Warehouse Analytics Pipeline

Skills, Tech Stack, and Libraries

1. Skills: ETL Workflows, Real-Time Data Processing, Data Visualization, Performance Monitoring

2. Tech Stack: Python, SQL, Azure Data Factory, Snowflake, Tableau, Power BI

3. Libraries: Pandas, NumPy, Matplotlib

Description and Approach

Objective:

I developed a warehouse analytics pipeline to streamline the collection, processing, and visualization of operational data. The pipeline aimed to optimize warehouse performance by providing real-time insights into key metrics such as inventory levels, order processing times, and delivery schedules.

Approach:

1. Data Collection and Integration:

   • Integrated data from multiple sources, including transactional databases (SQL Server, Oracle), IoT-enabled warehouse devices, and flat files.

   • Designed an ETL workflow using Azure Data Factory to automate data extraction, transformation, and loading into a centralized Snowflake data warehouse.

2. Data Cleaning and Transformation:

   • Preprocessed raw data using Pandas to handle inconsistencies, normalize formats, and standardize units across datasets.

   • Aggregated data into daily, weekly, and monthly summaries for easy analysis.

3. Key Metrics and Insights:

   • Identified key performance indicators (KPIs) such as inventory turnover rates, order accuracy, and storage utilization.

   • Grouped metrics by warehouse zones, products, and delivery regions for granular analysis.

Dashboard Design:

Built an interactive dashboard in Tableau to display:

1. Real-time inventory levels and trends.

2. Order processing times and bottlenecks.

3. Delivery efficiency and on-time performance metrics.

4. Included filters to allow stakeholders to drill down by time periods, zones, or product categories.

Automation and Real-Time Updates:

1. Set up scheduled data pipeline runs to ensure the dashboard refreshed in near real-time with the latest operational data.

2. Enabled notifications for warehouse managers when metrics exceeded predefined thresholds (e.g., low inventory alerts).

Code Flow:

1. Connect to data sources and extract raw data using Azure Data Factory.

2. Transform data in Python using Pandas for cleaning and aggregating metrics.

3. Load processed data into Snowflake for storage and query optimization.

4. Visualize operational metrics in Tableau for stakeholder access and analysis.

Results

The warehouse analytics pipeline delivered significant operational improvements, including:

• Increased Efficiency: Reduced order processing times by 20% by identifying bottlenecks and optimizing workflows.

• Improved Inventory Management: Achieved a 25% reduction in stockouts through real-time inventory monitoring and predictive alerts.

• Data-Driven Decisions: Provided actionable insights to warehouse managers, improving overall storage utilization by 15%.

• Scalability: The automated pipeline supported seamless integration of new warehouses and datasets, ensuring adaptability to future needs.

This project showcased the integration of advanced data engineering and analytics to transform warehouse operations and drive efficiency.

Git Link

For more information and code, visit the Git link.