Explanation:
Moving to the cloud involves a lot of these tasks. Planning for capacity in the cloud differs from planning for capacity in on-premises data centers; TCO calculations are modified because TerramEarth uses services rather than renting or purchasing servers; and OpEx/CapEx allocation is modified as services are consumed rather than by using capital expenditures.
Explanation:
Time-series data is best suited for Cloud Bigtable. It is low-latency, highly available, and economical. It is scalable. The fact that it is a managed service that doesn't need a lot of maintenance work is the best part.
Explanation:
To consume 9 TB per day, multiple load-balanced Compute Engine VMs would be sufficient, and Google Cloud Storage is the least expensive per-byte storage option. Depending on the format, the data may be immediately accessible via BigQuery or shortly after completing an ETL procedure. Thus, while maximizing cost, this approach satisfies both business and technological criteria.
Explanation:
Making the system less vulnerable to hacking, particularly man-in-the-middle assaults across modules increase system security.
Explanation:
If you need to manage and save your secret keys, use a secrets manager. Hackers search GitHub for inexperienced users who don't secure their secrets, and all too frequently, developers take quick cuts and store their secrets in code, forgetting to remove them when they move to production. One of the main assault vectors in the present day is this one!
Explanation:
Replatforming is the process of moving your programs from one platform to another, then optimizing them without having to completely rewrite them. If you already have a large number of VMware virtual machines on your local network, you may simply run them on GCE to take advantage of the platform's advantages.
Explanation:
Since the data is now only collected when the machines are down for maintenance, having cellular connectivity will significantly increase how recent the data is when it is utilized for analysis. Using streaming transport as opposed to recurrent FTP will further tighten the feedback loop. Workloads requiring predictive maintenance are perfect for machine learning.