Your organization requires that metrics from all applications be retained for 5 years for future analysis in possible legal proceedings. Which approach should you use?
A.
Grant the security team access to the logs in each Project
B.
Configure Stackdriver Monitoring for all Projects, and export to BigQuery
C.
Configure Stackdriver Monitoring for all Projects with the default retention policies
D.
Configure Stackdriver Monitoring for all Projects, and export to Google Cloud Storage
+1. For archival purposes, Customer should use Cloud Storage. BigQuery is a datawarehouse, and could eventually import data from Cloud Storage if necessary.
A and C can be quickly ruled out because none of them is solution for the requirements "retained for 5 years"
Between B and D, the different is where to store, BigQuery or Cloud Storage. Since the main concern is extended storing period, D (Correct Answer) is better choice, and the "retained for 5 years for future analysis" further qualifies it, for example, using Coldline storage class.
With regards of BigQuery, while it is also a low-cost storage, but the main purpose is for analysis. Also, logs stored in Cloud Storage is easy to transport to BigQuery or do query directly against the files saved in Cloud Storage if and whenever needed.
Bigquery long term storage cost: $0.020 per GB
Cloud Storage archive cost: $0,0012 per GB
Only if metrics need less than 10 GB (free service part on Bigquery) then the correct solution will be B... But all metrics for all applications during more than 5 years... I think never will be the case :D
This is a good example. thanks.
But, we can easily change that implementation to dump the metrics to buckets to save lots of money. And, when talking about legal purpose, 1 hour interval may not be enough. You may have to keep more frequent metrics. So, only cold line or archive work for that purpose.
B in my opinion.
Take a look: https://cloud.google.com/architecture/monitoring-metric-export#store_metrics
BQ is also long-term storage with option to reduce cost of older data.
https://cloud.google.com/bigquery/docs/best-practices-storage
1. Long-term Retention: BigQuery is a data warehouse designed for long-term storage and analysis of large datasets. It's the ideal place to store metrics for 5 years to meet your organization's legal requirements.
2. Cost-Effective: BigQuery's storage pricing is very competitive, especially for long-term data retention.
3. Analysis and Reporting: BigQuery provides powerful tools for analyzing and querying data, making it easy to extract insights and generate reports from the stored metrics.
4. Integration: Stackdriver Monitoring (now Cloud Monitoring) can be easily configured to export metrics to BigQuery.
D is not correct as while Cloud Storage can store data for long periods, it's not optimized for querying and analyzing data like BigQuery.
I go for B, as the question is about 5 years worth of data "for future analysis in possible legal proceedings", and the "future" can be next day, based on when the legal proceeding happen.
It is not about long term log storage.
Even the argument of "future" means 100 years later, the Cold Storage Archival still does not fulfill the "analysis" portion of the requirements.
You will need to move the data from Cold Storage to BigQuery for the analysis.
So the ideal answer should be combination of D and B, but we do not have such option, hence the answer can meet all requirements is B.
The statement is not about the actual analysis of the data, but 'where' to store the data for future analysis. Who know when that will be??? So GCS is the best answer. When need be, it can be import into BQ
I mean, "for possible future legal proceedings", I think that immutable storage that grants data integrity is the best option here, what's more, it's also the cheapest one...
I think B is the right answer because transferring the data could be a basis for discrediting the data for legal use. Since BIg Query can store the data and retain it with all the metadata intact, I will go for it.
This should be D. The tool lists option B as correct option which doesn't sound right. Bigquery export is fine for analysis but not for long term storage for 5 yrs.
D - Correct
B. Configure Stackdriver Monitoring for all Projects, and export to BigQuery: This approach can work, but it may be expensive since BigQuery charges for the storage and processing of data. Additionally, you may need to configure a specific retention policy to retain the data for 5 years, which can further increase the cost.
D. Configure Stackdriver Monitoring for all Projects, and export to Google Cloud Storage: This approach is the most appropriate because it allows for the configuration of retention policies that meet the requirement of retaining metrics for 5 years. Also, Google Cloud Storage is a cost-effective solution for long-term data storage. Exporting the logs to Google Cloud Storage can be automated and scheduled for regular intervals, reducing the manual effort required to ensure compliance with the retention policy. The exported logs can then be analyzed using various tools, including BigQuery, if needed.
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