tailwindlabs / tailwindcss
github.com·23h ago
Developer Tools
Databricks Blog·Jun 25·5 min read
From test bench to lakehouse: how AVL modernizes measurement data analytics with Impulse
TL;DR
Automotive test campaigns generate vast amounts of time-series sensor data, which traditional analysis tools struggle to handle. Impulse, a Python-based analytics library on the Databricks platform, was developed to modernize measurement data analytics.
✦ Why It Matters
Engineers can leverage Impulse for scalable, reproducible analytics of large measurement datasets.
Key Takeaways
- Utilize Impulse for time-series data analysis.
- Adopt lakehouse architecture for better data governance.
- Implement Unity Catalog for data management.
- Explore three usage modes for diverse engineering roles.
Full Summary
Automotive testing generates hundreds of thousands of measurement recordings and terabytes of time-series data, typically analyzed using desktop tools like NI DIAdem or MATLAB. These tools, while user-friendly for domain engineers, do not scale well and lead to isolated analyses that are hard to reproduce.
To address this, AVL implemented Impulse, a Python-based analytics library on the Databricks Intelligence Platform, which integrates with a lakehouse architecture. This architecture follows the Medallion Model, ensuring data governance through Unity Catalog and orchestrating workflows with Databricks Workflows.
Impulse supports three usage modes for domain engineers, data engineers, and data scientists, facilitating collaboration and efficiency. AVL's adoption of this platform has led to significant improvements in data management and analysis, allowing for faster and more reliable insights into vehicle and energy systems.