if you plan to store extensive historical data for a large number of remote-sensing devices and then use the data to generate daily reports, the cost savings for HDD storage might justify the performance tradeoff. On the other hand, if you plan to use the data to display a real-time dashboard, it probably would not make sense to use HDD storage-reads would be much more frequent in this case, and reads that are not scans are much slower with HDD storage.

https://cloud.google.com/architecture/migration-to-google-cloud-transferring-your-large-datasets#online_versus_offline_transfer

https://cloud.google.com/compute/docs/disks/resize-persistent-disk#resize_partitions

https://cloud.google.com/spanner/docs/schema-design D because high cardinality means you have more unique values in the collumn. That's a good thing for a hot-spotting issue. E because Spanner specifically has this feature to reduce hot spotting. Basically, it generates unique values https://cloud.google.com/spanner/docs/schema-design#bit_reverse_primary_key D) Promote high-cardinality attributes in multi-attribute primary keys.
This is a correct answer because promoting high-cardinality attributes in multi-attribute primary keys can help avoid hotspots in Cloud Spanner. High-cardinality attributes are those that have many distinct values, such as UUIDs, email addresses, or timestamps1. By placing high-cardinality attributes first in the primary key, you can ensure that the rows are distributed more evenly across the key space, and avoid having too many requests sent to the same server2.
E) Use bit-reverse sequential value as the primary key.
This is a correct answer because using bit-reverse sequential value as the primary key can help avoid hotspots in Cloud Spanner. Bit-reverse sequential value is a technique that reverses the bits of a monotonically increasing value, such as a timestamp or an auto-incrementing ID1. By reversing the bits, you can create a pseudo-random value that spreads the writes across the key space, and avoid having all the inserts occurring at the end of the table2.

PgBouncer maintains a pool for connections for each database and user combination. PgBouncer either creates a new database connection for a client or reuses an existing connection for the same user and database. + PgBouncer is a simple PostgreSQL connection pool that allows for several thousand connections at a time. Using Kubernetes Engine to run a Helm Chart w/ PgBouncer based on the great article from futuretech-industries, we were able to set up an easily deployable system to get the most out of our CloudSQL DBs without breaking the bank. https://medium.com/google-cloud/increasing-cloud-sql-postgresql-max-connections-w-pgbouncer-kubernetes-engine-49b0b2894820#:~:text=That%20is%20where,breaking%20the%20bank.