How to load data from Microsoft Dataverse to Apache Iceberg

Learn how to use Airbyte to synchronize your Microsoft Dataverse data into Apache Iceberg within minutes.

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Set up a Microsoft Dataverse connector in Airbyte

Connect to Microsoft Dataverse or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up Apache Iceberg for your extracted Microsoft Dataverse data

Select Apache Iceberg where you want to import data from your Microsoft Dataverse source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Microsoft Dataverse to Apache Iceberg in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.

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TL;DR

set up Microsoft Dataverse as a source connector (using Auth, or usually an API key)
set up Apache Iceberg as a destination connector
define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Microsoft Dataverse

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.

What is Apache Iceberg

For huge analytical tables, Apache Iceberg is a high-performance format. Using Apache Iceberg, engines such as Spark, Trino, Flink, Presto, Hive and Impala can safely work with the same tables, at the same time, providing the reliability and simplicity of SQL tables to big data. With Apache Iceberg, you can merge new data, update existing rows, and delete specific rows. Data files can be eagerly rewritten or deleted deltas can be used to make updates faster.

Integrate Microsoft Dataverse with Apache Iceberg in minutes

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Prerequisites

A Microsoft Dataverse account to transfer your customer data automatically from.
A Apache Iceberg account.
An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including Microsoft Dataverse and Apache Iceberg, for seamless data migration.

When using Airbyte to move data from Microsoft Dataverse to Apache Iceberg, it extracts data from Microsoft Dataverse using the source connector, converts it into a format Apache Iceberg can ingest using the provided schema, and then loads it into Apache Iceberg via the destination connector. This allows businesses to leverage their Microsoft Dataverse data for advanced analytics and insights within Apache Iceberg, simplifying the ETL process and saving significant time and resources.

Step 1: Set up Microsoft Dataverse as a source connector

1. Open the Airbyte platform and navigate to the "Sources" tab on the left-hand side of the screen.
2. Click on the "Microsoft Dataverse" source connector and select "Create New Connection".
3. Enter a name for the connection and click "Next".
4. In the "Authentication" section, select "OAuth2" as the authentication method.
5. Click on the "Configure OAuth2" button and enter the required credentials for your Microsoft Dataverse account.
6. Once the credentials have been entered, click "Authorize" to allow Airbyte to access your Microsoft Dataverse data.
7. Select the entities you want to replicate and configure any additional settings, such as the replication frequency and data mapping.
8. Click "Test" to ensure that the connection is working properly.
9. If the test is successful, click "Create Connection" to save the connection and begin replicating data from Microsoft Dataverse to Airbyte.

Step 2: Set up Apache Iceberg as a destination connector

1. Open the Airbyte platform and navigate to the "Destinations" tab on the left-hand side of the screen.
2. Click on the "Apache Iceberg" destination connector and select "Create new connection."
3. Enter a name for your connection and provide the necessary credentials for your Apache Iceberg database, including the host, port, database name, username, and password.
4. Test the connection to ensure that it is successful. 5. Select the tables or data sources that you want to replicate to your Apache Iceberg database.
6. Configure any additional settings or options for your connection, such as the frequency of data replication or any transformations that you want to apply to your data.
7. Save your connection and start the replication process.
8. Monitor the progress of your data replication and troubleshoot any issues that may arise.
9. Once the replication process is complete, verify that your data has been successfully replicated to your Apache Iceberg database.
10. Use your Apache Iceberg database to analyze and query your data as needed.

Step 3: Set up a connection to sync your Microsoft Dataverse data to Apache Iceberg

Once you've successfully connected Microsoft Dataverse as a data source and Apache Iceberg as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
Choose your source: Select Microsoft Dataverse from the dropdown list of your configured sources.
Select your destination: Choose Apache Iceberg from the dropdown list of your configured destinations.
Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
Select the data to sync: Choose the specific Microsoft Dataverse objects you want to import data from towards Apache Iceberg. You can sync all data or select specific tables and fields.
Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Microsoft Dataverse to Apache Iceberg according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your Apache Iceberg data warehouse is always up-to-date with your Microsoft Dataverse data.

Use Cases to transfer your Microsoft Dataverse data to Apache Iceberg

Integrating data from Microsoft Dataverse to Apache Iceberg provides several benefits. Here are a few use cases:

Advanced Analytics: Apache Iceberg’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Microsoft Dataverse data, extracting insights that wouldn't be possible within Microsoft Dataverse alone.
Data Consolidation: If you're using multiple other sources along with Microsoft Dataverse, syncing to Apache Iceberg allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
Historical Data Analysis: Microsoft Dataverse has limits on historical data. Syncing data to Apache Iceberg allows for long-term data retention and analysis of historical trends over time.
Data Security and Compliance: Apache Iceberg provides robust data security features. Syncing Microsoft Dataverse data to Apache Iceberg ensures your data is secured and allows for advanced data governance and compliance management.
Scalability: Apache Iceberg can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Microsoft Dataverse data.
Data Science and Machine Learning: By having Microsoft Dataverse data in Apache Iceberg, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
Reporting and Visualization: While Microsoft Dataverse provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to Apache Iceberg, providing more advanced business intelligence options. If you have a Microsoft Dataverse table that needs to be converted to a Apache Iceberg table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

Configure a Microsoft Dataverse account as an Airbyte data source connector.
Configure Apache Iceberg as a data destination connector.
Create an Airbyte data pipeline that will automatically be moving data directly from Microsoft Dataverse to Apache Iceberg after you set a schedule

With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.

We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!

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How to Sync Microsoft Dataverse to Apache Iceberg Manually

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Step 8:

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.

What is Microsoft Dataverse?

What data can you extract from Microsoft Dataverse?

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.

How do I transfer data from Microsoft Dataverse?

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:
1. Set up Microsoft Dataverse to Apache Iceberg as a source connector (using Auth, or usually an API key)
2. Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector
3. Define which data you want to transfer from Microsoft Dataverse to Apache Iceberg and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

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.

Back to all tutorials

Databases

How to load data from Microsoft Dataverse to Apache Iceberg

Learn how to use Airbyte to synchronize your Microsoft Dataverse data into Apache Iceberg within minutes.

Try Airbyte Cloud free

Deploy Airbyte Open Source

TL;DR

What is Microsoft Dataverse

What is Apache Iceberg

Prerequisites

Step 1: Setup Microsoft Dataverse

Step 2: Setup Apache Iceberg

Step 3: Setup a connection Use Cases Wrapping Up

Example H2

Example H3

Sync data between Microsoft Dataverse and Apache Iceberg

Get your data syncing in minutes

Try Airbyte free

TL;DR

set up Microsoft Dataverse as a source connector (using Auth, or usually an API key)
set up Apache Iceberg as a destination connector
define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Microsoft Dataverse

What is Apache Iceberg

Integrate Microsoft Dataverse with Apache Iceberg in minutes

Try for free now

Prerequisites

A Microsoft Dataverse account to transfer your customer data automatically from.
A Apache Iceberg account.
An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Step 1: Set up Microsoft Dataverse as a source connector

Step 2: Set up Apache Iceberg as a destination connector

Step 3: Set up a connection to sync your Microsoft Dataverse data to Apache Iceberg

Once you've successfully connected Microsoft Dataverse as a data source and Apache Iceberg as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
Choose your source: Select Microsoft Dataverse from the dropdown list of your configured sources.
Select your destination: Choose Apache Iceberg from the dropdown list of your configured destinations.
Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
Select the data to sync: Choose the specific Microsoft Dataverse objects you want to import data from towards Apache Iceberg. You can sync all data or select specific tables and fields.
Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Microsoft Dataverse to Apache Iceberg according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your Apache Iceberg data warehouse is always up-to-date with your Microsoft Dataverse data.

Use Cases to transfer your Microsoft Dataverse data to Apache Iceberg

Integrating data from Microsoft Dataverse to Apache Iceberg provides several benefits. Here are a few use cases:

Advanced Analytics: Apache Iceberg’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Microsoft Dataverse data, extracting insights that wouldn't be possible within Microsoft Dataverse alone.
Data Consolidation: If you're using multiple other sources along with Microsoft Dataverse, syncing to Apache Iceberg allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
Historical Data Analysis: Microsoft Dataverse has limits on historical data. Syncing data to Apache Iceberg allows for long-term data retention and analysis of historical trends over time.
Data Security and Compliance: Apache Iceberg provides robust data security features. Syncing Microsoft Dataverse data to Apache Iceberg ensures your data is secured and allows for advanced data governance and compliance management.
Scalability: Apache Iceberg can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Microsoft Dataverse data.
Data Science and Machine Learning: By having Microsoft Dataverse data in Apache Iceberg, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
Reporting and Visualization: While Microsoft Dataverse provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to Apache Iceberg, providing more advanced business intelligence options. If you have a Microsoft Dataverse table that needs to be converted to a Apache Iceberg table, Airbyte can do that automatically.