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When we face with engineering problems, it’s too easy to fall into the trap thinking it should be solved with a technical solution. Seasoned engineers think differently, because they realize that most of the time, it’s “people” or “workflow” problems. Let me provide a few examples. Management wants analysts to use Jupyter notebook to reduce time required to create a routine report Background: Most analysts are comfortable using Microsoft Excel to work with data, some can also use SQL, but it’s rare for analysts to be familiar with Python....

Every business generates data, some very little, some do generate ginormous amount of data. If you are familiar with the basic web application architecture, there are data, application and web tier. But it doesn’t end there, because the data generated has to be analyzed for reports. A lot of organizations have analysts working on production database directly. This works fine and well, until the data they are working with is very large to the point that a single query can take half a day to process!...

Background Think of websites you visit each day. Most likely they are hosted on a cloud provider such as AWS, GCP, Azure. The good news is it’s very easy to create a simple deployment with a virtual machine, but for scalable and high-availability workloads, usual recommendations is to use a container-based runtime such as AWS ECS/EKS, GCP Cloud Run/GKE. These services also require more configurations than a simple VM deployment....

Background For data processing tasks, there are different ways you can go about it: using SQL to leverage a database engine to perform data transformation dataframe-based frameworks such as pandas, ray, dask, polars big data processing frameworks such as spark Check out this article for more info on polars vs spark benchmark. The problem At larger data scale, other solutions (except spark) can work, but with a lot of vertical scaling, and this can get very expensive....

Note: if you’ve seen the list elsewhere, it was probably me. I first posted this list on Data Engineering Discord and Data Engineer Cafe. Books Data fundamentals (good entrypoint) Fundamentals of Data Engineering - Joe Reis & Matt Housley Seven Databases in Seven Weeks - Luc Perkins & Eric Redmond & Jim Wilson Designing Data-Intensive Applications - Martin Kleppmann The Data Warehouse Toolkit - Ralph Kimball & Margy Ross Data Science for Business - Foster Provost & Tom Fawcett Practical Statistics for Data Scientists - Peter Gedeck & Peter Bruce & Andrew Bruce Software engineering Python Crash Course - Eric Matthes The Pragmatic Programmer - Andrew Hunt & David Thomas Platform Terraform: Up & Running - Yevgeniy Brikman Management Team Topologies - Matthew Skelton & Manuel Pais Radical Candor - Kim Scott Data Teams - Jesse Anderson Practical DataOps - Harvinder Atwal Resources https://brendanthompson....