Faster and Reliable Reporting
The time required to generate daily reports dropped from nearly 3 hours to just 15 minutes. Reports were now consistent, accurate and automatically updated, allowing business teams to make quicker decisions.
Client Overview
Client: Mid-Sized Online Fashion Retailer
Location: United Kingdom
Industry: E-Commerce, Fashion, Retail Analytics
Project Background
A UK-based fashion brand operated across multiple digital stores and relied on seasonal campaigning and stock-keeping units to generate revenue. With growing order volumes, they required a system to coordinate between e-commerce operations, marketing analysis, and inventory planning. However, the client was facing critical issues due to a fragmented data workflow. Unintegrated product catalog, sales, marketing, and warehouse systems led to significant lags in report generation and decision-making, which affected their overall ability to stay adaptive in fast fast-changing retail environment.
Technical Challenges
Multi-Platform Data Fragmentation : The data was spread across multiple platforms, making it difficult to bring everything into a single pipeline and delaying analysis.
No Centralized Performance Metrics : No unified platform to track key metrics; making sales, marketing, and customer trends view separately, leading to inconsistent reporting.
Manual reporting workflows : Manual daily reports consumed multiple team hours, which were prone to errors, often leading to inaccurate or outdated business insights.
Limited Demand Insights : Absence of trend analysis made it hard to predict which products would sell fast or run out, leading to poor supply chain and promotion decisions.
Inability to React to Live Trends: The marketing team struggled to access up-to-date campaign performance during live promotions, leading to missed sales opportunities.
Technical Implementation
Automated Data Orchestration with Airflow
We used Apache Airflow to manage and schedule daily data workflows. E-commerce platform, inventory system, and marketing tools were grouped into DAGs, which allowed data to flow smoothly and automatically, with clear visibility and retry mechanisms.
Scalable Data Processing with Databricks
Raw data collected, cleaned, and processed inside Databricks using PySpark. This setup efficiently handled large volumes of data and enabled parallel processing, thereby reducing overall data preparation time and improving performance across the pipeline.
Unified Data Storage with Delta Lake
We used Delta Lake as the central storage layer to hold cleaned and transformed data. Its version control and time travel features made it easy to audit any changes, fix issues quickly and smoothly, and ensure the accuracy of all the data over time.
Performance Metrics Layer and Error Handling
A new performance metrics layer was created, which updated automatically, helping them make faster & data-backed decisions. Each pipeline had alerts and checks to catch failures, ensuring quick resolutions and an accurate flow of data.
Business Benefits
Accurate Inventory Forecasting
With clean historical data and automated trend analysis, the brand improved its ability to predict product demand. This led to better stock management and fewer cases of out-of-stock or overstocked items.
Improved Campaign Planning
Marketing teams now had access to timely performance data, which helped them adjust campaigns while they were still running. This led to more effective promotions and better use of marketing budgets.
Reduced Manual Effort through Automation
By automating the daily data workflows, the need for manual downloads, merging and report building was eliminated. This saved over 20 hours of manual effort per week, allowing teams to focus on strategic tasks instead of repetitive data cleanup.
Lower Operational Costs with Lean Data Team
With streamlined pipelines and auto-updating dashboards, the company no longer needed extra analysts just for reporting. The existing data team could handle increased data volume, resulting in measurable cost savings in operations.
Key Innovation
The data pipelines were designed in a modular way using Airflow DAGs and Task Groups. This provides the team flexibility to easily plug in new data sources or make changes without disturbing existing flows. Delta Lake was introduced as the core data layer. Its built-in support for time travel and version control made debugging and data auditing much easier, reducing risks during critical reporting periods. A centralized performance layer combined metrics from different business units (sales, inventory, marketing) into one unified view. This reduced dependency on separate reports and allowed all teams to work with the same real-time insights. Each step in the pipeline included validation checks, and failure alerts were integrated with the company’s communication tools. This ensured that data issues were caught and fixed quickly.
Architecture
The solution was designed around a streamlined data pipeline using Apache Airflow as the central scheduler and Databricks for data transformation and analytics. Daily workflows were triggered through Airflow, which pulled data from multiple systems including the e-commerce platform, ERP and marketing tools. Each source was handled through modular tasks grouped within DAGs, ensuring clean separation and easy maintenance.
Raw data was processed in Databricks using PySpark, where it was cleaned, joined, and transformed into business-ready tables. All final data was stored in Delta Lake, allowing version control, rollback and time-based tracking. The processed data powered auto-refresh dashboards built using business intelligence tools. These dashboards provided unified performance views across teams, with insights on sales, inventory levels, product performance and campaign trends all updated with minimal latency.
The architecture supported validation steps, alerting mechanisms and could be scaled to include more data sources in the future without disrupting the existing flow.
Solution
To overcome these challenges, we implemented a scalable data pipeline using Apache Airflow for orchestration and Databricks for transformation and analytics. The system was designed to pull data automatically from various platforms such as e-commerce store, ERP systems and marketing tools to have up to date information.
Using Airflow, we scheduled and monitored daily workflows that ingested raw data of key areas such as orders, inventory, campaigns and customer requirements. This data was then transformed and processed in Databricks using PySpark which helped to do operations like data cleaning and parallelized computing.
We also built an easy to use Dashboard to have a single performance view where all important numbers from sales, inventory and marketing were brought together. This dashboard get updated automatically which reduced manual efforts, improved decision making, helped team to plan promotions and forecast product demand accurately.
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