rushi0611

1.Real-time Transactions with Kafka and PySpark: Set up a Kafka cluster to handle real-time streaming of transactions data. Use PySpark to consume this data in real-time, perform necessary transformations, and ensure data quality. 2.Data Storage with MongoDB: Store the processed data into MongoDB for efficient querying and retrieval. MongoDB's flexible schema design and scalability make it suitable for storing large volumes of data. 3.Visualization with Tableau: Connect Tableau to MongoDB to visualize the data in real-time. Create meaningful dashboards and reports to analyze transaction patterns, trends, and anomalies. 4.Decision Making and Analysis: Utilize Tableau's interactive features to drill down into specific aspects of the data and identify insights. Use these insights to make informed decisions, optimize business processes, and enhance operational efficiency.

1.Load Data: Import your e-commerce sales data into Power BI. 2.Transform Data: Clean and shape your data in the Power Query Editor. 3.Build Dashboard Sheets: Create visuals (charts, tables, etc.) to represent sales data. 4.Add Cards: Display key metrics like total sales, number of orders, etc., using card visuals. 5.dd Slicers: Insert slicers to filter data by state or other categories for better insights.