Have you ever tried building a simple analytics stack for your company, yet not sure what’s the best way to start with “just the right amount of effort” that can still scale it up later when you grow? And when you went googling you soon got confused by the conflicting advice on the internet: ETL or ELT, data warehouse or data lake, SQL or OLAP cube, etc?
That’s a common problem that I saw when speaking to our customers. I soon realized that books about data analytics are quite outdated given the current pace of development in the space.
There isn’t an up-to-date, high-level “map” that gives readers a proper framework of thinking about the analytics landscape. Much of the materials out there are step-by-step how-to based on a pre-selected set of tools/technologies.
We decided to write that “map” in form of this short book - “The Analytics Setup Guidebook”. The book aims to give readers better clarity over the confusing analytics landscape.
It covers:
* How does a simple, yet scalable analytics stack look like, and how you can build one cheaply and quickly
* ETL vs ELT, what’s the big deal? And why ELT is better than ETL nowadays.
* Why data modeling is important in analytics, Kimball data modeling and how it’s relevant in modern cloud systems
* Why the market of BI tools is so confusing nowadays
* How company’s BI adoption will predictably evolve over time
The book is intended as a comprehensive intro for data people, but data veterans might find chapter 3 and chapter 4 useful — we spoke about how Kimball Data Modeling fits in with modern cloud infrastructure (MPP), and how BI has evolved over time in an organization.
The best part about the book is it’s short, well-illustrated and can be finished within 3 hours of reading!
Here’s a direct, ungated link to the book if you don’t want to give your email: https://cdn.holistics.io/guidebook/the-analytics-stack-guide...
We’ve received some encouraging feedback, but love to hear you guys’ feedback on the book.
Regards -Huy
