Case Study: For Leading Pharmacy Retail Stores 

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Business Context

A leading US retail pharmacy chain handles vast amounts of transactional, inventory, and customer data daily. This data comes from diverse sources, including APIs (REST and GraphQL), SQL/NoSQL databases, and third-party platforms such as marketing and logistics systems. The company requires a scalable ELT pipeline to ensure high-quality data is transformed and analyzed efficiently.

Problem Statement

The pharmacy faced the following challenges:

1. Scattered Data Sources:
• APIs, SQL/NoSQL databases, and third-party platforms provided data in various formats and frequencies, making it difficult to consolidate.
2. Data Quality Issues:
• Inconsistent customer details, duplicate entries, and missing product information created reporting inaccuracies.
3. Performance Bottlenecks in Legacy ETL System:
• The traditional ETL process extracted and transformed data before loading it into the database.
• This approach could not handle 2 million new rows daily (~50 million rows in total).
4. Inefficient Reporting:
• Tableau dashboards were slow, taking several minutes to load due to high query complexity.

Proposed Solution

To address these challenges, the company implemented a modern ELT (Extract-Load-Transform) architecture with the following components:

1. Data Sources: APIs, SQL/NoSQL databases, and third-party platforms.
2. Data Quality Tool: Informatica Data Quality (IDQ) for profiling, cleansing, and standardization.
3. Data Destination: MySQL as the centralized data warehouse.
4. Reporting: Tableau for analytics and visualization.

Scenario Details

Data Sources and Volumes

1. APIs (REST, GraphQL):
• Customer transactions, loyalty programs, and prescription refills.
• Daily Volume: 1 million rows.
2. SQL Databases:
• Inventory and store-level sales data.
• Daily Volume: 600,000 rows.
3. NoSQL Databases:
• Real-time clickstream data from the pharmacy's e-commerce platform.
• Daily Volume: 300,000 rows.
4. Third-Party Platforms:
• Logistics, marketing campaigns, and supplier data.
• Daily Volume: 100,000 rows.

Solution Architecture

1. Extract-Load (EL)
• Data is directly extracted from APIs, SQL/NoSQL databases, and third-party platforms in near real-time.
• Extracted data is loaded as-is into staging tables in MySQL without transformations.
• APIs are scheduled to fetch data every 15 minutes, while SQL/NoSQL databases use incremental loads.
2. Transform (T): ELT in MySQL
• Transformations occur inside MySQL using SQL scripts, stored procedures, and views for better performance and scalability.
• Key Transformation Steps:
• Data Deduplication: Identify and remove duplicate customer and transaction records.
• Standardization: Harmonize product names, customer addresses, and date formats.
• Aggregations: Summarize daily transactions, inventory levels, and campaign performance for Tableau.
• Data Enrichment: Join customer loyalty data with transaction history to derive customer lifetime value (CLV).

1. Data Quality with Informatica Data Quality (IDQ):
• Profiling: Regularly profile data to identify anomalies such as missing or invalid fields.
• Cleansing: Remove duplicates, validate email addresses, and ensure data consistency across sources.
• Validation Rules: Ensure business rules are met, e.g., product price > $0, valid prescription IDs.

1. Data Destination: MySQL as Centralized Repository
• Partitioning: Data is partitioned by date to optimize query performance and storage.
• Indexing: Key fields like customer ID, transaction date, and product ID are indexed for faster lookups.
• Compression: Reduces storage costs while maintaining query speed for large tables.

1. Reporting with Tableau
• Tableau connects directly to MySQL’s aggregated and transformed tables.
• Dashboards:
• Sales Dashboard: Displays sales trends by store, region, and product category.
• Inventory Dashboard: Highlights stockouts and overstocked items by location.
• Customer Insights Dashboard: Provides metrics like CLV, loyalty program engagement, and top customers.
• Optimizations: Tableau’s extract mode is used for historical data, while live connections provide real-time updates.

Performance Metrics

1. Data Volume Management:
• 2 million rows daily (~50 million total) are processed efficiently within the MySQL database.
• ELT ensures that raw data is loaded quickly, with transformations handled during off-peak hours.
2. Improved Query Performance:
• MySQL’s indexing and partitioning reduce query execution times by 40%.
• Tableau dashboards load in under 5 seconds, down from several minutes previously.
3. Data Quality:
• Informatica Data Quality ensures 99% data accuracy by resolving duplicates and fixing errors during profiling.

KPIs Monitored Post-Implementation

1. Operational Efficiency:
• ETL pipeline execution time reduced from 4 hours to 1.5 hours with ELT.
2. Reporting Speed:
• Tableau dashboard loading time reduced from 3 minutes to under 5 seconds.
3. Sales Growth Analysis:
• Enabled real-time insights into sales trends, increasing revenue by 8% through optimized promotions.
4. Inventory Optimization:
• Reduced stockouts by 15% and overstock by 10% using near-real-time inventory monitoring.
5. Customer Engagement:
• Enhanced loyalty program participation by 20% through data-driven campaign targeting.

Benefits of the ELT Approach

1. Scalability:
• MySQL can handle growing data volumes, ensuring scalability beyond the current 50 million records.
2. Efficiency:
• ELT reduces the need for expensive compute resources during extraction and loading.
3. Improved Data Quality:
• Informatica Data Quality ensures reliable data for decision-making.
4. Faster Reporting:
• Tableau’s optimized dashboards empower executives with actionable insights, improving decision-making speed.

This ELT-based architecture transformed the pharmacy’s data operations, enabling efficient data processing, high-quality reporting, and data-driven decisions.