Purple Lotus, a dispensary of cannabis products, was looking for a way to clean up its data pipelines to make business process improvements. The company, which generates a massive volume of data, wanted to derive meaningful insights from them, and – to do that – their data infrastructure had to work flawlessly.
The company’s operations cover selling – both via offline and online channels – cannabis products and the whole logistics, including home delivery. These, combined with the medical nature of offered products and their variety, caused a massive amount of data to be digested to get valuable business insights.
Meet Our Client
Purple Lotus, a dispensary of cannabis products, was looking for a way to clean up its data pipelines to make business process improvements. The company, which generates a massive volume of data, wanted to derive meaningful insights from it, and – to do that – their data infrastructure had to work flawlessly.
The company’s operations cover selling, both via offline and online channels, cannabis products and the whole logistics, including home delivery. These, combined with the medical nature of the offered products and their variety, caused a massive amount of data to be digested to get valuable business insights.
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Case Study Shortcut
Challenge
It didn’t handle data volume growth
Historical data sometimes got “lost in translation” and happened to be labeled or classified incorrectly
There were no integrations with external data sources, or they didn’t meet the expectations, no API, etc.
Goal
The goal was to:
- Improve Data Infrastructure
- Enhance Business Insights and Decision Making
- Set the Foundation for Future Enhancements
Outcome
The solutions delivered by Addepto for Purple Lotus not only met but exceeded the initial expectations, especially considering it was only a Minimum Viable Product (MVP). This success lays the groundwork for further enhancement of the system’s capabilities. The planned expansion includes integrating advanced data orchestration and data observability features.
The addition of data orchestration will streamline and automate the process of managing complex data workflows. This will ensure more efficient and error-free data handling, allowing Purple Lotus to seamlessly integrate data from various sources and formats. This integration is crucial for synthesizing a comprehensive view of the business landscape, leading to more informed decision-making.
Before
- Data Infrastructure Limitations
- Limited Integration Capacity
- Inefficiencies in Data Management
After
- Robust Data Infrastructure
- Seamless Data Integration
- Enhanced Analytical Capabilities
- Foundation for Future Growth
Integrate those solutions in your company
Case Study Details
Approach
Building a Modern, Scalable Data Warehouse
- Addepto developed a new data warehouse architecture using Google BigQuery to centralize and efficiently manage large volumes of business data.
- Historical and real-time data from various sources were migrated and organized for structured, high-performance querying.
Implementing AirByte Pipelines with Custom Connectors
- The team used AirByte, a modern open-source data integration tool, to set up automated data ingestion pipelines.
- Custom connectors were developed to ensure compatibility with Purple Lotus’s unique systems and external sources.
Deploying on a Kubernetes Cluster for Flexibility and Scalability
- The infrastructure was deployed on a Kubernetes cluster, ensuring scalability, reliability, and orchestration of various services and data workflows.
Enhancing Business Intelligence with Looker
- Looker was integrated as the BI visualization layer, providing real-time dashboards and customized reports for better business insight and decision-making.
- Teams could now explore and analyze data independently, reducing dependency on data engineering support.
Laying the Groundwork for AI and Data Orchestration
- The MVP included GPT-driven enhancements and was designed with future AI capabilities and data observability in mind.
- This future-proof design enables easy scaling and integration of AI-driven analytics and automation in the next development phases.
