BigQuery

Solution

Partition on the filter column (usually date). Cluster on high-frequency filters / join keys. Select only needed columns (columnar). Avoid SELECT *. A clustered table still scans the partition; it just reads fewer blocks inside it.

Solution

On-demand: you pay per TiB scanned — great until a JOIN without a partition hits a 20 TB table. Slots / editions: you buy capacity; waste is idle slots, not a surprise scan. Dashboards that refresh every 5 minutes belong on capacity or a materialized table, not on-demand ad-hoc SQL.

Solution

Streaming is for true low-latency rows and costs more. Nightly dumps should be load jobs (or Storage Write API in batch). Querying the streaming buffer has caveats. Do not stream a daily Salesforce extract.

Solution

Authorized views let project B query a restricted slice without owning the data. Copying creates a second source of truth and a stale-ACL problem. Row-level security / policy tags are the next layer when a view is not enough.

Solution

Clustering is not a partition. If you do not filter the partition column, BigQuery still opens every partition and then uses clustering inside those files. Date filter + cluster on customer_id is the usual pair. Clustering alone is not a magic index.

Solution

Materialize the aggregate (or an MV) and/or put repeat BI on capacity. Do not re-scan 4 TB on-demand every refresh.

Solution

Authorized view (or row ACLs) keeps one source of truth. Copies go stale and pick up the wrong ACL.

Solution

Streaming is for low-latency rows. Nightly files are load jobs. You pay more and gain nothing.

Solution

Jobs by user, bytes billed, slot-ms, the query text that scanned the monster. Then partition that table.

Solution

Clustering helps when the filter matches the cluster columns (and the table is large enough). Country is not customer_id. Partition by date, cluster by the selective columns you actually filter.

Solution

A truncate-and-load can show empty or half-written depending on job type. Load with a temp table and swap, or use time-travel/clone. BI should not hit a table you overwrite in place without a transaction story.

Solution

The job ran outside the reservation assignment (wrong project, admin project, or on-demand fallback). Draw the assignment: project → reservation → folder. “We bought slots” is not a policy.

Solution

user_email or principal, query (or labels), and total_bytes_billed / slot_ms. Then look at cache_hit. Without labels, you have a list of people, not teams.

Solution

Streaming is for low-latency rows; you pay a streaming premium and get tiny buffers. Load jobs (or Storage Write API in batches) for dumps. Nightly is not streaming.

Solution

Direct SELECT on raw. The view’s owner needs source access; the analyst gets the view only. Copying the table into their project is how PII spreads.