I want to know the tables of Real-Time Journey data into Dynamic 365 and how can we take them into Fabric Lakehouse? I want to know the names of

Real time Journey data

Hello NK-12031121-0,

You're looking to extract Real-Time Journey data from Dynamics 365 Customer Insights - Journeys (formerly Real-Time Marketing) into Fabric Lakehouse. This involves identifying the relevant Dataverse tables and setting up the data pipeline.

Understanding Real-Time Journey Data:
Real-Time Journey data in Dynamics 365 is stored in Dataverse tables related to customer interactions, journey executions, and analytics. Here are the key tables you'll likely need:

Key Dataverse Tables for Real-Time Journeys:

msdynmkt_customerjourney (Customer Journey):
- Contains the definition and metadata of your Real-Time Journeys.
- Provides information about journey goals, segments, and steps.
msdynmkt_customerjourneyinstance (Customer Journey Instance):
- Stores instances of journey executions.
- Tracks the progress of individual contacts through a journey.
msdynmkt_customerjourneyactivity (Customer Journey Activity):
- Records the activities performed within a journey (e.g., sending an email, triggering a custom event).
- Provides details about activity outcomes and timings.
msdynmkt_emailengagement (Email Engagement):
- Tracks email engagement metrics (opens, clicks, bounces).
- Provides insights into email performance.
msdynmkt_textmessageengagement (Text Message Engagement):
- Tracks text message engagement metrics(deliveries, clicks, ect).
msdynmkt_pushnotificationengagement (Push Notification Engagement):
- Tracks push notification engagement metrics(deliveries, clicks, ect).
msdynmkt_contactpoint (Contact Point):
- Stores information about contact points used in journeys (e.g., email addresses, phone numbers).
Contact (Contact):
- Stores contact information, which is essential for identifying and segmenting customers.
msdynmkt_segment (Segment):
- Stores segment definitions used in journeys.
msdynmkt_segmentmembership (Segment Membership):
- Tracks contact membership in segments.
msdynmkt_customchannelactivity (Custom Channel Activity):
- Stores data from custom channels that have been setup.

Extracting Data into Fabric Lakehouse:
Here's how you can move this data into Fabric Lakehouse:

Azure Synapse Link for Dataverse:
- This is the recommended approach for near real-time data replication from Dataverse to Azure Data Lake Storage Gen2 (which Fabric Lakehouse uses).
- Enable Synapse Link:
  - In the Power Platform admin center, enable Azure Synapse Link for Dataverse.
  - Select the Dataverse environment containing your Real-Time Journey data.
  - Choose the tables you want to export (the ones listed above).
  - Configure the Azure Data Lake Storage Gen2 account that will store the data.
- Fabric Lakehouse:
  - Once the Dataverse data has landed in Azure Data Lake Storage Gen2, you can then connect your Fabric Lakehouse to that storage account.
  - You can then create shortcuts, or use copy activity's within data pipelines to bring the data into the lakehouse tables.
Power Automate (For Incremental Updates):
- If you only need incremental updates or specific data transformations, you can use Power Automate.
- Dataverse Connector: Use the Dataverse connector to query the relevant tables.
- Lakehouse Connector: Use the Lakehouse connector to insert/update data in your Lakehouse tables.
- Limitations: Power Automate is best for smaller datasets and less frequent updates.
Azure Data Factory or Synapse Pipelines:
- For more complex data transformations and scheduled batch processing, use Azure Data Factory or Synapse Pipelines.
- Dataverse Connector: Use the Dataverse connector to extract data.
- Lakehouse Connector: Use the Lakehouse connector to load data into the Lakehouse.
- Data Transformation: Use Data Factory/Synapse transformation activities to clean, transform, and enrich the data.

Key Considerations:

Data Volumes: Real-Time Journey data can be voluminous, especially for high-traffic journeys. Consider data partitioning and optimization techniques.
Data Relationships: Understand the relationships between the Dataverse tables to ensure data integrity in your Lakehouse.
Incremental Updates: Implement incremental update strategies to avoid full data refreshes, which can be time-consuming.
Security: Secure your Azure Data Lake Storage Gen2 account and Fabric Lakehouse to protect sensitive customer data.
Performance: Monitor the performance of your data pipelines and optimize them as needed.

By using Azure Synapse Link for Dataverse and Fabric Lakehouse, you can create a robust data pipeline for analyzing Real-Time Journey data.

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Best Regards,
Daivat Vartak