Our client, a prominent player in the entertainment industry, approached our team with a specific need: to build a robust data warehouse enabling them to extract valuable insights from the vast amount of data generated by sensors and other machine metrics.

To streamline the processes of extraction, transformation, and loading (ETL), we leveraged tools like Apache Airflow. By orchestrating ETL pipelines, we ensured efficient data processing and seamless integration with the data warehouse. This allowed our client to extract actionable insights from sensor data and machine metrics in a timely and automated manner.

To optimize the consumption of this data, we implemented an incremental model and utilized partitioned tables. This approach enabled us to store and retrieve relevant subsets of data efficiently, significantly improving query performance and reducing processing times.

The data-driven insights obtained through our optimized ETL pipelines, combined with the use of an incremental model and partitioned tables, empowered our client to optimize their laser tag systems, enhancing operational efficiency and overall guest experience.