I have about 120 pipeline with almost 400 activities all together and I would like to log them in our datalake storage system so we can report on the performance using powerBI. I came across How to get output parameter from Executed Pipeline in ADF? but it seems to me to work with a single pipeline, but I am wondering if I could get the whole pipeline in my ADF in one single call and the activities also.
Thnaks
Assuming the source in these pipelines varies which makes it difficult to apply the logic for monitoring.
One way is to store the logs individually for each pipeline by running some queries with pipeline parameters. Refer Option 2 in this tutorial.
Although, the best feasible and appropriate way to monitor ADF pipelines and activities is to use the Azure Data Factory Analytics.
This solution provides you a summary of overall health of your Data Factory, with options to drill into details and to troubleshoot unexpected behavior patterns. With rich, out of the box views you can get insights into key processing including:
At a glance summary of data factory pipeline, activity and trigger
runs
Ability to drill into data factory activity runs by type
Summary of data factory top pipeline, activity errors
Go to Azure Marketplace, choose Analytics filter, and search for Azure Data Factory Analytics (Preview)
Select Create and then create or select the Log Analytics Workspace.
Installing this solution creates a default set of views inside the workbooks section of the chosen Log Analytics workspace. As a result, the following metrics become enabled:
ADF Runs - 1) Pipeline Runs by Data Factory
ADF Runs - 2) Activity Runs by Data Factory
ADF Runs - 3) Trigger Runs by Data Factory
ADF Errors - 1) Top 10 Pipeline Errors by Data Factory
ADF Errors - 2) Top 10 Activity Runs by Data Factory
ADF Errors - 3) Top 10 Trigger Errors by Data Factory
ADF Statistics - 1) Activity Runs by Type
ADF Statistics - 2) Trigger Runs by Type
ADF Statistics - 3) Max Pipeline Runs Duration
You can visualize the preceding metrics, look at the queries behind these metrics, edit the queries, create alerts, and take other actions.
Related
I wanted to comment on this post: I want to trigger Azure datafactory pipeline whenever there is a change in Azure SQL database
but I don't have enough reputation...
The solution that Skin comes up with (SQL DB trigger events) looks exactly like what I'm after but I can't find any further documentation on it - in fact the only references I've found say that this functionality doesn't exist?
Can anyone point me to anything online - or a book - that could help?
Cheers
AFAIK, In ADF there are no such triggers for SQL changes. ADF supports only Schedule,Tumbling window and Storage event and custom event triggers.
But You can use the logic app triggers (item created and item modified) to triggers ADF pipeline.
For this we the SQL table should have an auto increment column.
Here is a demo I have built for item created trigger:
First search for SQL in logic app and click on item created trigger. Then create a connection with your details.
After that give your table details.
After trigger create Action for ADF pipeline run.
Make sure you publish your ADF pipeline to reflect its name in the above drop down. You can assign SQL columns to ADF pipeline parameter like above.
You can set the trigger for one every one minute or one hour as per your requirement. If any new item inserted into SQL table in that period of time it will trigger ADF pipeline.
I have inserted a new record like this insert into practice values('Six');
Flow Suceeded:
My ADF pipeline:
Pipeline Triggered:
Pipeline successful and you can see variable value:
You can use another flow for item modified trigger as same above and trigger ADF pipeline from that as well.
with the new latest feature A new feature that allows invocation of any REST endpoints is now in public preview in Azure SQL databases
, I guess it is possible :
https://devblogs.microsoft.com/azure-sql/azure-sql-database-external-rest-endpoints-integration-public-preview/
Blog:
https://datasharkx.wordpress.com/2022/12/02/event-trigger-azure-data-factory-synapse-pipeline-via-azure-sql-database/
I am working on a migration project where I have a few SQL Server Integration Service projects that will be moved to Azure Data Factory. While I go through this we have a few jobs scheduled via SQL Server Agent which has multiple steps. If were to replicate the same using Azure Data Factory triggers is there a way to group multiple pipelines together and sequence the execution accordingly like we have multiple job steps in SQL Server Agents.
For instance:
Load all of the lookup tables
Load all of the staging tables
Load all of the dimension tables
Load Fact table
Please guide in the right direction.
You can use the Execute Pipeline Activity to build a master pipeline that runs your other pipelines. eg
Hi i have a scenario where i have an csv file in azure datalake storage. while running azure pipeline, the parameters from the excel has to be picked up one by one in an iterative manner. Based on each parameter the databricks notebook should be run.
Is there any solution for this - how to iterate through the values in csv file?
If you are in Azure, you should consider Azure Data Factory (ADF) or Azure Synapse Analytics which has pipelines. Both are good for moving data from place to place and data orchestration. For example you could have an ADF pipeline with a Lookup activity which reads your .csv, then calls a For Each activity with a parameterised Databricks notebook inside:
Interestingly, the For Each activity runs in parallel so could deal with multiple lines at once, depending on your Databricks cluster size etc.
You could try and do all this within a single Databricks notebook which I'm sure is possible, but I would say that is a more code heavy approach and you still have questions around the scheduling, doing tasks in parallel, orchestration etc
Usecase
We have an on-premise Hadoop setup and we are using power BI as a BI visualization tool. What we do currently to get data on Powerbi is as follows.
Copy data from on-premise to Azure Blob(Our on-premise schedule does this once the data is ready in Hive)
Data from Azure Blob is then copied to Azure-DataWarehouse/Azure-SQL
Cube refreshes on Azure AAS, AAS pulls data from Azure DataWarehouse/SQL
To do the step2 and step3 we are currently running a web server on Azure and the endpoints are configured to take few parameters like the table name, azure file location, cube information and so on.
Sample http request:
http://azure-web-server-scheduler/copydata?from=blob&to=datawarehouse&fromloc=myblob/data/today.csv&totable=mydb.mytable
Here the web servers extract the values from variables(from, fromloc, to, totable) and them does the copy activity. We did this as we had a lot of tables and all could reuse the same function.
Now we have use cases piling up(retries, control flows, email alerts, monitoring) and we are looking for a cloud alternative to do the scheduling job for us, we would still like to hit an HTTP endpoint like the above one.
One of the alternatives we have checked till now is the Azure Data Factory, where are create pipelines to achieve the steps above and trigger the ADF using http endpoints.
Problems
How can we take parameters from the http post call and make it available as custom variables[1], this is required within the pipeline so that we can still write a function for each step{2, 3} and the function can take these parameters, we don't want to create an ADF for each table.
How can we detect for failure in ADF steps and send email alerts during failures?
What are the other options apart from ADF to do this in Azure?
[1] https://learn.microsoft.com/en-us/azure/data-factory/control-flow-system-variables
You could trigger the copy job from blob to SQL DW via a Get Metadata Acitivity. It can be used in the following scenarios:
- Validate the metadata information of any data
- Trigger a pipeline when data is ready/ available
For eMail notification you can use a Web Activity calling a LogicApp. See the following tuturial how to send an email notification.
Consider a data processing pipeline as follows:
Fetch a large amount of data from a REST API that's hosted somewhere on the internet and persist it to a data store.
Perform some complex data transformations on the persisted data.
Persist the results of the data transformations on a data store.
Aiming to implement such a pipeline in Azure, steps 2 and 3 seem like a good fit for implementation as Azure Data Factory activities.
My questions is - Does it make sense to implement step 1 in an Azure Data Factory activity as well?
Technically it might be possible to code a .Net activity that perform the data download and persistence.
No - do not implement step 1 in an Azure Data Factory activity.
Technically it is possible to run the entire process from ADF but I would argue that the choice is more costly (relatively) than other options available to you because you will pay for each activity in Azure Data Factory.
For instance, what if the rest api does not have any new data to offer when you initiate the (scheduled) activity? You'll pay for that.
You might consider the following as an easy to implement alternative:
1 - Create a .NET console app, publish as a WebJob, schedule to run daily.
2 - The long-running console app can query the rest api, persist data into azure storage / documentdb, push a message into queue which triggers ADF steps 2/3 to run against the saved data.
I have done exactly that using .Net Activity. I had a need to fetch data from Salesforce api. This has been working well for my needs. Here is a post I wrote up about creating a .net activity and storing the data in azure data lake.
As in Newport99's answer yes you will incur costs for that activity but I am not sure how cost effect it would be to be running a separate web app to host a web job and also run the Azure Data Factory pipeline. When I was originally designing a solution the WebJob was my first choice but in the end I prefer to have the whole solution utilizing one azure service instead of multiple.
Hope that helps.
There have been a lot of improvements to ADF in the years since this question was posted, including a REST connector.
Here's the approach recommended by ADF at this time...
Copy data from a REST endpoint by using Azure Data Factory