As businesses continue to generate large amounts of data, it's becoming increasingly important for organizations to be able to connect and analyze this data effectively. One tool that has gained popularity in recent years for data analysis is Microsoft's Power BI. Power BI is a business analytics service that provides interactive visualizations and business intelligence capabilities with an interface simple enough for end-users to create their own reports and dashboards.

Power BI comes equipped with a wide range of data connectors, including popular cloud-based services such as Azure, Salesforce, and Google Analytics, as well as on-premises data sources such as SQL Server, Oracle, and SharePoint. However, there may be cases where a custom data connector is required to connect to a data source that is not already supported by Power BI.

In this blog post, we will explore the development techniques and best practices for creating custom data connectors with Power BI.

What are Custom Data Connectors?

Custom data connectors are code libraries that enable Power BI to connect to data sources that are not natively supported. With custom data connectors, developers can create connections to any data source, whether it's a proprietary data store, web service, or file format. This allows organizations to integrate all their data sources into a single dashboard, enabling more accurate and informed decision-making.

Development Techniques for Creating Custom Data Connectors

Power Query SDK

Power Query SDK is a development kit that allows developers to build custom data connectors for Power BI. It provides a set of APIs and tools for building and testing connectors. The Power Query SDK is a Visual Studio extension and requires Visual Studio 2015 or later.

To create a custom data connector using the Power Query SDK, follow these steps:

• Create a new project in Visual Studio and select the "Power Query and M Language" template.
• In the project, create a new class that inherits from the "DataSourceKind" class. The DataSourceKind class defines the type of data source that the connector will connect to.
• Create a new class that inherits from the "DataSource" class. This class defines the connection details for the data source.
• Create a new class that inherits from the "Navigator" class. This class defines the logic for retrieving data from the data source.
• Create a new class that inherits from the "Table" class. This class defines the schema for the data retrieved from the data source.
• Once these classes are created, the connector can be packaged as a .mez file and installed in Power BI.

Power BI Custom Connector SDK

The Power BI Custom Connector SDK is a newer development kit that simplifies the process of building custom data connectors. It is a Node.js-based command-line tool that provides a set of APIs and tools for building and testing connectors.

To create a custom data connector using the Power BI Custom Connector SDK, follow these steps:

• Install the Power BI Custom Connector SDK using npm (Node.js package manager).
• Create a new project using the "pbiviz new" command.
• Use the "pbiviz package" command to package the connector as a .mez file.
• Install the .mez file in Power BI.

The Power BI Custom Connector SDK simplifies the development process by providing a set of templates and samples, as well as handling many of the low-level details of connector development.

Best Practices for Creating Custom Data Connectors

Test Thoroughly

Custom data connectors can be complex and may have many edge cases. It's important to test the connector thoroughly to ensure that it works correctly in all scenarios. This includes testing with a variety of data sources and ensuring that the connector can handle large amounts of data.

Follow Standard Coding Practices

When developing custom data connectors, it's important to follow standard coding practices. This includes using proper code organization, commenting code, and adhering to best practices for error handling and logging. Properly organized code can make the development process easier, especially if other developers need to work on the connector in the future.

Consider Security

Custom data connectors may be used to connect to sensitive data sources, so it's important to consider security when developing them. This includes encrypting credentials and securely storing them, as well as ensuring that the connector only accesses the data it needs and nothing more.

Keep Up with Updates

Power BI is constantly updated with new features and bug fixes. As a result, custom data connectors may need to be updated periodically to ensure compatibility with the latest version of Power BI. Keeping up with updates can help avoid compatibility issues and ensure that the connector continues to function properly.

Consider Performance

Performance is a key factor in data analysis, so it's important to consider performance when developing custom data connectors. This includes optimizing queries to retrieve data as efficiently as possible, as well as minimizing the amount of data transferred between the data source and Power BI.

Conclusion

Custom data connectors are a powerful tool for integrating data sources into Power BI. Whether you use the Power Query SDK or the Power BI Custom Connector SDK, creating custom data connectors requires a thorough understanding of the data source and the Power BI platform. By following best practices for development, testing, and security, you can create high-quality connectors that enable more accurate and informed decision-making.

If your organization has data sources that are not natively supported by Power BI, custom data connectors can help fill the gap. By leveraging the development techniques and best practices outlined in this blog post, you can create custom data connectors that connect to any data source, providing a comprehensive view of your organization's data and insights into key business metrics.