Book: Data Pipelines with Apache Airflow
Authors: Bas Harenslak and Julian deRuiter
ISBN: 9781617296901
Azure gets the same treatment AWS got in the previous chapter. Map Airflow components to managed services, survey the provider package, then build the movie-ranking pipeline again. Same goal, different cloud.
Book: Data Pipelines with Apache Airflow
Authors: Bas Harenslak and Julian deRuiter
ISBN: 9781617296901
Chapter 16 is where the cloud chapters get concrete. Harenslak and deRuiter map every Airflow component to an AWS service, then build a working movie-ranking pipeline with S3, Glue, and Athena.
Book: Data Pipelines with Apache Airflow
Authors: Bas Harenslak, Julian deRuiter
ISBN: 9781617296901
Previous: Securing Apache Airflow | Next: Airflow in the Clouds
Book: Data Engineering with AWS: A Comprehensive Guide to Building Robust Data Pipelines
Author: Brian Paul
ISBN: Not listed in the source edition
Book: Data Engineering for Beginners
Author: Chisom Nwokwu
ISBN: 9781394325412
The second half of Chapter 7 answers a question every data team hits eventually: how do you run live dashboards AND historical reports without building two completely separate systems? Then it gets practical with orchestration and a hands-on Airflow lab.
Chapter 6 of Data Engineering for Beginners by Chisom Nwokwu (ISBN 9781394325412) is a big one. It moves beyond traditional databases into the storage systems that power modern analytics. The fictional bakery Dough & Delight runs through every example, which keeps things grounded.
Book: Data Engineering with AWS: A Comprehensive Guide to Building Robust Data Pipelines
Author: Brian Paul
ISBN: Not listed in the source edition
Book: Data Engineering with Python
Author: Paul Crickard
ISBN: 978-1-83921-418-9
Chapters 3 and 4 taught you how to move data in and out of files and databases. Chapter 5 asks the next obvious question: what do you do with the data in the middle? Often the answer is clean it, reshape it, and sometimes bolt on extra context from another source. Crickard uses real Albuquerque e-scooter trip data for all of this, which makes the examples feel grounded instead of toy-like.
Book: Data Engineering with Python
Author: Paul Crickard
ISBN: 978-1-83921-418-9
Chapter 1 of Paul Crickard’s book is the orientation lecture. No installs yet. No code. Just a clear picture of what data engineering is, who does it, and what tools show up again and again.
Book: Data Engineering with Python
Author: Paul Crickard
ISBN: 978-1-83921-418-9
Publisher: Packt (October 2020)
I’m starting a walkthrough of Data Engineering with Python by Paul Crickard. This is not a copy-paste summary. It’s my retelling of each chapter with notes on what stuck, what felt dated, and what still holds up.
This is post 12 in my Data Engineering with AWS retelling series.
In Part 1, we covered the generic data preparation transforms: converting to Parquet, partitioning, PII protection, and data cleansing. Those transforms work on individual datasets and do not need much business context. Now we get to the transforms that actually create business value. The ones that combine multiple datasets, add context, flatten structures, and produce the tables that analysts and dashboards consume.
Chapter 6 is where the book zooms out from “how to design one database” to “where does all this data actually live in a real company.” The answer: it depends on what you are trying to do with it.
Chapter 1 of Data Engineering with Python by Paul Crickard starts with the basics. What is data engineering? What do data engineers actually do? And how is it different from data science?
Chapter 1 starts with a confession most of us in the data world can relate to. Adi Wijaya says he used to think data was clean. Neatly organized, ready to go. Then he actually worked with data in real organizations and realized most of the effort goes into collecting, cleaning, and transforming it. Not the fun machine learning part. The plumbing part.