This document discusses choosing a technology stack and data architecture. It recommends starting with the minimum viable technology to build an initial alpha version, using only familiar skills and technologies to build the most important pieces first according to the product narrative and wireframes. For data architecture, it suggests a simple description of where data will be stored and what each store will contain, such as using a MySQL database for social media listings in a web app or SQLite for a mobile app. The goal is not to optimize at this stage but have a functional initial setup.

2. Decide on Technology
Stack & Data
Architecture
Vishnu Gopal

3. Where is your team now?

4. 1. You’ve Shortlisted an Idea
2. You’ve got a clear
Product Narrative

5. If you haven’t done all of those, please go
back and complete those targets.
You can’t choose a technology stack
without knowing what you’ll be building.

6. Why is this important?

7. 1) A good technology stack can
make or break version 1 of
your product.

8. 2) A simple “good enough” data
architecture can lead to very
fast initial iterations.

9. How is this done?
A: Technology Stack

10. Basic Philosophy:
Minimum Viable Technology
Review session by
Ajay Shrivastava

11. 1) Pick “just enough”
technology to build an alpha

12. 2) Do not plan forward too
much.

13. 3) Use only technology you
already know & are familiar
with.
See Target: List Engineering
Skills.

14. 4) Build according to your
Product Narrative &
Low-fidelity Wireframe.

15. 5) Build the most important
pieces first.
Note: for the purposes of
Alpha, do not build a login &
signup workflow.

16. How is this done?
B: Data Architecture

17. Data Architecture = simple
answer to: “Where is your data
stored? What does each store
contain?”

18. No diagrams required at
this stage.

19. Example: “My data is mostly social media listings
that I will store in MySQL”.
(Web App Example. After you scale, you can explore
alternative data stores)

20. Example: “My data is mostly stored in the mobile app
in a SQLite DB”.
(Mobile App Example. After you scale, you can
explore
alternative data stores)

21. Do not optimise data stores right
now.
For 90% of cases, stick to a
relational database.
MySQL or Postgres (Web) and
SQLite (Mobile) is preferred.

22. Todo: Submit a document
which lists the stack chosen
and the data architecture.

23. Check the Rubric for more
information.