Need to interpolate new time series data values over 5 billion rows? Don’t reach for python. Make that Google’s problem and do it in BigQuery.
Need to aggregate petabytes of geospatial data across arbitrary polygons and put it on a map for analysis? Make that Google’s problem and use BQ-GIS.
Great – your map was awesome and now we need it every hour. Roll your own Airflow server? Nope. Make that Google’s problem. You see the pattern.
Geotab, which operates in in the telematics space, applies instrumenting to vehicles to learn how to optimize fleet maintenance, routes and costs, and even find way to optimize city infrastructure.
Geotab has been a long-time customer of Google Cloud products. They leverage the entire GCP suite to empower data scientists to efficiently ingest, process, and analyze petabytes of IoT data.
One of the keys to their pace of innovation and growth has been to focus on their key competencies and partner with others to fill in the gaps.
For Geotab’s data scientists this means focusing on the data and model-building and whenever possible making the processing, storage, and orchestration, well Google’s problem.