Azure Data Factory v2 and its available components in Data Flows

Azure Data Factory v2 and its available components in Data Flows

Many of you (including me) wonder about it.
Namely: Is it possible to move my ETL process from SSIS to ADF? How can I reflect current SSIS Data Flow business logic in Azure Data Factory? And turned out that new feature in ADF: Data Flow – comes with help. Furthermore, such solution will be scalable as Azure Databricks works under the hood. Don’t worry – you don’t have to know Databricks and extra language (Scala, Python) at all. How does it possible? Carry on reading.

In this post’s section, I would like to show you what kind of actions you can do and what is their equivalent in SQL and SSIS.

 

New world: Data Flow in Azure Data Factory

The big benefit here is that you will not write any line of code. You can design whole business logic from the scratch using Data Flow UX and appropriate code in Scala will be prepared, compile and execute in Azure Databricks behind the scenes. So that you can focus on business logic and data transformations like data cleaning, aggregation, data preparation and build code-free dataflow pipelines.
Additionally, the process would be automatically scale-out if you allow for that.

ADF Data Flow vs SSIS vs T-SQL

The main purpose of this post is to bring capabilities of (ADF) Data Flow closer and compare it to its counterparts from SSIS and relevant code of T-SQL.
Why? Because it’s far easier to understand something new by comparison to something that we know very well.
Furthermore, tables and icons talks to us much more, hence it is easy to acquire such new knowledge.
Having those fundamentals, you can re-design the current ETL process in Azure Data Factory when having a clear image of mapping components between SSIS and ADFDF. To fulfil the picture out, I have added a column that shows T-SQL code that does the same or similar things in SQL.
So, no matter which technology your current process uses, either Stored Procedures in SQL or SSIS, you are able to sit down and recreate that process to uncover new opportunities.

Components

Operation / Activity Description SSIS equivalent SQL Server equivalent

New branch
Create a new flow branch with the same data
Multicast (+icon)
SELECT INTO 
SELECT OUTPUT

Join
Join data from two streams based on a condition
Merge join
INNER/LEFT/RIGHT JOIN,
CROSS/FULL OUTER JOIN

Conditional Split
Route data into different streams based on conditions
Conditional Split
SELECT INTO WHERE condition1
SELECT INTO WHERE condition2
CASE ... WHEN

Union
Collect data from multiple streams
Union All
SELECT col1a UNION (ALL) 
SELECT col1b

Lookup
Lookup additional data from another stream
Lookup
Subselect, function,
LEFT/RIGHT JOIN

Derived Column
Compute new columns based on the existing once
Derived Column
SELECT Column1 * 1.09 as NewColumn

Aggregate
Calculate aggregation on the stream
Aggregate
SELECT Year(DateOfBirth) as YearOnly,
MIN(), MAX(), AVG()
GROUP BY Year(DateOfBirth)

Surrogate Key
Add a surrogate key column to output stream from a specific value
Script Component
SELECT ROW_NUMBER() 
   OVER(ORDER BY name ASC) AS Row#,
name
FROM sys.databases

Incremental Primary Key
(with limited capabilities)


Exists
Check the existence of data in another stream
Lookup / Merge Join
SELECT * FROM Table
WHERE EXISTS(SELECT ...)

Select
Choose columns to flow to the next stream OUTPUT in components,
mapping columns
SELECT Column1, Column4 
FROM Table

Filter
Filter rows in the stream based on a condition
Conditional Split
SELECT * FROM Table 
WHERE [Column] LIKE '%pattern%'

Sort
Order data in the stream based on column(s)
Sort
SELECT * FROM Table 
ORDER BY [Column] ASC

Extend
Use any custom logic from an external library
Script Component
SQL CLR

Source
Source for your data flow.
Obligatory first element of every Data Flow in ADF.

OLE DB Source and more …
SELECT * FROM SourceTable

Sink
Destination for your data flow
OLE DB Destination and more…
INSERT INTO TargetTable

Update 04/01/2022
Do you think the above table is useful? Download the updated version (PDF) as a two-page cheat sheet.
More interesting materials like this can be found in the following free course: Cheat sheets for Data Engineers

Summary

This new feature has huge capabilities. I’m very excited being had opportunity to use it more.
An automatically scalable process, like this, might be very efficient with Big Data processing. Hence, it’s worth to start designing new processes with Azure Data Factory or even migrating existing processes when your enterprise suffers from performance degradation due to the amount of processing data.
Please be aware that among Microsoft solutions is another Data Flow – exists in Power BI. Do not confuse them.

In next posts of this series, I will be explaining all activities from ADF Data Flow a bit deeper.
Let me know your thoughts or leave a comment once you have any questions.
Thanks for reading!

Useful links

ADF Data Flow’s documentation
ADF Data Flow’s videos
Follow this tag on the blog: ADFDF

Previous Last week reading (2018-12-02)
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Kamil Nowinski
Kamil Nowinski 190 posts

Blogger, speaker. Data Platform MVP, MCSE. Senior Data Engineer & data geek. Member of Data Community Poland, co-organizer of SQLDay, Happy husband & father.

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  1. TC
    February 13, 10:38 Reply

    Hi, any idea the release date? It’s been in preview for a while now

    In ADF dataflow, do you know if the source is able to read directly from gzip files? (this is possible with the Copy Activity source, so hoping this will be available in dataflow)

    Also will the dataflow source read all files in blob storage without having to create any looping logic, again like the copy activity

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