Book Review: Azure Data Factory Cookbook - 2nd Edition
The people at PackT recently sent me a book to review, and I was happy to do so as it was on a topic that’s dear to my heart: Azure Data Factory. The book was Azure Data Factory Cookbook and it’s the second edition of the book. The authors are Dmitry Foshin, Tonya Chernyshova, Dmitry Anoshin, and Xenia Ireton.
PackT
In the past, I wasn’t keen on PackT books. When they first appeared, they tended to be low cost books from unknown authors, many of whom struggled with writing in English, and pretty poor editing of the content.
I’m really pleased to see how this has changed in recent times. The authors of most of their books are now people who are knowledgeable about the topics, write well in English, and the editing has improved out of sight.
Not sure how that was achieved, but am really pleased to see that it has.
In terms of production, there are only two comments I’d make:
- I find the font style, size, etc. still harder to read than an equivalent book from, say, Apress. I find the books harder to read for long periods.
- I know it’s hard to ask for colour, but I have to agree with one of the reviewers on Amazon who commented that the lack of colour make some of the pictures hard to read.
Other than that, the book was large, solid, and well-presented.
Content Style
I like books that are cookbook style. I used to think the same about books on topics like MDX and DAX. There is a place for books that teach the theory but often what people need once they get past the basics, are books that just say “if you’re trying to achieve this, do this”, and have a big list of recipes.
This book does that. Most of the topics are covered with walkthroughs that step you through how to do a task. I liked that approach.
Topic Coverage
This book covers a lot of topics. Given the title of the book was about ADF, I was really suprised to see the breadth of topics that were covered. The subtitle is A data engineer’s guide to building and managing ETL and ELT pipelines with data integration. And that gives a clue to the fact that the coverage is much, much broader than ADF.
I was surprised to see so much coverage of pipelines in other places like Synapse Analytics, Fabric, etc. but more surprised to see coverage of HDInsight and big data concepts. I can’t remember the last time I saw anyone using HDInsight. I always thought it was seriously over-hyped while it was being promoted, and still think the same way.
It made more sense to see a bunch of coverage of Databricks, delta tables and integrating ADF with Azure Machine Learning, Azure Logic Apps, Azure Functions and more. They are relatively common areas of integration for ADF, along with migrating on-premises SSIS packages to ADF.
Note: in general, I don’t like migrating SSIS packages to ADF in any way except rewriting them. Most of my customers never complain about the cost of using ADF. The only ones I hear complaining are people who use either the SSIS runtime for ADF or those using dataflows in ADF. (I don’t like using those either)
Summary
The book is substantial, well written, and comprehensive.
What I really would have liked is more ADF content. I don’t want the book to be larger, but for a book with this title, I’d prefer more depth on how to do things in ADF and less on other related but ancilliary topics.
7 out of 10
2024-04-20