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Answer: Use the BigQuery streaming to stream changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
The best approach is to use BigQuery streaming to stream the inventory changes into a daily inventory movement table. This method allows for near real-time updates with low latency, keeping the inventory movement table constantly updated. Calculating balances in a view that joins the movement table with the historical balance table provides an accurate view of current inventory levels without frequently updating the main balance table. Updating the main inventory balance table nightly ensures long-term data consistency while maintaining efficiency. Other options are less suitable due to inefficiencies and limitations in handling several thousand updates per hour.
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You have been tasked with creating a near real-time inventory dashboard that reads from the main inventory tables in your BigQuery data warehouse. The historical inventory data is stored in the form of inventory balances categorized by item and location. The inventory system receives several thousand updates every hour. Your goal is to maximize the performance of the dashboard while ensuring the accuracy of the data. What steps should you take to accomplish this?
A
Leverage BigQuery UPDATE statements to update the inventory balances as they are changing.
B
Partition the inventory balance table by item to reduce the amount of data scanned with each inventory update.
C
Use the BigQuery streaming to stream changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
D
Use the BigQuery bulk loader to batch load inventory changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.
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