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Answer: Send the data to Google Cloud Pub/Sub, stream Cloud Pub/Sub to Google Cloud Dataflow, and store the data in Google BigQuery.
Option B is the correct answer. Using Google Cloud Pub/Sub allows for efficient ingestion and real-time data streaming. Google Cloud Dataflow can process and transform the streaming data in real-time, while Google BigQuery is a fully managed, highly scalable data warehouse suited for real-time analysis and querying of large datasets. This combination makes it the most suitable solution for processing, storing, and analyzing very large datasets in real time.
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You are about to deploy 10,000 new Internet of Things (IoT) devices across multiple warehouses around the world. These devices will continuously collect temperature data, resulting in a significant volume of data that needs to be processed, stored, and analyzed in real-time. Considering the scale and the need for real-time insights, how should you approach the processing, storage, and analysis of these large datasets?
A
Send the data to Google Cloud Datastore and then export to BigQuery.
B
Send the data to Google Cloud Pub/Sub, stream Cloud Pub/Sub to Google Cloud Dataflow, and store the data in Google BigQuery.
C
Send the data to Cloud Storage and then spin up an Apache Hadoop cluster as needed in Google Cloud Dataproc whenever analysis is required.
D
Export logs in batch to Google Cloud Storage and then spin up a Google Cloud SQL instance, import the data from Cloud Storage, and run an analysis as needed.
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