Google Professional Data Engineer
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You are working for a farming company that utilizes a BigQuery table named 'sensors', which is 500 MB in size and contains information on 5000 sensors, including their id, name, and location. The table is updated hourly, and each sensor generates a metric every 30 seconds, complete with a timestamp that needs to be stored in BigQuery. Your task is to choose the most suitable data model for storing these sensor metrics to facilitate weekly analytical queries while minimizing costs. What would be the best approach?
You are working for a farming company that utilizes a BigQuery table named 'sensors', which is 500 MB in size and contains information on 5000 sensors, including their id, name, and location. The table is updated hourly, and each sensor generates a metric every 30 seconds, complete with a timestamp that needs to be stored in BigQuery. Your task is to choose the most suitable data model for storing these sensor metrics to facilitate weekly analytical queries while minimizing costs. What would be the best approach?
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