Comprehensive KPI Dashboard: Analyzing Sales Performance, Financial Health, and Customer Purchasing Trends

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Description

This dashboard offers a detailed view of KPIs including sold units, gross/net sales, tax data, and outlet count. It also provides insights into Average Selling Price (ASP), Average Transaction Value (ATV), and Units Per Transaction (UPT) to reflect financial health and customer purchasing patterns.

Practical Use Case and User Story

As a sales analyst, I need an AWS QuickSight dashboard that integrates sales and performance data from Amazon Redshift and PostgreSQL. The dashboard should display KPIs such as sold units, gross sales, and outlet performance, with forecasts for future trends using models deployed in Amazon SageMaker. Materialized Views should optimize data load times for fast performance, and automated daily updates via AWS Lambda and AWS Glue will ensure the data stays current. This will help me track sales, revenue, and customer behavior effectively.

Tech Stack Involved

Data Collection & Integration
  • APIs: REST, GraphQL (to collect data from external sources)
  • Data Connectors: AWS Glue, Talend, Stitch (for integrating multiple data sources)
  • Data Streams: Apache Kafka, AWS Kinesis (for real-time data streams)
  • ETL/ELT
  • ETL Tools: Apache Airflow, dbt (data transformations in the cloud)
  • Cloud ETL Services: AWS Glue, Azure Data Factory (for scalable ETL pipelines)
  • Data Processing: AWS Lambda (for event-driven data processing)
Databases & Data Storage
  • Relational Databases: PostgreSQL, MySQL (for structured data storage)
  • Data Warehousing: Amazon Redshift, Snowflake (for centralized data storage and fast queries)
  • NoSQL Databases: DynamoDB, MongoDB (for unstructured or semi-structured data)
  • Cloud Storage: Amazon S3, Azure Blob Storage (for storing large datasets or flat files)
Data Analytics & Visualization

Business Intelligence (BI) Tools:

  • Amazon QuickSight: Scalable cloud-native BI service
  • Microsoft Power BI: Comprehensive analytics and interactive dashboards
  • Tableau: Popular for creating highly visual dashboards
  • Google Data Studio: Free and integrated with Google services for basic dashboards
  • Data Querying: SQL, PostgreSQL (for querying data for dashboarding tools)
Data Preparation & Transformation
  • Data Wrangling Tools: Pandas, PySpark (for handling complex data transformations before visualization)
  • Data Cleansing: Trifacta, OpenRefine (for preparing clean datasets for dashboarding)
Cloud Infrastructure
  • Cloud Compute: AWS EC2, Azure VMs (for hosting dashboards or running backend services)
  • Containerization: Docker (for packaging and deploying dashboard applications)
  • Serverless Options: AWS Lambda, Azure Functions (for lightweight, event-driven tasks)
Collaboration & Version Control
  • Version Control: GitHub, GitLab (to track dashboard development)
  • CI/CD: Jenkins, GitLab CI (to automate the deployment of dashboards)

Demo

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