Update readme to reflect moved sample files (#58)

Sample files now live in the same s3 bucket as the classic jaffle shop
(but in a subdirectory), so that the sharing rules are consistent. We
were seeing that the old bucket couldn't be accessed from Snowflake for
some reason.

I've also added:
- a direct download link for each file, so that people can alternatively
download the file and then reupload it into their warehouse of choice
- the schema for each table to make it easier to manually write create
table statements

I've validated that all files can be successfully copied into Snowflake
and accessed from DuckDB.

I recommend using the visual diff: 
<img width="823" alt="image"
src="https://github.com/user-attachments/assets/815b84d4-df40-43c6-9e4d-c16b16f5fa89">

README.md

@@ -199,12 +199,14 @@ There are two ways to work with a larger dataset than the default one year of da
 
 To load the data from S3, consult the [dbt Documentation's Quickstart Guides](https://docs.getdbt.com/guides) for your data platform to see how to copy data from an S3 bucket to your warehouse. The S3 bucket URIs of the tables you want to copy into your `raw` schema are:
 
-- `raw_customers`: `s3://jaffle-shop-raw/raw_customers.csv`
-- `raw_orders`: `s3://jaffle-shop-raw/raw_orders.csv`
-- `raw_order_items`: `s3://jaffle-shop-raw/raw_order_items.csv`
-- `raw_products`: `s3://jaffle-shop-raw/raw_products.csv`
-- `raw_supplies`: `s3://jaffle-shop-raw/raw_supplies.csv`
-- `raw_stores`: `s3://jaffle-shop-raw/raw_stores.csv`
+| table name        | S3 URI                                                           | Direct Download Link                                                                                     | Schema                                                                                                    |
+|-------------------|------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------|
+| `raw_customers` | `s3://dbt-tutorial-public/long_term_dataset/raw_customers.csv` | [Download](https://dbt-tutorial-public.s3.us-west-2.amazonaws.com/long_term_dataset/raw_customers.csv) | `(id text, name text)` |
+| `raw_orders` | `s3://dbt-tutorial-public/long_term_dataset/raw_orders.csv` | [Download](https://dbt-tutorial-public.s3.us-west-2.amazonaws.com/long_term_dataset/raw_orders.csv) | `(id text, customer text, ordered_at datetime, store_id text, subtotal int, tax_paid int, order_total int)` |
+| `raw_order_items` | `s3://dbt-tutorial-public/long_term_dataset/raw_order_items.csv` | [Download](https://dbt-tutorial-public.s3.us-west-2.amazonaws.com/long_term_dataset/raw_order_items.csv) | `(id text, order_id text, sku text)` |
+| `raw_products` | `s3://dbt-tutorial-public/long_term_dataset/raw_products.csv` | [Download](https://dbt-tutorial-public.s3.us-west-2.amazonaws.com/long_term_dataset/raw_products.csv) | `(sku text, name text, type text, price int, description text)` |
+| `raw_supplies` | `s3://dbt-tutorial-public/long_term_dataset/raw_supplies.csv` | [Download](https://dbt-tutorial-public.s3.us-west-2.amazonaws.com/long_term_dataset/raw_supplies.csv) | `(id text, name text, cost int, perishable boolean, sku text)` |
+| `raw_stores` | `s3://dbt-tutorial-public/long_term_dataset/raw_stores.csv` | [Download](https://dbt-tutorial-public.s3.us-west-2.amazonaws.com/long_term_dataset/raw_stores.csv) | `(id text, name text, opened_at datetime, tax_rate float)` |
 
 #### 🌱 Generate via `jafgen` and seed the data with dbt Core