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Google Cloud Certified Professional Cloud Architect · Google Cloud
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A. Load logs into Google BigQuery: BigQuery is Google Cloud's serverless, highly scalable, and cost-effective multicloud data warehouse designed for data analytics. It's ideal for storing and analyzing large volumes of log data (100 TB in this case). You can use BigQuery's powerful SQL capabilities to run queries, generate reports, and gain insights from your logs. E. Upload log files into Google Cloud Storage: Cloud Storage provides durable, scalable, and secure object storage. It's perfect for storing your log data as a long-term disaster recovery backup. Cloud Storage offers different storage classes to optimize costs based on your data access frequency and retention needs.
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AE To archive approximately 100 TB of log data to the cloud and test the analytics features available while also retaining the data as a long-term disaster recovery backup, you can take the following steps: E: Upload log files into Google Cloud Storage: Google Cloud Storage is a scalable, durable, and fully-managed cloud storage service that can be used to store large amounts of data. You can upload your log files to Cloud Storage to archive them in the cloud. A: Load logs into Google BigQuery: Google BigQuery is a fully-managed, cloud-native data warehouse that can be used to analyze large amounts of data quickly and efficiently. You can load your log data into BigQuery to perform analytics on it and test the available analytics features. Other options, such as loading logs into Google Cloud SQL, importing logs into Google Stackdriver, or inserting logs into Google Cloud Bigtable, may not provide the necessary functionality for archiving and analyzing the log data.