This is great! I couldn't agree more with what you've written above. This is something so basic and fundamental, but at the same time new to many people. I haven't found a book as easy-to-read yet substantive as this. 10/10 would recommend, more fun than writing an aggregation query in MongoDB

Howdy makers! Today you can get the digital version of 'The Analytics Setup Guidebook' for FREE on Product Hunt! 🚀 → https://www.holistics.io/books/s... In 2015, I co-founded Holistics — a self-service business intelligence tool that lets you turn raw data into actionable business insights. We've dealt with over a hundred enterprise clients at this point, and probably hundreds more data analysts, data engineers and CTOs, from across the world. After a number of years, I realised that data professionals I spoke to often had piecemeal knowledge of our industry. Either they weren't aware of what came before, and therefore could not see the cyclical nature of ideas in data analytics, or they hadn't yet caught up with the full implications of more recent tech developments, some of which have enabled *completely different* workflows from the BI tools from before. I found myself repeating the same stories again and again, along with sending links to the same handful of explanatory articles. The fact of the matter was that modern data analytics knowledge is not yet equally distributed: much of it is locked up in the heads of practicing professionals, and spread out in blog posts and meetup talks and conference keynotes. Nobody had the time to put everything together into a map of the entire industry. This year, we decided to create exactly this map. The Analytics Setup Guidebook is meant to give you just enough to 'not get lost' in the entire business intelligence landscape. You will, in two hours, learn what a modern analytics stack looks like, learn how to think about the various steps involved, understand what the best practices are, and walk away with a high-level overview of the industry — and all that came before. I hope you find the book useful, and if you have any thoughts, I'd love to hear your comments at content@holistics.io! On behalf of the team, Huy Nguyen, CTO @ Holistics.io —- You may also reach out to us on these channels: - Twitter: https://twitter.com/holistics_bi - Blog: https://holistics.com/blog - Facebook: https://www.facebook.com/holisti... - Medium: https://medium.com/holistics-sof...

As a newbie who just started my path in data analytics, I really appreciate the use of language in the book, it sounds more like a friendly sharing from an experienced partner in the field. You do not just lay out technical details and pure recommendations, but each piece comes with thorough analysis from different perspectives and considerations (for resources, performance, contemporary outlooks, and company directions), so it rhymes very well with me and I guess with other readers, too. Really great work & please keep it going!

This looks great! I think you get the problem statement right. I work as a VC (venture capital) and most of the startups we spoke to (or invested in) are either under invested in BI, or over-invested for their stage (spent more money and time on it than necessary). Will share this with our group of portfolio companies.

Just skimmed through the book and it looks amazing! I have a few friends working in startups and struggling to set up the data analytics platform for their company. Now I can simply forward this book to them :)

Awesome! This guidebook shows me a clear roadmap to set up a data analytics architecture. It answers the questions asked by people suffering from inefficient ETL processes. It also answers the questions asked by people confused by unstandardized business definitions. In the end, it shows a way to build a consistent business logic with the data modeling layer ONCE! It's worth to read for people wanna pick a business intelligence tool.

Awesome read! I took a quick first pass through the book, and will come back to reread it over the weekend. You've covered a lot of the main questions and concerns and explained things in a very clear and concise manner. The use of images is good in explaining the concepts in a more visual snapshot. I love that you went into more of the detail in the later chapters around modelling, transformations, and providing real-world case studies. One area that I've found lacking in explanations of the modern analytics stack in general is any lack of comparison with the older or commercial tools that so many companies seem to pin their hopes and expectations on.