Migrating data from Paddle to Databricks using CData SSIS Components.
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 Paddle data to Databricks using the CData SSIS Components for Paddle 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.
Prerequisites
- Visual Studio 2022
- SQL Server Integration Services Projects extension for Visual Studio 2022
- CData SSIS Components for Databricks
- CData SSIS Components for Paddle
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 Paddle Source control and a CData Databricks Destination control to the data flow task.
Configure the Paddle source
Follow the steps below to specify properties required to connect to Paddle.
-
Double-click the CData Paddle Source to open the source component editor and add a new connection.
-
In the CData Paddle Connection Manager, configure the connection properties, then test and save the connection.
Using API Key Authentication
Paddle uses API key authentication. To obtain an API key:
- Sign in to your Paddle account at https://vendors.paddle.com
- Navigate to Developer Tools > Authentication
- Click "Generate API Key"
- Assign the appropriate permissions for the data you wish to access
- Copy the generated key (sandbox keys begin with pdl_sdbx_apikey_; production keys begin with pdl_live_apikey_)
After obtaining your API key, set the following connection properties:
- AuthScheme: Set this to APIKey.
- APIKey: Set this to your Paddle API key.
Example Connection String
Profile=C:\profiles\Paddle.apip;AuthScheme=APIKey;ProfileSettings="APIKey=your_api_key";
Connecting to Paddle
Once the authentication is configured, you can connect to Paddle and query data from any of the available tables such as Products, Customers, Subscriptions, and Transactions.
-
After saving the connection, select "Table or view" and select the table or view to export into Databricks, then close the CData Paddle Source Editor.
Configure the Databricks destination
With the Paddle 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.