Andreas Braun shows how to tame Germany’s massive fuel‑price feed with Symfony and MongoDB. The public repository holds roughly 78 GB of daily price CSVs and 10 GB of station data, amounting to almost a billion price rows from around 18 000 pumps. Processing that volume demands careful schema work.

First, Braun inspects the CSVs, noting each station carries a UUID, brand, address and GeoJSON coordinates, while price rows list diesel, e5 and e10 values plus change flags. He adopts MongoDB’s bucket pattern, grouping daily prices per station‑fuel pair and embedding station details to avoid costly joins, then builds the pipeline with the driver’s aggregation builder.

Running the import on a local SSD, Braun loads 582 million price reports in under three hours and completes the full aggregation in about twelve. The resulting collections hold ~17 000 stations (≈1 GB) and 28 million daily buckets (≈24 GB). This structure lets a Symfony front‑end render maps, latest prices and 30‑day trends without scanning the raw dataset.