Azure external tables between two azure sql databases on the same server don't perform well. This is known. I've been able to improve performance by defining a view from which the external table is defined. This works if the view can limit the data set returned. But this partial solution isn't enough. I'd love a way to at least nightly, move all the data that has been inserted or updated from the full set of tables from the one database (dbo schema) to the second database (pushing into the altdbo schema). I think Azure data factory will let me do this, but I haven't figured out how. Any thoughts / guidance? The copy option doesn't copy over table schemas or updates
Data Factory Mapping Data Flow can help you achieve that.
Using the AlterRow active and select an uptade method in Sink:
This can help you copy the new inserted or updated data to the another Azure SQL database based on the Key Column.
Alter Row: Use the Alter Row transformation to set insert, delete, update, and
upsert policies on rows.
Update method: Determines what operations are allowed on your
database destination. The default is to only allow inserts. To
update, upsert, or delete rows, an alter-row transformation is
required to tag rows for those actions. For updates, upserts and
deletes, a key column or columns must be set to determine which row
to alter.
Hope this helps.
Related
I am setting up a pipeline in data factory where the first part of the pipeline needs some pre-processing cleaning. I currently have a script set up to query these rows that need to be deleted, and export these results into a csv.
What I am looking for is essentially the opposite of an upsert copy activity. I would like the procedure to delete the rows in my table based on a matching row.
Apologies in advanced if this is an easy solution, I am fairly new to data factory and just need help looking in the right direction.
Assuming the source from which you are initially getting the rows is different from the sink
There are multiple ways to achieve it.
in case if the number of rows is less, we can leverage script activity or lookup activity to delete the records from the destination table
in case of larger dataset, limitations of lookup activity, you can copy the data into a staging table with in destination and leverage a script activity to delete the matching rows
in case if your org supports usage of dataflows, you can use that to achieve it
I have 100-150 Azure databases with same table schema. There are 300-400 tables in each database. Separate reports are enabled on all these databases.
Now I want to merge these database into a centralized database and generate some different Power BI reports from this centralized database.
The approach I am thinking is -
There will be Master table on target database which will have
DatabaseID and Name.
All the tables on target database will have the composite primary key
created with the Source Primary key and Database ID.
There will be multiple (30-35) instances of Azure data factory
pipeline and each instance will be responsible to merge data from
10-15 databases.
These ADF pipelines will be scheduled to run weekly.
Can anyone please guide me that the above approach will be feasible in this scenario? Or there could any other option we can go for.
Thanks in Advance.
You trying to create a Data Warehouse.
I hope you will never archive to merge 150 Azure SQL Databases because is soon as you try to query that beefy archive what you will see is this:
This because Power BI, as any other tool, comes with limitations:
Limitation of Distinct values in a column: there is a 1,999,999,997 limit on the number of distinct values that can be stored in a column.
Row limitation: If the query sent to the data source returns more than one million rows, you see an error and the query fails.
Column limitation: The maximum number of columns allowed in a dataset, across all tables in the dataset, is 16,000 columns.
A data warehouse is not just the merge of ALL of your data. You need to clean them and import only the most useful ones.
So the approach you are proposing is overall OK, but just import what you need.
I am looking to use Azure Data Factory to import a number of users from a third party source CSV file into the D365 user table/entity. This is usually straight forward but on this occasion I have a complication.
The D365 users table/entity is already populated. The source CSV user file will have a mixture of users that are already in the D365 table/entity and others that are not.
What I would like to do is ensure the users in my source file that are already in the D365 table are not copied over as this would create duplicates.
Source CSV FILE
Existing D365 User table (subset of fields just to illustrate)
Updated D365 table with two new record added from source csv
From what I can see there are two possible solutions in Azure Data Factory
Configure the D365 sink to do it. e.g configure th sink in order to ignore records that match on a certain column?
Is it possible to configure the sink in some way to accomplish this?
Pull in the D365 table/entity as a source and use it to filter my source CSV to remove user records that already exist in D365 perhaps by using a common field such as fullname to identify such records. This would ensure I only try to import new users.
I have had a look into both methods but have been struggling to find a way to implement them.
I'd like to think the scenario I have outlined above is not uncommon and there are tried and tested methods to filter out records from a source CSV that already exists in the target D365 table?
I'd apprecate any help/suggestion to help me achieve this
You can use any one of these 2 approaches.
Use Azure data flow and Upsert the data to sink using Upsert as your writeBehavior in your dynamics sink transformation. You can refer to this SO link for information to use the Upsert method in the Azure data factory.
Pull CSV data as source1 and D365 table data as source2 and connect both sources to join transformation with left outer join. Then you can use filter transformation to filter out the NULL records of source2 (or right table). The output of filter transformation will be only new records which can be directly passed to D365 sink transformation. You can refer to this SO thread to similar process.
When we do data extract as yours from Synapse into Azure, upsert is not working correctly, many times go into a dead loop
What we do:
Create a temp table in the target
select source table data and extract it into the target temp table
run stored procedure to update, insert and delete target real table based on the temp table
Here is the stored procedure query, hope it can help you:
UPDATE t
SET
t.bcfsa_teamid = syn.bcfsa_teamid
,t.bcfsa_account = syn.bcfsa_account
,t.bcfsa_name = syn.bcfsa_name
,t.bcfsa_contactteamownerid = syn.bcfsa_contactteamownerid
FROM website.bcfsa_team t
INNER JOIN syn.bcfsa_team syn on t.id = syn.id;
INSERT website.bcfsa_team (id,bcfsa_teamid,bcfsa_account,bcfsa_name,bcfsa_contactteamownerid)
SELECT id,bcfsa_teamid,bcfsa_account,bcfsa_name,bcfsa_contactteamownerid
FROM syn.bcfsa_team
EXCEPT
SELECT id,bcfsa_teamid,bcfsa_account,bcfsa_name,bcfsa_contactteamownerid
FROM website.bcfsa_team;
DELETE FROM website.bcfsa_team
WHERE Id NOT in (SELECT id FROM syn.bcfsa_team);
TRUNCATE TABLE syn.bcfsa_team;
I wanted to achieve an incremental load from oracle to Azure SQL data warehouse using azure data factory. The Issue that I am facing is I don't have any date column or any key column to perform Incremental load Is there any other way to achieve this.
You will either have to:
A. Identify a field in each table you want to use to determine if the row has changed
B. Implement some kind of change capture feature on the source data
Those are really the only the only two ways to limit the amount of data you pull from the source.
It wouldn't be very efficient, but if you are just trying not to update rows that haven't changed in your destination, you can hash your source values and hash the values in the destination, and only insert/update rows where the hashes don't match. Here's an example of how this works in T-SQL.
There is a section of the Data Factory documentation dedicated to incrementally loading data. Please check it out if you haven't.
I would like to set up a pipeline to sync data from Data Warehouse to no sql CosmosDB. Copy tool works fine for one to one table relations but for one to many obviously, I will have duplicates of objects in my NoSQL DB. What is the best way to solve this issue and have an array of one to many items instead of duplicating rows?
Thanks in advance
In your case, I don’t think copy activity can achieve that. Copy activity just copy data from one table to another by appending new documents or do upsert based on cosmos dB ID. Maybe you could write your own code to do the merging and then use ADF custom activity to invoke your code.
https://learn.microsoft.com/en-us/azure/data-factory/transform-data-using-dotnet-custom-activity