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Begin by exporting the data from Microsoft Dataverse. Navigate to the Dataverse environment, select the tables you need, and use the built-in export functionality to download the data as a CSV or Excel file. Ensure you have the necessary permissions to export data from your Dataverse environment.
Once exported, review the data files to ensure they are properly formatted and clean. Check for any inconsistencies or errors that might affect the data’s integrity. Make any necessary adjustments to the file format, ensuring it is suitable for import into Starburst Galaxy, typically CSV format.
Log into your Starburst Galaxy account. If you do not have an account, you will need to create one and set up your environment. Familiarize yourself with the interface to understand how data is ingested and queried within the platform.
In the Starburst Galaxy interface, create a new schema where you will store the imported data. This involves defining the structure of your data, including table names, columns, and data types that correspond to your exported Dataverse data.
Upload the prepared data files to Starburst Galaxy. Use the platform's built-in data upload interface to transfer your CSV files into the newly created schema. Follow the prompts to map the CSV columns to the schema's columns accurately.
Once uploaded, perform any necessary data transformations to ensure compatibility and optimize performance. Use SQL queries within Starburst Galaxy to validate the data, checking for consistency, completeness, and accuracy. Run queries to confirm that the data aligns with your expectations and matches the original Dataverse data.
If ongoing data synchronization is required, set up a manual process to regularly export and import data. This could involve scheduling regular exports from Dataverse and repeating the import process to Starburst Galaxy. Although manual, this ensures the data remains current and accurate between the two platforms.
By following these steps, you can effectively move data from Microsoft Dataverse to Starburst Galaxy without relying on third-party connectors or integrations.
FAQs
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
Microsoft Dataverse provides access to the org-based database on Microsoft Dataverse in the current environment This connector was anciently known as Common Data Service. Microsoft Dataverse is one kind of data storage and management engine serving as a foundation for Microsoft’s Power Platform, Office 365, and Dynamics 365 apps. It can easily decouple the data from the application, permitting an administrator to analyze from every possible angle and report on data previously existing in different locations.
Microsoft Dataverse's API provides access to a wide range of data types, including:
1. Entities: These are the primary data objects in Dataverse, such as accounts, contacts, and leads.
2. Fields: These are the individual data elements within an entity, such as name, address, and phone number.
3. Relationships: These define the connections between entities, such as the relationship between a contact and an account.
4. Business rules: These are rules that govern how data is entered and processed within Dataverse.
5. Workflows: These are automated processes that can be triggered by specific events or conditions within Dataverse.
6. Plugins: These are custom code modules that can be used to extend the functionality of Dataverse.
7. Web resources: These are files such as HTML, JavaScript, and CSS that can be used to customize the user interface of Dataverse.
Overall, the Dataverse API provides access to a wide range of data types and functionality, making it a powerful tool for developers and users alike.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
