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Consider the following conceptual schema of an operational database owned by multinational real estate company. The database contains information about the real estate properties offered for sale, owners of properties, potential buyers who are interested in the properties and real estate agents involved in selling of the properties.

Whenever a property is put on market by an owner, a description of the property is entered into an operational database. Whenever a property is purchased, its description is removed from operational database. The real estate company would like to create a data warehouse to keep information about the finalized real estate transactions, properties involved in the transaction, sellers/owners, and agents involved in the real estate transactions. The real estate company would like use a data warehouse to implement following classes of analytical applications.

1. Find the total number of real estate properties sold per month, along with information about year, street, city country and agent involved.

2. Find an average asked price of real estate properties sold per month, along with information about year, street, city country and agent involved.

3. Find an average final price of real estate properties sold per month, along with information about year, street, city country and agent involved.

4. Find an average period of time on the marked real estate properties sold per month, along with information about year, street, city country and agent involved.

5. Find the total number of times each real estate property has been sold in a given period of time.

6. Find the number of buyers interested in purchases of real estate properties sold per day, month, along with information about year, street, city country and agent involved.

Question 1 – Perform dimension modelling for above use case by explicitly performing following.

1. Show fact and dimension tables along with required attributes for the same.

2. Call out explicitly Primary Key & Foreign key in each of the tables using acronyms as PK for Primary Key and FK for Foreign Key.

3. Establish relationship between Fact and Dimension tables. [2 Marks] 4. Data Modelling using:

a. Star Schema

b. Snowflake Schema

Question 2 – Prepare a data pipeline design to ingest data from transactional (aka OLTP) system into data warehouse by showing visual representation of your data pipeline, along with explanation of the same. You can consider that data needs to be ingested every 30mins from OLTP systems to Data Warehouse system into the final tables designed in previous questions using ELT technique.

Please note that Data pipeline should clearly articulate clearly tables being read from transactional system (you can assume and consider any table name/structure from transactional system) and how that data will land into final reporting tables for meeting reporting needs as mentioned in this use case but should consider either Star or Snowflake schema of data modelling showing tables in which data will be inserted and should also make use of staging tables.

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