Automotive Time-Series Formats
I’m comfortable reading, parsing, and working with data in common automotive formats on CAN networks, including ASAM MF4/MDF, FAMOS DAT, and more.
I’m Bryan Wandrych, a Data Engineer and Analytics Engineer in Metro Detroit. I build Python and SQL pipelines that turn large-scale automotive measurement data into queryable Delta tables in Microsoft Azure and Azure Databricks. The data includes ASAM MDF/MF4 files. At Daimler Truck North America, I’ve built systems that process 500,000+ logger files and 262,000+ signals at 10 Hz. Outside work, I maintain open-source projects and a self-hosted homelab.
I’m comfortable reading, parsing, and working with data in common automotive formats on CAN networks, including ASAM MF4/MDF, FAMOS DAT, and more.
I run my own servers: a Raspberry Pi rack, mini PCs, and self-hosted media, files, and remote desktop. Everything is secured and reachable from anywhere.
I run local models on my own hardware and use agentic coding to prototype quickly. I still prefer to write my own code.
Most of my day job is turning raw vehicle measurement data into datasets that engineers and analysts can query. The V.O.L.T.S pipeline turns 500,000+ MF4 logger files into one Delta table with 262,000+ signals at 10 Hz. The File Converter reads about 30 input formats and exports 24. These are the systems I’ve built at Detroit Diesel, along with the open-source projects I maintain on my own time.
A production pipeline that turns testing-fleet MF4 logger files into one cloud-queryable dataset. It parses hundreds of thousands of files, aligns and resamples signals onto global timestamps, and loads structured Delta tables into Azure Databricks. I designed and built the whole system.
A company-wide time-series converter that reads about 30 input formats and exports 24. It routes files by content, merges and cuts recordings, decodes frame-based data with DBC and A2L files, and ships with a desktop GUI for non-technical users. It started as a side project and now powers the V.O.L.T.S pipeline.
Outside of work, I build Playdate browsers in C and Lua, a Flask remote-desktop proof of concept, and a soil-infiltrometer field app used for research. Every public repository is on GitHub.
I care about pipelines that are boring to operate: clear failure modes, observable progress, and outputs that analysts can trust. That preference shows up in the converter’s fallback ladder, the pipeline’s memory-aware workers, and the metadata tables that make every load traceable. The Work page goes deeper into the architecture and tradeoffs behind each system.
I’m a Data Engineer who works with large-scale automotive time-series data. I build pipelines that move measurement data from on-premises systems into Microsoft Azure and Azure Databricks, so analytics teams and downstream engineering applications can use it.
I build end-to-end data pipelines for automotive measurement files, including ASAM MDF/MF4 and other formats. That covers ingestion, transformation, and cloud integration. The result is structured Delta tables that teams can query for their own analytics and engineering work.
Most of my work is in Python and SQL, with Microsoft Azure, Azure Databricks, and Delta Lake. I use them to build scalable data pipelines and make large time-series datasets easy to query and reuse.
ASAM MDF is an automotive measurement format, usually stored as .mf4 files. It records time-series signals from CAN buses, ECUs, and test instrumentation. I parse, convert, and resample that data into Delta tables that are ready for the cloud.
The Work page covers my projects and the engineering behind them. You can also find my code on GitHub and my background on LinkedIn.
Send me a message through the Contact page. I’m always happy to connect with other engineers, talk about technology, or hear about interesting opportunities.