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Databricks Lakehouse for Automotive Data: How AVL Modernizes Vehicle Testing

Databricks16 June 2026Watch on YouTube

Description

The car is becoming a device that updates like your phone, so the idea of "fully testing" one before it ships is quietly falling apart. How do you validate a vehicle that adapts every minute it's on the road? In episode 2 of Data + AI Exchange, Viktoria Semaan sits down at AVL's headquarters in Graz with VP of Corporate Strategy Georg List and Director of Global Enterprise Architecture Andreas Juffinger to unpack how the automotive industry's shift to software-defined vehicles created a data explosion and how AVL and Databricks built an open-source lakehouse for measurement data to handle it. ⏱️ Inside the conversation: 00:00 – The Paradigm Shift in the Automotive Industry 01:01 – What Does AVL Do? 02:25 – Navigating Industry Transformation and Data Challenges 05:25 – Innovation in RaceTech and its Transfer to Traditional Cars 07:47 – The Future Vision: Adaptive Cars and AI-Driven Testing 09:20 – Technical Walkthrough: Building a Lakehouse for Measurement Data 12:19 – The Power of the Impulse Framework 14:52 – Data Governance, Quality, and Democratization 17:33 – Future Outlook: Agents, Generative AI, and Getting Started 🔗 Resources to get started: Impulse repo: https://github.com/databrickslabs/impulse Docs: https://databrickslabs.github.io/impulse/

What you'll learn

  • Software-defined vehicles generate a data explosion that traditional systems cannot manage, pushing AVL and Databricks toward an open-source lakehouse solution.
  • The Impulse framework enables large-scale measurement data from vehicle tests to be stored, accessed, and analyzed in a structured way.
  • Data governance and quality are critical in automotive testing, as continuously updating cars require constant validation and real-time monitoring.
  • AVL integrates innovation from motorsport and race tech into testing methodologies for consumer vehicles, where AI and machine learning increasingly play a role.
  • A lakehouse architecture combines the flexibility of data lakes with the structure of data warehouses, simplifying management of heterogeneous automotive data.

Frequently asked questions

What is the core challenge that automotive companies like AVL face with software-defined vehicles?
Cars are becoming devices like phones, with regular software updates that happen after the car ships. This makes 'fully testing' a vehicle before it leaves the factory nearly impossible, as the car constantly undergoes changes. The volume of test data therefore explodes exponentially.
What does the Impulse framework do and why is it important for AVL?
Impulse is an open-source framework specifically designed to manage large volumes of measurement data from vehicle tests. It helps AVL store test data in a structured way, make it easily accessible, and analyze it, which is essential for validating continuously updating vehicles.
How does a lakehouse architecture differ from traditional data management systems?
A lakehouse combines the flexibility of data lakes (where you store unstructured data) with the structure and governance of data warehouses. This enables efficient management of automotive data with varying formats and complexity without compromising data quality.
What role do AI and machine learning play in the future of automotive testing?
As cars now continuously update, AI-driven testing methods and adaptive testing become crucial. AVL develops methodologies where machine learning helps recognize patterns in test data and intelligently guide vehicle validation, similar to how they implement innovation from motorsport.

Topics

data engineeringlakehouse architectureautomotive aidata governanceenterprise ai