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
Related
What are possible solutions to do per-request or on schedule 1-way sync of one SQL Server database to the other in Azure?
Both DBs are configured to allow access only via private endpoints.
I've just started exploring options, appreciate expert's opinion on the question.
1-way replication, incrementally data sync and on schedule configuration -- Azure Data Factory is the most suitable service to achieve the following requirements.
Using ADF, you can incrementally load data from multiple tables in a SQL Server Database to one or more databases in another or same SQL Server by creating pipeline using Copy activity. You can also schedule the pipeline trigger based on your requirement.
This official tutorial from Microsoft on Incrementally load data from multiple tables in SQL Server to a database in Azure SQL Database using the Azure portal will help you to create the ADF environment using Linked Service, Datasets, and copy Activity to accomplish your requirement (skip setting Self hosted Integration Runtime as it is required when one of your database is in on-premises).
Once your pipeline has been created, you can schedule it by creating New Trigger. Follow Create a trigger that runs a pipeline on a schedule to create new trigger.
I have an Azure SQL database with some fact tables. I have an SSAS Tabular Cube on an Azure Analysis Services database running on the same subscription. The cube's source database is the Azure SQL database.
I have an elastic job which contains steps to calculate the fact tables in Azure SQL. It does this daily. I would like to have an additional step that would invoke the SSAS Tabular Cube to process with the latest information.
In an on-premise version - I could use SQL Agent to call a powershell script. Azure SQL does not have an agent - only elastic jobs. So it needs to be something that I can call using a T-SQL script. i.e. the T-SQL could be a script that calls a powershell script but I'm not sure how that would work given that the script would need to be saved somewhere and there is nowhere to store scripts in Azure SQL.
Does anyone know if I can invoke the tabular cube processing command from Azure SQL using a T-SQL script? Or if that isn't possible, would I be able to schedule the Azure SSAS Cube to process at a certain time every day? Or is there some other Azure method I could use?
NOTE: Please do not suggest I switch to a virtual machine or managed instance - we need to use Azure SQL. I am willing to use other Azure technology to achieve the same result but I can't change the source database from Azure SQL.
Any and all help appreciated.
No. Elastic Database Jobs just run TSQL. Use Azure Automation instead. You can schedule the Azure Automation job, and to coordinate the Elastic Database Job and the AAS processing you can kick-off an Elastic Database Job by calling a stored procedure.
Or manage the whole process in an Azure Data Factory Pipeline.
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.
I have a copy data pipeline in the Azure Data Factory. I need to deploy the same Data Factory instance in multiple environments like DEV, QA, PROD using Release Pipeline.
The pipeline transfer data from Customer Storage Account (Blob Container) to Centralized Data Lake. So, we can say - its a Many to One flow. (Many customers > One Data Lake)
Now, suppose I am in DEV environment & I have 1 demo customer there. I have defined an ADF pipeline for Copy Data. But in prod environment, the number of customers will grow. So, I don't want to create multiple copies of the same pipeline in production Data Factory.
I am looking out for a solution so that I can keep one copy pipeline in Data Factory and deploy/promote the same Data Factory from one environment to the other environment. And this should work even if the number of customers is varying from one to another.
I am also doing CI/CD in Azure Data Factory using Git integration with Azure Repos.
You will have to create additional linked services and datasets which do not exist in a non-production environment to ensure any new "customer" storage account is mapped to the pipeline instance.
With CI/CD routines, you can deliver this in an incremental manner i.e. parameterize you release pipeline with variable groups and update the data factory instance with newer pipelines with new datasets/linked services.
I have some T-sql scripts which generate some data and we manually update them into the excel spreedsheet, we need a way to push this into azure sql database, from a job so that we can access them from there and remove the manual process of uploading the information to the azure sql database every time. What is the best way to do this?
I assume you are trying to move data from an on prem server to Azure. The simplest method may be Azure Data Sync.
You could load your data from your queries into an on prem table which syncs to Azure.
On all your SQL Server instances, you can create a Linked Server to one Azure SQL Database. Once the linked server is created you can directly insert on Azure SQL Database from your on-premises SQL Server instances.
Here is how you create the Linked Server.
Below image shows how you insert data on Azure SQL Database using the linked server.
For detailed steps, you can visit this tutorial.
I think you can think about Azure Data Factory.
Azure Data Factory Copy Active can help you use T-sql scripts to move data to another Azure SQL database.
For more details, please the Azure tutorial:Copy multiple tables in bulk by using Azure Data Factory.
When the pipeline created, you can trigger and monitor the pipeline runs.
Trigger the pipeline on a schedule:
You can create a scheduler trigger to schedule the pipeline to run periodically (hourly, daily, and so on). In this procedure, you create a trigger to run every minute until the end date and time that you specify.
Please see: Trigger the pipeline on a schedule.
This can help you push the data to Azure SQL Database automatically.
Hope this helps.
you can try the SSIS package? which automates the process of data upload data into azure sql database.... i have not used ssis for Azure but to sink data from csv/xls/xlsx into ms sql server database,,I refered this article which can be helpful in anyway