When this is set to True, the components will convert date-time values to GMT, instead of the local time of the machine.
- UseUploadApi: Setting this property to true will improve performance if there is a large amount of data in a Bulk INSERT operation.
- UseCloudFetch: This option specifies whether to use CloudFetch to improve query efficiency when the table contains over one million entries.
After saving the connection, select a table in the Use a Table menu and in the Action menu, select Insert.
On the Column Mappings tab, configure the mappings from the input columns to the destination columns.

Run the project

You can now run the project. After the SSIS Task has finished executing, data from your SQL table will be exported to the chosen table.

Ready to get started?

Download a free trial of the Microsoft Dataverse SSIS Component to get started:

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Learn more:

Microsoft Dataverse SSIS Components

Powerful SSIS Components that allows you to easily connect SQL Server with Microsoft Dataverse through SSIS Workflows.

Use the Data Flow Components to synchronize with any Dataverse entities. Perfect for data synchronization, local back-ups, workflow automation, and more!