Category: ETL / ELT

Meagan Longoria has a list:

If you’ve been using Azure Data Factory for a while, you might have hit some limitations that don’t exist in tools like SSIS or Databricks. Knowing these limitations up front can help you design better pipelines, so I’m listing a few here of which you’ll want to be aware.

1. You cannot nest For Each activities.
Within a pipeline, you cannot place a For Each activity inside of another For Each activity. If you need to iterate through two datasets you have two main options. You can combine the two datasets before you iterate over them. Or you can use a parent/child pipeline design where you move the inner For Each activity into the child pipeline. Fun fact: currently the Data Factory UI won’t stop you from nesting For Each activities. You won’t find out until you try to execute the pipeline.

Click through for several other limitations and workarounds.

We have proof that Meagan Longoria is a consultant:

Question: When an activity in a Data Factory pipeline fails, does the entire pipeline fail?
Answer: It depends

Read on to understand the circumstances of when a pipeline activity failure will doom an entire pipeline run and when recovery might be possible.

Rayis Imayev wants to pass a JSON array from one Azure Data Factory pipeline to another:

This next post came out of an error message during my attempt to pass a hard-coded array value between pipelines. Strangely, this use-case worked well in the pipeline that was already deployed in ADF, however, I was getting an error message while trying to test and execute this very same pipeline in a Debug mode.

Click through for the explanation.

Rayis Imayev is at a crossroads:

I was raised listening and reading fairy tales where the main character would reach a crossroad with a large stone that had some directions written on it – turn right and you will lose your horse, turn left and you will lose your life, walk straight and you will find your happiness. 

Also, growing up in a small Ukrainian industrial city, closely situated to a railroad hub, I was always fascinated to see many colorful rail traffic lights, trying to imagine where a myriad of rail tracks would lead trains on them.

Similarly, Azure Data Factory (ADF) provides several ways, to control/direct/filter your pipeline workflows; it’s all conditioned and constrained to the boundaries of my “crossroad stone” writings.

As one of my intellectual heroes is purported to have said, if you see a fork in the road, take it.

Kamil Nowinski contrasts two methods for deploying Azure Data Factory pipelines:

Turned out that two-third of people use Microsoft’s deployment way, according to their answers on that poll (including few people who publish the code manually). In 1/3 cases people prefer to deploy directly from code. You may ask: what’re the differences? What characterizes both methods? Which one is better?
Before I start answering these questions, let me present both methods of publishing.

Read on to learn more about these approaches.

Rayis Imayev has some kind words about small niceties in Azure Data Factory:

Recently Microsoft team conducted a brief year-end survey about a “one thing” that Azure Data Factory (ADF) “made your day in 2020” – https://twitter.com/weehyong/status/1343709921104183296. There were different responses from the global parameters support to the limit increase of ADF instances per subscription.

I personally like the little things that are not easily detected on a surface, but with a deeper immersion into a data pipeline development, your level of gratefulness increases even more.

Click through for a few examples.