[ Case Study ]
Transforming Analytics: From Database Blockages to Efficient DWH & Top-Tier BI Dashboards
Coinspaid sought to establish a Business Intelligence (BI) system from scratch, shifting from their previous method which involved running queries directly on the production database. This old method was not only inefficient but also disrupted transactional processes.
Our primary objectives were to construct a data warehouse (DWH) to alleviate analytical load from production databases, facilitate easy migration, and ensure that data control remained entirely within the company.
Unregulated company processes and a dearth of BI expertise.
Designing a system that could efficiently free up analytical load from production databases.
Solution and Technologies
The solution was built using SQL, Vertica, Python, Tableau, Kubernetes, Apache Airflow, and Apache Superset. An architectural project was presented, listing potential tools along with their advantages and disadvantages. After discussions with top management, we spearheaded the development of a data warehouse based on the key requirements.
Developed a database storage project with the ability to easily migrate.
To visualize and analyze the data effectively, we utilized dashboards that informed the company's top management decisions.
Conclusions on the Project
The DWH was successfully developed. Not only did this solution free up the production databases, but it also streamlined the company's analytics and reporting process. All regular reports are now channeled through our DWH. Three new processing systems were integrated. The DWH continues to function seamlessly without any issues a long time after our partnership.
The introduction of the Jira Service Desk facilitated better communication with data users and reduced the workload by around