Introduction to Cloud-Based Big Data Analytics for Finance Professionals
Welcome!
We are passionate about finance and technology, and one of the greatest ways we know to share that passion and have others grow to share it with us is through training and teaching.
This learning path is for any finance professional who works with data in some shape or form and feels that there must be better tools out there than spreadsheets. There are! Spreadsheets are great and continue to have many uses, but they are not the answer to everything.
Using a data warehouse and analytics platform can be a transformative experience. We have many personal anecdotes where finance professionals have gone from "I'm ready to quit, everything runs on pivot tables and all I do is copy-paste between spreadsheets" to "Wow, not only have I automated all my data workflows with SQL, I've now set up automated delivery of reports in PDF format by email to key stakeholders, giving them key insights daily instead of monthly!".
We think everyone should have a chance to experience that, and grow personally and professionally by learning and doing new things. Writing code and automating workflows compounds in a way that repetitive work such as creating monthly reports does not.
Introducing Google BigQuery as a Data Warehouse and Analytics Engine
There are many cloud-based big data analytics solutions on the market, and we at AI for Finance don't spend enough time with each of them to do a fair side-by-side comparison – there are others who do that kind of work much better. (Consider Gartner, Forrester Research, IDC, and G2, for example).
Our analytics platform of choice is Google Cloud, with Google BigQuery as our data warehouse, analytics and AI solution. We are not affiliated with Google in any way but have found from personal experience that it has several benefits for finance professionals:
- It's user friendly and easy to get started with also for non-technical users. You don't have to be an engineer to feel at home in the BigQuery console.
- It's powerful, allowing you to analyze huge datasets at lightning speed.
- You interact with it using SQL and Python, global (ANSI, ISO) and de facto standards .
- It supports data workflow automation using scheduled queries
- The Looker Studio lightweight data visualization tool has a good balance of features while being accessible to users coming from visualizing data primarily in spreadsheets and slides, rather than in other BI tools.
- It integrates well with Google Workspace. As more and more finance professionals find themselves working in a Google Workspace environment, Google Sheets has also become their go-to spreadsheet tool.
But don't worry if you are not able to get access to Google BigQuery at your workplace – these introductory tutorials should work on other similar platforms with minimal modifications, and as said, many other platforms have many of these benefits as well
Our approach to learning and development
We believe that the best way to learn is by doing. Rather than relying on theory alone, our tutorials will guide you through practical, hands-on examples that reflect real-world finance use cases.
Instead of following abstract exercises, we encourage you to think about something valuable that you want to build—whether it's automating a reporting process, running a financial forecast, or analyzing large datasets more efficiently. By applying what you learn to a meaningful project, the knowledge will stick, and you'll develop skills that have an immediate impact on your work.
To make the learning experience interactive, we will include exercises, challenges, and opportunities for you to test your knowledge at each step. The goal is for you to come out of this learning path not just understanding how BigQuery works but how to use it effectively in your own finance workflows.
The modules in this learning path will be released gradually – click here to start reading the first one. We look forward to having you with us on this learning journey! 🚀