Join behavior is fundamental in analytics work because it directly affects row counts, missing data handling, and metric accuracy. In transaction analysis, choosing the wrong join can silently exclude valid records or introduce unexpected NULL values.
Explain the difference between an INNER JOIN and a LEFT JOIN in SQL. Then use a simple transaction example to explain a many-to-one relationship, such as many card transactions mapping to one cardmember account or one merchant category.
You should describe what rows each join returns, how unmatched rows are handled, and when each join is appropriate. For the relationship example, focus on how the keys connect the tables and why analysts often aggregate transaction-level data up to the account or merchant level.
Join behavior is fundamental in analytics work because it directly affects row counts, missing data handling, and metric accuracy. In transaction analysis, choosing the wrong join can silently exclude valid records or introduce unexpected NULL values.
Explain the difference between an INNER JOIN and a LEFT JOIN in SQL. Then use a simple transaction example to explain a many-to-one relationship, such as many card transactions mapping to one cardmember account or one merchant category.
You should describe what rows each join returns, how unmatched rows are handled, and when each join is appropriate. For the relationship example, focus on how the keys connect the tables and why analysts often aggregate transaction-level data up to the account or merchant level.
