Forum Discussion
ETL Data flow multiple servers
To be honest, Fabric is new to me as well. And I have more of a Power BI background, so I know Power Query quite well, but I don't have so much real experience about SQL database, stored procedure, data warehouse admin, etc.
I have tested a lot of features in Fabric, but I haven't used them in production (I have only used Power BI in production).
If I understand correctly, you want to build an analytical data store in Fabric (either as a Fabric Lakehouse or Fabric Warehouse).
I guess you have some operational systems which are your ultimate data sources. I think a common pattern is
Data source -> Pipeline (with or without a custom SQL query) -> Staging files or tables (Bronze Lakehouse) -> Notebook (ETL, upsert, etc.) -> Tables (Silver Lakehouse) -> Notebook (ETL, upsert, etc.) -> Tables (Gold Lakehouse)
or
Data source -> Pipeline (with or without a custom SQL query) -> Staging tables (Warehouse) -> Stored procedure (ETL, upsert, etc.) -> Gold Tables (Warehouse)
I think also Lakehouse (Bronze) -> Lakehouse (Silver) -> Warehouse (Gold) is a normal pattern. Here you will probably find a lot of information if you Google "Medallion architecture in Fabric".
But I think this is up to your organization to decide if you want to go all in on Lakehouse, Warehouse or a mix.
Fabric Lakehouse uses Spark language (you can choose between and mix PySpark, SparkSQL, Scala or SparkR).
Fabric Warehouse uses T-SQL language (but there are some limitations, because the underlying storage format is Delta Lake so not all T-SQL commands are available). I suppose you can Google to search for which workarounds people are using to adapt to the language limitations.
A general advice is to use the tools which work well with your current skillset (also bearing in mind which direction you want to go).
Using the Fabric Trial, you can test Fabric and its tools cost-free for a period.
So I don't think using Dataflows Gen2 is a must. Actually, my impression is that it is quite heavy on resource-consumption. My impression is that it is a tool for low-code, UI-based ETL (which I like, coming from a Power BI background).
Hopefully someone with more practical experience than me can guide you further.
I think this is a great blog (serverlesssql.com) by AndyDDC.
He can probably correct any mistakes I made in the text above here 😁
I also like this blog: Fabric: Lakehouse or Data Warehouse? - Sam Debruyn
Although I don't think it's necessary to use both Lakehouse and Data Warehouse. It's just an option.
Hi frithjof_v
Thankyou so much for getting back to me.
So a Synonym is just essentially an alias for a database object. They can change a lot with what I deal with e.g. pointing it to test and dev environments with the same fields.
If e.g. you were joining to the destination table that you are inserting into (snapshot table) to check if the id exists or not, could this be done in power query?
I'm open to using both, but as we have quite an old fashioned current method of bulk stored procedures inserting for our ETL, I didn't know if it was better to change the whole thing to Dataflow Gen2, or still use the whole existing code in SSMS just into Power Query.
I thought the latter defeated the object of migrating our data warehouse but I'm not sure.
Thanks
Liam
In Power Query M, you could join your new data with the current data in your destination table, in order to check if there are matches. (You can do inner joins, anti joins, etc.)
You could also find the max ID from your destination table, and filter your new data to only process rows which have a higher ID, as an example.
But it made me think...
Are your current stored procedures doing an upsert of the data in your destination table?
Dataflows Gen2 only support full overwrite ("flush and fill") or append method. So you either overwrite everything, or you can append new rows. But not upsert.
The upsert operation would need to be done in another tool, like Notebook (Fabric Lakehouse) or Stored procedure (Fabric Warehouse).
What is your data storage?
Is it in the cloud or on-prem?
Are you planning to use a Fabric Warehouse or a Fabric Lakehouse?
Dataflows Gen2 supports the following destinations:
Do you have a sketch or a diagram which outlines what you need to achieve? (from source via ETL to destination)
Thanks frithjof_v
That's good to know re the existing data.
Yes there is a few that do insert & updates at the same time. We currently have an on prem Sql Server, with data coming out of SSMS.
I guess I wasn't sure yet on Warehouse or a Fabric Lakehouse. Do you recomend a way to go with this in the scenario of e.g. 10 Stored procedures that upsert via sql agent twice a day, translating to Fabric?
I'm open to changing a lot of the process to fit in with Fabric but I'm not sure what the best way to go is. Would it be Gen2 > Pipeline > Lakehouse/Warehouse? Is Stored procedure in Warehouse just the same as I'm doing now?
Apologies if any of this is a silly question - Fabric is very new to us.
Thanks again for your help
Liam
To be honest, Fabric is new to me as well. And I have more of a Power BI background, so I know Power Query quite well, but I don't have so much real experience about SQL database, stored procedure, data warehouse admin, etc.
I have tested a lot of features in Fabric, but I haven't used them in production (I have only used Power BI in production).
If I understand correctly, you want to build an analytical data store in Fabric (either as a Fabric Lakehouse or Fabric Warehouse).
I guess you have some operational systems which are your ultimate data sources. I think a common pattern is
Data source -> Pipeline (with or without a custom SQL query) -> Staging files or tables (Bronze Lakehouse) -> Notebook (ETL, upsert, etc.) -> Tables (Silver Lakehouse) -> Notebook (ETL, upsert, etc.) -> Tables (Gold Lakehouse)
or
Data source -> Pipeline (with or without a custom SQL query) -> Staging tables (Warehouse) -> Stored procedure (ETL, upsert, etc.) -> Gold Tables (Warehouse)
I think also Lakehouse (Bronze) -> Lakehouse (Silver) -> Warehouse (Gold) is a normal pattern. Here you will probably find a lot of information if you Google "Medallion architecture in Fabric".
But I think this is up to your organization to decide if you want to go all in on Lakehouse, Warehouse or a mix.
Fabric Lakehouse uses Spark language (you can choose between and mix PySpark, SparkSQL, Scala or SparkR).
Fabric Warehouse uses T-SQL language (but there are some limitations, because the underlying storage format is Delta Lake so not all T-SQL commands are available). I suppose you can Google to search for which workarounds people are using to adapt to the language limitations.
A general advice is to use the tools which work well with your current skillset (also bearing in mind which direction you want to go).
Using the Fabric Trial, you can test Fabric and its tools cost-free for a period.
So I don't think using Dataflows Gen2 is a must. Actually, my impression is that it is quite heavy on resource-consumption. My impression is that it is a tool for low-code, UI-based ETL (which I like, coming from a Power BI background).
Hopefully someone with more practical experience than me can guide you further.
I think this is a great blog (serverlesssql.com) by AndyDDC.
He can probably correct any mistakes I made in the text above here 😁
I also like this blog: Fabric: Lakehouse or Data Warehouse? - Sam Debruyn
Although I don't think it's necessary to use both Lakehouse and Data Warehouse. It's just an option.