Abstract:

Bike-Share Analysis is to process Cyclistic company's bike-share data and visualize the analytics on Power BI Dashboard. The data set includes annual subscription data and casual single or full day rides data. This Analytics is to help decision makers to come up with plans to convert casual trip users to subscription users. The project objective is to create a Azure Data Pipeline to read the big sets of data, transform them by using Azure Data Factory(ADF) and visualize the key performance indicator (KPIs) on a Power BI report. In this large amount of datasets (which are in csv format) which contains the information about bike-share analysis of Cyclistic company out of which we took 3 months of data, i.e, May, June, and July of 2022. This dataset is injected into Azure Data Lake Storage account Gen2 and then use Azure Data Factory (ADF) to load the data into snowflake and then transform the data as per the requirements. The transformations are all done in Snowpark. Then we connect the transform to Power BI. Finally, in the Power BI dashboard we display the key performance indicators(KPIs) with filters option to visualize the month wise data, total users and how they are constituted as members and casuals. The dashboards also contain visuals that represent the distribution of rides by types of users, also the distribution of rides by type of bikes. Apart from this, we have also added top start and end stations as they act as important keys if the company is to improve their subscription members.

Description:

Azure, Snowflake, Power BI