pipeline
3 TopicsADF Data Flow Fails with "Path does not resolve to any file" — Dynamic Parameters via Trigger
Hi guys, I'm running into an issue with my Azure Data Factory pipeline triggered by a Blob event. The trigger passes dynamic folderPath and fileName values into a parameterized dataset and mapping data flow. Everything works perfectly when I debug the pipeline manually or trigger the pipeline manually with the trigger and pass in the values for folderPath and fileName directly. However, when the pipeline is triggered automatically via the blob event, the data flow fails with the following error: Error Message: Job failed due to reason: at Source 'CSVsource': Path /financials/V02/Forecast/ForecastSampleV02.csv does not resolve to any file(s). Please make sure the file/folder exists and is not hidden. At the same time, please ensure special character is not included in file/folder name, for example, name starting with _ I've verified the blob file exists. The trigger fires correctly and passes parameters The path looks valid. The dataset is parameterized correctly with @dataset().folderPath and @dataset().fileName I've attached screenshots of: 🔵 00-Pipeline Trigger Configuration On Blob creation 🔵 01-Trigger Parameters 🔵 02-Pipeline Parameters 🔵 03-Data flow Parameters 🔵 04-Data flow Parameters without default value 🔵 05-Data flow CSVsource parameters 🔵 06-Data flow Source Dataset 🔵 07-Data flow Source dataset Parameters 🔵 08-Data flow Source Parameters 🔵 09-Parameters passed to the pipeline from the trigger 🔵 10-Data flow error message Here are all the images What could be causing the data flow to fail on file path resolution only when triggered, even though the exact same parameters succeed during manual debug runs? Could this be related to: Extra slashes or encoding in trigger output? Misuse of @dataset().folderPath and fileName in the dataset? Limitations in how blob trigger outputs are parsed? Any insights would be appreciated! Thank youSolved60Views0likes1CommentWhat Are the Ways to Dynamically Invoke Pipelines in ADF from Another Pipeline?
I am exploring different approaches to dynamically invoke ADF pipelines from within another pipeline as part of a modular and scalable orchestration strategy. My use case involves having multiple reusable pipelines that can be called conditionally or in sequence, based on configuration stored externally (such as in a SQL Managed Instance or another Azure-native source). I am aware of a few patterns like using the Execute Pipeline activity within a ForEach loop, but I would like to understand the full range of available and supported options for dynamically invoking pipelines from within ADF. Could you please clarify the possible approaches for achieving this? Specifically, I am interested in: Using ForEach with Execute Pipeline activity How to structure the control flow for calling multiple pipelines in sequence or parallel. How to pass pipeline names dynamically. Dynamic pipeline name resolution Is it possible to pass the pipeline name as a parameter to the Execute Pipeline activity? How to handle validation when the pipeline name is dynamic? Parameterized execution Best practices for passing dynamic parameters to each pipeline when calling them in a loop or based on external config. Calling ADF pipelines via REST API or Web Activity When would this be preferred over native Execute Pipeline? How to handle authentication and response handling? If there are any recommendations, gotchas, or best practices related to dynamic pipeline orchestration in ADF, I would greatly appreciate your insights. Thanks!29Views0likes0CommentsHow to Orchestrate ADF Pipelines as Selectable Steps in a Configurable Job
I am working on building a dynamic job orchestration mechanism using Azure Data Factory (ADF). I have multiple pipelines in ADF, and each pipeline represents a distinct step in a larger job. I would like to implement a solution where I can dynamically select or deselect individual pipeline steps (i.e., ADF pipelines) as part of a job. The idea is to configure a job by checking/unchecking steps, and then execute only the selected ones in sequence or based on dependencies. Available resources for this solution: Azure Data Factory (ADF) Azure SQL Managed Instance (SQL MI) Any other relevant Azure-native service (if needed) Could you please suggest a solution that meets the following requirements: Dynamically configure which pipelines (steps) to include in a job. Add or remove steps without changing hardcoded logic in ADF. Ensure scalability and maintainability of the orchestration logic. Keep the solution within the scope of ADF, SQL MI, and potentially other Azure-native services (no external apps or third-party orchestrators). Any design pattern, architecture recommendations, or examples would be greatly appreciated. Thanks!22Views0likes0Comments