Case Study

Building a Cost-Effective Business Intelligence Platform Using Open-Source Tools

An online retail company, RetailX, was facing challenges in managing and analyzing vast amounts of transactional data. Their existing proprietary business intelligence (BI) solution was expensive to maintain and lacked the flexibility needed for real-time insights. To address these challenges and reduce costs, RetailX opted to build a new BI platform using open-source tools—Apache Superset, Apache Druid, and Apache Airflow. The new solution not only resulted in significant cost savings but also improved the retailer’s ability to derive actionable insights in real time.

Quick
Summary

Team Size

6 Members

Duration

3 months

Platform

React Native, Firebase

The Challenges

High Costs of Proprietary BI Tools

RetailX was spending a significant portion of its budget on licenses, infrastructure, and ongoing maintenance of its commercial BI software.

Lack of Flexibility and Real-Time Analytics

The existing BI tool did not support real-time data processing, leading to delays in accessing critical insights like sales trends, inventory levels, and customer behavior.

Scalability Issues

As RetailX’s data volumes grew, the legacy system struggled to scale, resulting in performance bottlenecks when querying large datasets.

Solution: Key Components of the Architecture

Apache Superset for Data Visualization

Interactive Dashboards

Superset provided a highly customizable and interactive dashboarding tool for visualizing key metrics. RetailX created sales reports, customer behavior insights, and inventory dashboards that updated in near real-time.

Open-Source Flexibility

Superset’s open-source nature allowed the team to extend its functionality and integrate it with other in-house systems, creating a tailored experience for their specific business needs.

SQL Lab

The SQL Lab feature in Superset enabled data analysts to explore and query data interactively, providing faster ad-hoc reporting capabilities.

Apache Druid for Real-Time Analytics

Real-Time Data Ingestion

Apache Druid served as the backbone of RetailX’s new BI infrastructure, providing a high-performance, distributed data store that could ingest real-time data from their sales platform, website, and inventory systems.

Fast Querying of Large Datasets

Druid’s columnar storage format and optimized indexing allowed RetailX to query terabytes of data with sub-second response times. This enabled instant access to critical insights, such as daily sales performance, customer segments, and website traffic.

Scalability

Druid’s distributed architecture allowed RetailX to easily scale the system as data volumes grew, ensuring consistent performance during peak shopping seasons.

Apache Airflow for Workflow Orchestration

Automated ETL Processes

Apache Airflow was used to orchestrate complex ETL workflows, automating the extraction of data from various sources (e.g., transactional databases, web logs, and third-party APIs), transforming it, and loading it into Apache Druid.

Scheduling and Monitoring

With Airflow, RetailX could schedule daily, hourly, and real-time workflows, ensuring that data was always fresh and up to date. Airflow’s monitoring and alerting features allowed the team to track failures and resolve issues proactively.

Modular Pipelines

Airflow’s DAG (Directed Acyclic Graph) structure provided a modular and scalable approach to workflow creation, making it easy to add new data sources or transformations without disrupting existing processes.

Results:

Significant Cost Savings

By switching from a proprietary BI platform to an open-source solution, RetailX saved over 60% on software licensing costs.
Infrastructure costs were reduced by optimizing hardware usage through the scalability of Apache Druid and the efficient scheduling of resources in Airflow.

Real-Time Insights

With Apache Druid’s real-time data ingestion and low-latency querying capabilities, RetailX could analyze sales trends and customer behavior in near real-time, allowing for more agile decision-making.
Real-time insights helped optimize inventory management, reducing stockouts and overstock situations, which directly improved customer satisfaction and reduced operational costs.

Improved Scalability and Performance

The new BI platform scaled effortlessly to handle increasing data volumes during peak seasons like Black Friday or holiday sales.
Apache Druid’s optimized indexing allowed RetailX to process complex queries on large datasets without performance degradation, even as data volumes grew exponentially.

Enhanced Flexibility and Customization

The open-source nature of Superset, Druid, and Airflow allowed RetailX to customize the platform to meet their specific needs, such as integrating with in-house systems and adding custom features for data visualization.
Developers and analysts could quickly iterate on reports, dashboards, and data pipelines, ensuring that the BI platform evolved in line with the company’s business objectives.

Faster Data-Driven Decisions

The combination of Apache Airflow’s automated workflows and Druid’s fast query engine enabled RetailX to process large datasets more frequently and make data-driven decisions faster than before.
Key metrics such as daily revenue, customer lifetime value, and conversion rates were always accessible, empowering executives and managers to make informed decisions in real time.

Conclusion

By adopting open-source tools like Apache Superset, Druid, and Airflow, RetailX successfully built a robust and scalable business intelligence platform that met the company’s needs for real-time analytics, flexibility, and cost-efficiency. The transformation resulted in significant cost savings, improved operational efficiency, and enhanced data-driven decision-making. RetailX’s experience showcases how open-source technologies can provide a powerful alternative to costly proprietary solutions, enabling companies to build tailored, high-performance systems without breaking the bank.