start with the idea before the implementation.
the mechanisms you need to reason about.
Joins and cardinality
Joins and cardinality is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
Aggregations
Aggregations is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
Window functions
Window functions is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
Indexes and query plans
Indexes and query plans is studied through intuition implementation evidence and trade-offs. The goal is to be able to explain the mechanism and verify it with a concrete test rather than only repeat a definition.
turn the lesson into evidence.
design three analytical queries
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
compare join strategies
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
inspect an execution plan
Build the smallest version first. Record the input, expected output, measured result and one failure you discovered.
prove you can explain and decide.
avoid accidental row multiplication
ask cortex to test me →explain GROUP BY vs window functions
ask cortex to test me →identify an index candidate
ask cortex to test me →what usually goes wrong.
SELECT * everywhere
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
missing join conditions
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
filtering after an expensive expansion
Detect this early by defining a baseline, a measurable signal and a condition that would cause you to stop or redesign the approach.
Create a short SQL engineering note with one working artifact one metric one failure case and one decision about when you would or would not use it.
Save the result in your portfolio or project repository. A strong learning artifact should make your assumptions, metrics and failure analysis visible.