Google Professional Data Engineer
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As a data engineer at a farming company, you manage a BigQuery table named "sensors," which is approximately 500 MB in size. This table includes 5000 sensors with columns for id, name, and location and is updated every hour. Each sensor produces a metric every 30 seconds, accompanied by a timestamp, and you need to store these metrics in BigQuery for analysis. You plan to run an analytical query on the collected data once a week for monitoring purposes. Given these requirements and the goal of minimizing costs, what data model should you implement?
As a data engineer at a farming company, you manage a BigQuery table named "sensors," which is approximately 500 MB in size. This table includes 5000 sensors with columns for id, name, and location and is updated every hour. Each sensor produces a metric every 30 seconds, accompanied by a timestamp, and you need to store these metrics in BigQuery for analysis. You plan to run an analytical query on the collected data once a week for monitoring purposes. Given these requirements and the goal of minimizing costs, what data model should you implement?
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