Category: ETL / ELT

Rayis Imayev shows how we can find values in a group using Azure Data Factory:

However only this use-case for the OR function with 2 condition could be possible:or(equals(variables(‘var1’), ‘A’), equals(variables(‘var1’), ‘B’)) – limit of two conditions

But what if we have an ability to check if a particular element variable/parameter/other ADF object value belongs to a range of values (array of value), similarly to what we can do with the IN operator in SQL language, this would definitely solve our problem and remove the limitation of logical conditions to check.

Click through for the answer.

Rayis Imayev takes us through the Union Transformation in Mapping Data Flows:

Recently I had a chance to work on a special case of creating a file where the first two rows were static and contained output table metadata (columns names and column types), assuming that further reading process would be able to explicitly identify each column data type and consume new file correctly in a single run.

Read on to see how Rayis solved this with Azure Data Factory.

Rayis Imayev shows how you can call an Azure Function from inside your Azure Data Factory Pipeline:

Creating a data solution with Azure Data Factory (ADF) may look like a straightforward process: you have incoming datasets, business rules of how to connect and change them and a final destination environment to save this transformed data. Very often your data transformation may require more complex business logic that can only be developed externally (scripts, functions, web-services, databricks notebooks, etc.).

In this blog post, I will try to share my experience of using Azure Functions in my Data Factory workflows: my highs and lows of using them, my victories and struggles to make them work.

This includes a description of the options, a demo function, and additional notes for each technique.

Paul Andrew has another update to the ADF metadata-driven processing framework:

With this release of the framework I wanted to take the opportunity to harden the database and add some more integrity (intelligence) to the metadata, things that go beyond the existing database PK/FK constraints. After all, this metadata drives everything that Azure Data Factory does/is about to do – so it needs to be correct. These new integrity checks take two main forms:

1. Establishing a minimum set of criteria within the metadata before the core Data Factory processing starts and creates an execution run.
2. Establishing a logical chain of pipeline dependencies across processing stages. Then providing a set of advisory checks for area’s of conflict and/or improvement.

More details on both are included against the actual stored procedure in the database changes section below.

In addition to database hardening, I’ve added a few other bits to the solution, including a PowerShell script for ADF deployments and a Data Studio Notebook to make the developer experience of implementing this code project a little nicer.

Read on to see what’s in version 1.3. Check it out on GitHub as well.

Koen Verbeeck takes us through switching the current environment in Azure Data Factory:

When working with Azure Data Factory, it’s possible you have multiple ADF environments. For example, you can have one for dev, one for test and one for production. Unfortunately, the functionality to switch between them is a bit hidden in the user interface.

Read on to see how.