{ "version": "https:\/\/jsonfeed.org\/version\/1.1", "title": "Bookshelf", "_rss_description": "A blog by George Mishurovsky — a senior software engineer with a medical degree. Drawing from both engineering and scientific thinking, he explores software, architecture, design, psychology, and product thinking.", "_rss_language": "en", "_itunes_email": "george@mishurovsky.com", "_itunes_categories_xml": "", "_itunes_image": "https:\/\/mishurovsky.com\/blog\/pictures\/userpic\/userpic-square@2x.jpg?1753619610", "_itunes_explicit": "no", "home_page_url": "https:\/\/mishurovsky.com\/blog\/?go=tags\/bookshelf\/", "feed_url": "https:\/\/mishurovsky.com\/blog\/?go=tags%2Fbookshelf%2Fjson%2F", "icon": "https:\/\/mishurovsky.com\/blog\/pictures\/userpic\/userpic@2x.jpg?1753619610", "authors": [ { "name": "George Mishurovsky", "url": "https:\/\/mishurovsky.com\/blog\/", "avatar": "https:\/\/mishurovsky.com\/blog\/pictures\/userpic\/userpic@2x.jpg?1753619610" } ], "items": [ { "id": "54", "url": "https:\/\/mishurovsky.com\/blog\/?go=all\/bookshelf-update-september-2026\/", "title": "Bookshelf update — September 2026", "content_html": "

I haven’t updated my book list for half a year. So, here is what I’ve read recently and can recommend — there are some gems in the list.<\/p>\n

1. Unit Testing Principles, Practices, and Patterns — V. Khorikov<\/h3>\n

Of all the titles in the list, this impresses me the most. Despite the name, it covers all types of automated behavioural tests: unit, integration, and end-to-end. It changed my approach to code organization and my view of which tests deserve a place in a codebase. The book is also helpful in today’s drift towards agentic coding, guided by spec-driven development and TDD.
\nLink to the book<\/a><\/p>\n

2. Fundamentals of Software Architecture — N. Ford<\/h3>\n

Software engineering is often described as a craft. One reason is the many decisions developers make based on what they once saw, heard, or practiced. I believe that proficiency comes with awareness of all possible tools. Awareness turns choices into deliberate informed decisions. This book explains software architecture methodology and provides the starting set of tools for architects.
\nLink to the book<\/a><\/p>\n

3. C# 12 in a Nutshell — J. Albahari<\/h3>\n

A great source for exhaustive knowledge of C#. It reads better than I could expect from a book focused on details of a complex language. Still took me more than a month to complete. Again, awareness of internal functioning and available options leads to elegant solutions, efficient and maintainable code. Definitely a must read for senior-level C# developers. I also tried ‘Pro C#’ by A. W. Troelsen, but its 1600 pages found my head to be too small for comfortable living.
\nLink to the book<\/a><\/p>\n

4. ASP.NET Core in Action — A. Lock<\/h3>\n

This book is much harder to read than the previous one. It is not supposed to be read from start to end — and I had to shortcut my reading process and skip some sections. For those starting with ASP.NET, selective reading provides a good explanation of how things work and many code snippets ready for use as starting points.
\nLink to the book<\/a><\/p>\n

5. Accelerate — N. Forsgren<\/h3>\n

My science-inclined heart fills with joy when I see a properly designed longitudinal study with a powerful sample size in the tech industry. The study is a solid justification of DevOps practices in software development. A logical relation is the one between software delivery performance and performance-focused organizational culture. The interesting part is that implementation of DevOps is highly likely to transform the culture as well, despite the latter is a much broader concept.
\nLink to the book<\/a><\/p>\n

6. On Writing Well — W. Zinsser<\/h3>\n

Clarity of writing means clarity of thought. This book does not provide exact writing instructions, but enables freedom and desire for writing — in any style, about any subject. It is a relaxing, slow, and amusing read, breaking the line of a tech reading list. Although it put my vocabulary to work: many words come from non-fiction world of American literature, which I rarely encounter in technical media.
\nLink to the book<\/a><\/p>\n", "date_published": "2026-09-10T14:10:52+02:00", "date_modified": "2026-09-11T09:59:43+02:00", "tags": [ "book review", "bookshelf", "C#", "devops", "testing" ], "image": "https:\/\/mishurovsky.com\/blog\/pictures\/bookshelf-update-sep-26@2x.jpg", "_date_published_rfc2822": "Thu, 10 Sep 2026 14:10:52 +0200", "_rss_guid_is_permalink": "false", "_rss_guid": "54", "_rss_enclosures": [], "_e2_data": { "is_favourite": false, "links_required": [], "og_images": [ "https:\/\/mishurovsky.com\/blog\/pictures\/bookshelf-update-sep-26@2x.jpg" ] } }, { "id": "35", "url": "https:\/\/mishurovsky.com\/blog\/?go=all\/book-review-data-pipelines-pocket-reference-james-densmore\/", "title": "Book review: “Data Pipelines Pocket Reference”, James Densmore", "content_html": "

\n\n\"\"\n<\/a><\/div>\n

Software engineering is all about manipulating data. A big portion of software engineer’s attention is drawn to collection data from users and presenting it back to them in a useful form. However, there is another side of data — the kind that is not produced by software users but only consumed by them. Here, we aim for the goals of achieving a single source of truth, data validity and availability, and enabling performant processing (for analysis or presentation).<\/p>\n

To get a better grasp of the tooling for working with such data, this week I read Data Pipelines Pocket Reference<\/i> by James Densmore. The book focuses on the modern ELT (Extract-Load-Transform) approach, as well as EtLT (with ‘t’ for generic non-business-related data transformation).<\/p>\n

It turned out to be a very practical pocket guide indeed. Each section of the book dedicated to data extraction, loading, and transformation is supplied with clear code snippets in Python. The snippets demonstrate means to connect to essential services, such as databases, AWS S3, Amazon Redshift, Snowflake, Apache Airflow, as well as basics of data manipulation.<\/p>\n

I liked two things. First, these snippets feel production-ready. Surely, they feature no robust logic, but they are sufficient to start moving data around, running validations and applying transformations. Second, the author not only focuses on interaction with services, but
\nalso provides some tricks of data processing and validation. In particular, there is a neat data testing framework based on separate Python scripts for each check, which can be integrated into Airflow workflows. The approach, while being quite lean, requires a certain mindset to arrive at, so this bit of knowledge was one of the things that saves time and builds a scalable data processing foundation.<\/p>\n

That said, I think this book lacks example that are closer to real practice. It would benefit from a companion GitHub repository with a substantial dataset to run ELT against, in addition to the primitive data samples from the book which take no more than 10 rows and 5 columns in a single SQL table. The book also misses any in-depth discussions, making it a pocket reference, indeed.<\/p>\n

What it covers:<\/b><\/p>\n