Problem Statement
Variability in data structure coming from different sources, especially semi-structured data from wearable devices, posed challenges for efficient data processing. The company needed a scalable solution to process data from their Apple Watch application and manage growing datasets for analysis.
Approach & Solution
To overcome these data challenges, we designed a flexible data architecture using Google Cloud technologies to handle complex, semi-structured data.
- Integrated Firebase data into BigQuery, organizing millions of data points collected daily from wearable devices into structured formats for easier analysis.
- Leveraged BigQuery's scalability to manage over 200GB of semi-structured transaction data from the Apple Watch app, ensuring seamless storage and accessibility for the ML team.
- Developed Python scripts for processing and cleaning the incoming data streams, automating repetitive tasks to handle real-time data ingestion efficiently.
- Utilized Compute Engine to support advanced statistical analysis and machine learning models, providing a platform for the company's analytics team to build actionable insights.
Results & Outcomes
The newly implemented system significantly improved the company's data processing capabilities. By organizing their data in BigQuery, they now handle millions of daily data points with ease. The automation of data processing tasks reduced manual intervention and allowed the team to focus on deeper analysis, improving the efficiency of their analytics workflows.
Tools & Technologies used
- Firebase
- Python
- Compute Engine
- BigQuery
.png)
.png)
