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Start by setting up an Azure Data Lake Storage (ADLS) account. This will act as an intermediary storage solution where you can export data from Microsoft Dataverse. Create a container where you will store the exported data. Ensure you have the necessary access permissions to manage the data in this container.
Use the built-in data export service in Microsoft Dataverse to export data to Azure Data Lake Storage. You can set this up by navigating to the Power Apps portal, selecting your environment, and configuring the data export profile to export data incrementally or as a full export to the ADLS container you set up.
Install Apache Iceberg on the data platform where you plan to store and query your datasets. This could typically be a Hadoop ecosystem or an environment that supports Apache Iceberg tables such as Apache Spark. Ensure that your environment is correctly configured to work with Iceberg.
Configure your data platform to have access to the Azure Data Lake Storage account. This involves setting up credentials (such as SAS tokens or service principal authentication) to allow your data platform to read data from Azure Data Lake.
Write a script or use a tool like Apache Spark to read the data from Azure Data Lake Storage and transform it into a format compatible with Apache Iceberg. This involves converting the data into Parquet or Avro files, which are the typical file formats used by Iceberg, and organizing them into the directory structure that Iceberg expects.
With your data in the correct format, use Apache Spark or another compatible tool to load the data into Iceberg tables. Create the necessary Iceberg tables and use SQL-like commands to insert the transformed data into these tables, ensuring that the schema matches the source data structure from Dataverse.
Once the data is loaded, conduct a series of checks to verify that the data has been moved and transformed accurately. Perform queries on the Iceberg tables to ensure data integrity and that the data is accessible as expected. Validate data consistency by comparing it with the original dataset in Dataverse to ensure no data loss or corruption has occurred during the transfer process.
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:
