I have a stored procedure that has a DateTime parameter, and I want to execute the pipeline sequence, so it will start first on 'JAN-FEB' and then 'MAR-APR' then 'MEI-JUN'
How can I do that ? without using hard pipeline from the stored procedure?
So for the example like this:
I have 3 stored procedures with different DateTime. And I don't want it run like this.
What can I do to solve my problem ? What function from Azure Data Factory can I use for this case?
Note:
Why I want to sequence this stored procedure is to prevent crash from Azure Synapse. Because in this script will process around 1 billion rows from the source table, and I need to batch this process to prevent from any error.
First you can define an array type variable in ADF. Eg. ['JAN-FEB','MAR-APR','MEI-JUN']
Traverse this array via Foreach activity. Select Sequential, this will sequentially cycle the internal activities. Add dynamic content, select your declared variable name.
Inside Foreach activity, we can use a stored procedure, click Import will import params in your stored procedure. Then add dynamic content #item().
ADF will execute the stored procedure sequentially.
Related
I'm using Azure Data Factory to copy data from Azure Cosmos DB to Azure Data Lake. My pipeline consists of a copy activity which copies data to the Data lake sink.
This is my query on the source dataset:
select * from c
where c.data.timestamp >= '#{formatDateTime(addminutes(pipeline().TriggerTime, -15), 'yyyy-MM-ddTHH:mm:ssZ' )}'
AND c.data.timestamp < '#{formatDateTime(pipeline().TriggerTime, 'yyyy-MM-ddTHH:mm:ssZ' )}'
I'm getting the data for the last 15 minutes before the trigger time.
Now, if there is no data retrieved by the query then the copy activity generates an empty file and stores it in the data lake. I want to prevent that. Is there any way I can achieve this?
You could use lookup activity and then use an if activity to decide whether you need to run the copy activity.
In the lookup activity, you could set firstRowOnly as true since you only want to check whether there are data.
This is an older thread but someone might have a more elegant way to handle the issue above that ADF produces a file even there are 0 records. Here are my concerns with the Lookup approach or having a post-process clean up the empty file.
It's inefficient to query database twice just to check if there are rows the first time.
Using the [IF Condition] componenet is not possible if you are already inside an [if component] or [case] component of ADF. (This is an ADF constraint/shortcoming also).
Cleaning up the empty file is also inefficient, and not an option if you are triggering off the event of the file being created since it causes a false-positive as it is written before you can clean it up.
I tried the following and it is working: I'm checking if the lookup entry returns more than 0 rows.
We are creating Azure Data Factory pipeline using .net API. Here we are providing input data source using sqlReaderQuery. By this mean, this query can use multiple table.
So problem is we can't extract any single table from this query and give tableName as typeProperty in Dataset as shown below:
"typeProperties": {
"tableName": "?"
}
While creating dataset it throws exception as tableName is mandatory. We don't want to provide tableName in this case? Is there any alternative of doing the same?
We are also providing structure in dataset.
Unfortunately you cant do that natively. You need to deploy a Dataset for each table. Azure Data Factory produce slices for every activity ahead of execution time. Without knowing the table name, Data Factory would fail when producing these input slices.
If you want to read from multiple tables, then use a stored procedure as the input to the data set. Do your joins and input shaping in the stored procedure.
You could also get around this by building a dynamic custom activity that operates, say, at the database level. When doing this you would use a dummy input dataset and a generic output data set and control most of the process yourself.
It is a bit of a nuisance this property being mandatory, particularly if you have provided a ...ReaderQuery. For Oracle copies I have used sys.dual as the table name, this is a sort of built-in dummy table in Oracle. In SQL Server you could use one of the system views or set up a dummy table.
Not really sure this is an explicit question or just a query for input. I'm looking at Azure Data Factory to implement a data migration operation. What I'm trying to do is the following:
I have a No SQL DB with two collections. These collections are associated via a common property.
I have a MS SQL Server DB which has data that is related to the data within the No SQL DB Collections via an attribute/column.
One of the NoSQL DB collections will be updated on a regular basis, the other one on a not so often basis.
What I want to do is be able to prepare a Data Factory pipline that will grab the data from all 3 DB locations combine them based on the common attributes, which will result in a new dataset. Then from this dataset push the data wihin the dataset to another SQL Server DB.
I'm a bit unclear on how this is to be done within the data factory. There is a copy activity, but only works on a single dataset input so I can't use that directly. I see that there is a concept of data transformation activities that look like they are specific to massaging input datasets to produce new datasets, but I'm not clear on what ones would be relevant to the activity I am wanting to do.
I did find that there is a special activity called a Custom Activity that is in effect a user defined definition that can be developed to do whatever you want. This looks the closest to being able to do what I need, but I'm not sure if this is the most optimal solution.
On top of that I am also unclear about how the merging of the 3 data sources would work if the need to join data from the 3 different sources is required but do not know how you would do this if the datasets are just snapshots of the originating source data, leading me to think that the possibility of missing data occurring. I'm not sure if a concept of publishing some of the data someplace someplace would be required, but seems like it would in effect be maintaining two stores for the same data.
Any input on this would be helpful.
There are a lot of things you are trying to do.
I don't know if you have experience with SSIS but what you are trying to do is fairly common for either of these integration tools.
Your ADF diagram should look something like:
1. You define your 3 Data Sources as ADF Datasets on top of a
corresponding Linked service
2. Then you build a pipeline that brings information from SQL Server into a
temporary Data Source (Azure Table for example)
3. Next you need to build 2 pipelines that will each take one of your NoSQL
Dataset and run a function to update the temporary Data Source which is the ouput
4. Finally you can build a pipeline that will bring all your data from the
temporary Data Source into your other SQL Server
Steps 2 and 3 could be switched depending on which source is the master.
ADF can run multiple tasks one after another or concurrently. Simply break down the task in logical jobs and you should have no problem coming up with a solution.
I'm using U-SQL with a table in Azure Data Lake Analytics. Is there any way to pass a list of partition keys generated in a C# program to the U-SQL script then have the script return all the elements in those partitions?
Do you want to run the C# code on your dev box and pass values to a U-SQL script or run C# code inside your U-SQL Script? Your description is not clear. Based on your question title, I will answer your first question.
Passing values as parameters from a C# program: The ADLA SDK (unlike Azure Data Factory) does not yet provide a parameter model for U-SQL scripts (please file a request at http://aka.ms/adlfeedback, although I know it is on our backlog already, having external customer demand helps in prioritization).
However it is fairly easy to add your parameter values by prepending DECLARE statements like the following in the beginning of the script and have the script refer to them as variables.
DECLARE #param = new SqlArray<int>( 1, 2, 3, 4); // 1,2,3,4 were calculated in your C# code (I assume you have int partition keys).
Then you should be able to use the array in a predicate (e.g., #param.Contains(partition_col)). That will not (yet, we have a workitem for it) trigger partition elimination though.
If you want partition elimination, you will have to have a fixed set of parameter values and use them in an IN clause. E.g., you want to check up to 3 months, you would write the query predicate as:
WHERE partition_col IN (#p1, #p2, #p3);
And you prepend definitions for #p1, #p2 and #p3, possibly duplicating values for the parameters you do not need.
Can i retrieve data from multiple data sources to Azure SQL DataWarehouse at the same time using single pipeline?
SQL DW can certainly load multiple tables concurrently using external (aka PolyBase) tables, bcp, or insert statements. As hirokibutterfield asks, are you referring to a specific loading tool like Azure Data Factory?
Yes you can, but there you have to mention a copy activity for each of the data source being copied to the azure data warehous.
Yes you can, and depending on the extent of transformation required, there would be 2 ways to do this. Regardless of the method, the data source does not matter to ADF since your data movement happens via the copy activity which looks at the dataset and takes care of firing the query on the related datasource.
Method 1:
If all your transformation for a table can be done in a SELECT query on the source systems, you can have a set of copy activities specifying SELECT statements. This is the simple approach
Method 2:
If your transformation requires complex integration logic, first use copy activities to copy over the raw data from the source systems to staging tables in the SQLDW instance (Step 1). Then use a set of stored procedures to do the transformations (Step 2).
The ADF datasets which are the output from Step1 will be input datasets to Step 2 in order to maintain consistency.