zeneli / startuporganizer

Django Unleashed running project.

Startup Organizer

A blog focused on news relating to technology startup businesses. The goal is to help publicize startups to blog readers.

Project Specification

Each startup will be its own object which allows us to add special information related to the business such as:

• listing a description and a date, or
• linking to external articles.

Each startup will have its own set of tags labels. Tags allow for categorizing and organizing blogs in a simple, but flexible manner.

Blog posts will exist for news about the site or to annouce news about startups in our system.

Behaviors Spec

The project should implement the following behaviors:

• list startup organizations
• organize startups (by tags or labels)
• link to external news sources about startups
• write blog posts about startups

Data Model

To achieve the behaviors above, we need to store information about four entities: startups, tags, news links, and blog posts. The following is a list of entites and associated attributes for our models. Each entity is a table and the associated attributes are the corresponding columns of the table.

• Tag
  • tag name
    • at most 31 characters
    • unique
  • slug
    • at most 31 characters
    • unique
• Startup
  • name of company
    • at most 31 characters
    • unique
  • slug
    • at most 31 characters
    • unique
  • description or purpose
  • date founded
  • contact email
  • website
• News Link
  • title or headline
    • at most 63 characters
  • link to article
    • at most 255 characters
  • publication date
  • foreign key to Startup
• Blog Post
  • post title
    • at most 63 characters
  • slug
    • at most 31 characters
    • unique across months
  • post text or description
  • publication date
• Many-to-Many Relationship
  • startup and tag
  • blog post and tag
  • blog posts and startup

Relationships

1. External news articles may thus only point to a single startup business, but any of our startup businesses may have multiple articles written about it.
2. We allow for Startups to be categorized by Tag objects. A Tag object may categorize many Startup objects, and Startups may be categorized by many Tag objects.
3. Blog Posts may be about multiple Startups, just as Startups may be written about multiple times.
4. Posts may also be categorized by multiple Tags, just as Tags may be used multiple times o categorize different Posts.

• one-to-many, News Link to Startup: A news link may belong to only one startup, but a startup may have multiple news links.
• many-to-many, Startup to Tag: Each startup will have multiple tags, and each tag may belong to multiple startups.
• many-to-many, Blog to Tag: Each blog post will have multiple tags, and each tag may belong to multiple blog posts.
• many-to-many, Blog to Startup: Each blog post may be about multiple startups, and each startup may be written about about multiple times.

Model behavior

• Tags are ordered alphabetically by name.
• Startups are ordered alphabetically by name. The latest startup should be the most recently founded startup.
• News links are ordered by descending publication date. The latest news link is the most recently published one.
• Posts are ordered by descending publication date, then by title. The latest post is the most recently published one.

Features

1. a structured organization of startups according to tags
2. a blog

List of pages.

List of webpages we will build:

1. page to list the tags
2. page to list startups
3. page to list blog posts
4. page for each tag
5. page for each startup
6. page for eac hblog post (which also lists news articles)
7. page to add a new tag
8. page to add a new startup
9. page to add a new blog post
10. page to add and connect news articles to blog posts