Google Professional Data Engineer
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You have migrated a data backend for an application that needs to store and manage 10 petabytes (PB) of historical product data for analytics purposes. Note that only the most recent state of each product, amounting to approximately 10 gigabytes (GB) of data, needs to be accessible via an API by other applications. The challenge is to select a cost-effective persistent storage solution that meets both the analytics requirements and ensures API performance that supports up to 1000 queries per second (QPS) with a latency of less than 1 second. How should you proceed?
You have migrated a data backend for an application that needs to store and manage 10 petabytes (PB) of historical product data for analytics purposes. Note that only the most recent state of each product, amounting to approximately 10 gigabytes (GB) of data, needs to be accessible via an API by other applications. The challenge is to select a cost-effective persistent storage solution that meets both the analytics requirements and ensures API performance that supports up to 1000 queries per second (QPS) with a latency of less than 1 second. How should you proceed?
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