ADF unable to ingest partitioned Delta data from Azure Synapse Link (Dataverse/FnO)
We are ingesting Dynamics 365 Finance & Operations (FnO) data into ADLS Gen2 using Azure Synapse Link for Dataverse, and then attempting to load that data into Azure SQL Database using Azure Data Factory (ADF). This is part of a migration effort as Export to Data Lake is being deprecated. Source Details Source: ADLS Gen2 Data generated by: Azure Synapse Link for Dataverse (FnO) Format on lake: Delta / Parquet Partitioned folder structure (e.g. PartitionId=xxxx) Destination: Azure SQL Database Issue Observed in ADF When configuring ADF pipelines: Using ADLS Gen2 dataset with: Delta / Parquet Recursive folder traversal Wildcard paths We encounter: No data returned in Data Preview Or runtime error such as: “No partitions information found in metadata file” Despite this: The data is present in ADLS The same data can be successfully queried using Synapse serverless SQL Key Question for ADF / Synapse Engineers What is the recommended and supported ADF ingestion pattern for: Partitioned Delta/Parquet data produced by Azure Synapse Link for Dataverse Specifically: Should ADF: Read Delta tables directly, or Use Synapse serverless SQL external tables/views as an intermediate layer? Is there a reference architecture for: Synapse Link → ADLS → ADF → Azure SQL Are there ADF limitations when consuming Synapse Link–generated Delta tables? Many customers are now forced to migrate due to Export to Data Lake deprecation, but current ADF documentation does not clearly explain how to replace existing ingestion pipelines when using Synapse Link for FnO. Any guidance, patterns, or official documentation would be greatly appreciated.
User Properties of Activities in ADF: How to add dynamic content in it?
On ADF, I am using a for each loop in which I am using an Execute Pipeline Activity which is getting executed for different iterations as per the values of the items provided to the For-Each Loop. I am stuck on a scenario which requires me to add the Dynamic Content Expression in the User Properties of individual activities of ADF. Specific to my case, I want to add the Dynamic Content Expression in the User Properties of Execute Pipeline Activity so that I get to individual runs of these activities on Azure Monitor with a specific label attached to it through its User Properties. The necessity to add the Dynamic Content Expression in the User Properties is due to the reason that each execution in respective iterations of these activities corresponds to a particular Step from a set of Steps configured for the Data Load Job as a whole, which has been orchestrated through ADF. To identify the association with the respective Job-Step, I require to add Dynamic Content Expression in its User Properties. Any sort of response regarding this is highly appreciated. Thank You!
Copy Data Activity Failed with Unreasonable Cause
It is a simple set up but it has baffled me a lot. I'd like to copy data to a data lake via API. Here are the steps I've taken: Created a HTTP linked service as below: Created a dataset with a HTTP Binary data format as below: Created a pipeline with a Copy Data activity only as shown below: Made sure linked service and dataset all working fine as below: Created a Sink dataset with 3 parameters as shown below: Passed parameters from pipeline to Sink dataset as below: That's all. Simple, right? But the pipeline failed with a clear message "usually this is caused by invalid credentials." as below: Summary: No need to worry about the Sink side of parameters etc. which I have used same thing for years on other pipelines and all succeeded. This time the API failed to reach a data lake from source side as said "invalid credentials". In Step 4 above one could see the linked service and dataset connections were succeeded, ie. credentials have been checked and passed already. How come it failed in data copy activity complaining an invalid credentials? Pretty weird. Any advice and suggestions will be welcomed.Introducing Azure Data Factory Community Templates
Today, we’re excited to announce that Community Templates are now available, giving our community members an opportunity to contribute to our existing template gallery. Templates are predefined Azure Data Factory pipelines that allow you to get started quickly with Data Factory. Templates are especially useful when you're new to Azure Data Factory or Azure Synapse pipelines and want to get started quickly, reducing development time for building data integration projects, and improving developer productivity. A wide variety of best practices are also infused in templates that cater to different scenarios that you can build on top of. And if you have Git enabled, templates help create shareable pipelines for better collaboration within the same factory.
Oracle 2.0 Upgrade Woes with Self-Hosted Integration Runtime
This past weekend my ADF instance finally got the prompt to upgrade linked services that use the Oracle 1.0 connector, so I thought, "no problem!" and got to work upgrading my self-hosted integration runtime to 5.50.9171.1 Most of my connection use service_name during authentication, so https://learn.microsoft.com/en-us/azure/data-factory/connector-oracle?tabs=data-factory, I should be able to connect using the Easy Connect (Plus) Naming convention. When I do, I encounter this error: Test connection operation failed. Failed to open the Oracle database connection. ORA-50201: Oracle Communication: Failed to connect to server or failed to parse connect string ORA-12650: No common encryption or data integrity algorithm https://docs.oracle.com/error-help/db/ora-12650/ I did some digging on this error code, and the troubleshooting doc suggests that I reach out to my Oracle DBA to update Oracle server settings. Which, I did, but I have zero confidence the DBA will take any action. https://learn.microsoft.com/en-us/azure/data-factory/connector-troubleshoot-oracle Then I happened across this documentation about the upgraded connector. https://learn.microsoft.com/en-us/azure/data-factory/connector-oracle?tabs=data-factory#upgrade-the-oracle-connector Is this for real? ADF won't be able to connect to old versions of Oracle? If so I'm effed because my company is so so legacy and all of our Oracle servers at 11g. I also tried adding additional connection properties in my linked service connection like this, but I have honestly no idea what I'm doing: Encryption client: accepted Encryption types client: AES128, AES192, AES256, 3DES112, 3DES168 Crypto checksum client: accepted Crypto checksum types client: SHA1, SHA256, SHA384, SHA512 But no matter what, the issue persists. :( Am I missing something stupid? Are there ways to handle the encryption type mismatch client-side from the VM that runs the self-hosted integration runtime? I would hate to be in the business of managing an Oracle environment and tsanames.ora files, but I also don't want to re-engineer almost 100 pipelines because of a connector incompatability.
ADF connection issue with Cassandra
Hi, I am trying to connect a cassandra DB hosted in azure cosmos db. I created the linked service but getting below error on test connection. Already checked the cassandra DB and its public network access is set to all networks. Google suggested enabling SSL but there is no such option in linked service. Please help. Failed to connect to the connector. Error code: 'Unknown', message: 'Failed to connect to Cassandra server due to, ErrorCode: InternalError' Failed to connect to the connector. Error code: 'InternalError', message: 'Failed to connect to Cassandra server due to, ErrorCode: InternalError' Failed to connect to Cassandra server due to, ErrorCode: InternalError All hosts tried for query failed (tried 51.107.58.67:10350: SocketException 'A request to send or receive data was disallowed because the socket is not connected and (when sending on a datagram socket using a sendto call) no address was supplied')Announcing the new Databricks Job activity in ADF!
We’re excited to announce that Azure Data Factory now supports the orchestration of Databricks Jobs! Databrick Jobs allow you to schedule and orchestrate a task or multiple tasks in a workflow in your Databricks workspace. Since any operation in Databricks can be a task, this means you can now run anything in Databricks via ADF, such as serverless jobs, SQL tasks, Delta Live Tables, batch inferencing with model serving endpoints, or automatically publishing and refreshing semantic models in the Power BI service. And with this new update, you’ll be able to trigger these workflows from your Azure Data Factory pipelines. To make use of this new activity, you’ll find a new Databricks activity under the Databricks activity group called Job. Once you’ve added the Job activity (Preview) to your pipeline canvas, you can connect to your Databricks workspace and configure the settings to select your Databricks job, allowing you to run the Job from your pipeline. We also know that allowing parameterization in your pipelines is important as it allows you to create generic reusable pipeline models. ADF continues to provide support for these patterns and is excited to extend this capability to the new Databricks Job activity. Under the settings of your Job activity, you’ll also be able to configure and set parameters to send to your Databricks job, allowing maximum flexibility and power for your orchestration jobs. To learn more, read Azure Databricks activity - Microsoft Fabric | Microsoft Learn. Have any questions or feedback? Leave a comment below!
SharePoint Online Multiple Files (Folder) Copy with Http Connector
This blog shows how to copy multiple files from a folder from SharePoint Online using ADF. Go through this public documentation on how to copy a single file - Copy data from SharePoint Online List by using Azure Data Factory - Azure Data Factory | Microsoft Docs
