Snowflake

Solution

Size doubles credits per hour. If Medium takes only a little longer, total credits drop. Single-threaded SQL and tiny result sets will not scale with size — you just pay more for the same clock. Test one size down on a real job and compare credits, not elapsed time alone.

Solution

Query History for the last good timestamp. SELECT count(*) … AT (TIMESTAMP => …). CLONE to a _fix table and diff. Do not UNDROP as the first move if writers are live — you can race a reload. Time Travel is a restore window, not a backup policy. Fail-safe is Snowflake-only and not queryable.

Solution

Exact same query + role + unchanged source data → result cache (no warehouse). Warehouse local disk cache helps repeats on the same WH after it stayed warm. Metadata (services layer) makes COUNT(*) and MIN/MAX cheap. If auto-suspend is 60s and the dashboard ticks every 5 minutes, you pay resume every time.

Solution

No. Materialize once, serve many times. Explore refresh is a token bill and a non-deterministic answer. Classify or extract in an incremental model, store the label, BI reads the column. Cortex is an ELT step, not a metric.

Solution

Natural ingestion order already clusters some tables (time-series). A clustering key helps when queries filter a column that is scattered across partitions (high clustering depth). It costs background credits. Check SYSTEM$CLUSTERING_INFORMATION before you turn it on. Search Optimization is a different product for point lookups.

Solution

Streams + Tasks are fine for a few in-Snowflake incremental loads. Once you have 30 jobs, cross-system dependencies, or a human on-call rotation, put Airflow / Dagster on top and keep Tasks as the SQL worker. Do not invent a second orchestrator inside Snowflake “just because it is native.”

Solution

A single COPY INTO is transactional per file set: failed files can be skipped with ON_ERROR, but committed files stay. Know VALIDATION_MODE, PURGE, and force vs load history. Interviewers want “I check COPY_HISTORY, I do not assume all-or-nothing across a folder of 400 files.”

Solution

Clustering helps range/filter scans on poorly ordered columns. Search Optimization helps highly selective point lookups. They cost extra credits. Do not buy both “just in case.”

Solution

Clone is cheap at t0. Storage grows as either side writes. Time Travel on the clone is not a backup of prod. Drop the clone when the test ends.

Solution

Share when both sides are Snowflake and you want live data. Export when the consumer is not Snowflake, needs a file SLA, or cannot be a reader account.

Solution

Dynamic Tables when the graph is SQL and you want a lag target. Streams+Tasks when you need custom Task logic or an external orchestrator already owns the clock. Do not run both on the same table without a reason.

Solution

In incremental ELT that writes a versioned column. Not in a Looker explore, not on every dashboard refresh, and not on raw PII.

Solution

The semantic model grain and joins — same fan-out you would debug in SQL. A bigger warehouse will not fix a bad metric definition.