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About
Real-time rent revenue data platform analyses various Tsavo Apartment rents in Nairobi in the months between the year 2020 and 2023. Data warehouse infrastructure is housed in the AWS ecosystem and uses Apache Airflow for orchestration with public-facing dashboards created using Tableau.
About
Real-time rent revenue data platform analyses various Tsavo Apartment rents in Nairobi in the months between the year 2020 and 2023. Data warehouse infrastructure is housed in the AWS ecosystem and uses Apache Airflow for orchestration with public-facing dashboards created using Tableau.
Questions? Feel free to reach me at raphaelmalimsj@gmail.com
Public Dashboard Link: https://public.tableau.com/views/TsavoKE/RentalRevenue?:language=en-US&:display_count=n&:origin=viz_share_link
Architecture
Architecture
Data is sourced from random ‘Tsavo Rental’ bank accounts that mimic rental payment transaction records. The payment records are subject to be sourced through bank APIs (ABSA, KCB, Coop, National Bank), streamed using Kafka after being published to their respective brokerage topics then stored in Db2 Data Warehouse on IBM Cloud. SQL scripts are ran to transform the data from its raw form though a staging schema and into production target tables. The presentation later is created using Tableau, a data visualization tool.
Data Model
Data Model
Dimension Tables: dim_property dim_tenant
Fact Table: rent-transaction
The data model is implemented using star schema relating the fact table to the two dimensions (tenant and property).
Dashboard
Dashboard
The dashboard is publicly accessible here: https://public.tableau.com/views/TsavoKE/RentalRevenue?:language=en-US&:display_count=n&:origin=viz_share_link
The dashboard communicates different metric analysis objectives of the real estate firm. ‘Tsavo’ was ale to analyze rental income, occupancy rates, lease duration, and tenant retention rates based on the stream customer payment records over the years. The analysis summaries have been stated on the dashboard making it easier for the business intelligence team to make decisions out of the data and potentially customize the pipeline to fit future business needs.
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