✨Project Highlights✨:
💠 Created a #Lakehouse (Data Lake) and #Warehouse in the Microsoft Fabric.
💠 Ingest five different data sources (JSON, CSV, Excel, SQL, and XML) to the Lakehouse by leveraging #DataPipelines.
💠 Created a #Dataflow, the data was pulled from Lakehouse to dataflow and perform data transformations and publish it to warehouse.
💠 Created a final data pipeline (#InvokePipeline) and orchestrated with source pipeline and transformation pipeline to get final output.
💠 By leveraging OneLake, data was pulled to Power BI desktop and crafted a report.
📝 Report Highlights:
🏠 Home Tab: It's crafted in a way that how the uber carrying a ride from home to destination. By using this tab, we can navigate to other tabs.
📈 Trend Analysis: We can see few key KPIs and current year (CY) v/s last year (LY) trends in the same visual also we can see numbers as well in a tabular format.
💰Revenue: We can see revenue by top rider, user and location. There are other insights those will give revenue by ride time, ride type and ride day. Additionally, we can see comparison of revenue growth over the time.
🚕 Rides: Rides by top rider, driver and location. There are other insights those will give rides by time, type and day. Additionally, we can see comparison of rides growth over the time.
🧑✈️ Driver info: We can see multiple driver related metrics like driver status, vehicle details, ride details, revenue details and the most important rating ⭐ is also here.
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