Query profiling and optimization in dbt - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is query profiling in dbt?

Query profiling in dbt means checking how long a SQL query takes to run and what parts use the most resources. It helps find slow or costly steps.

beginner

Name one common cause of slow queries in dbt models.

One common cause is missing indexes on columns used in joins or filters, which makes the database scan more data than needed.

intermediate

How can you use dbt to optimize query performance?

You can optimize by breaking big queries into smaller models, using incremental models, and avoiding unnecessary columns or rows in selects.

intermediate

What is the role of the EXPLAIN plan in query optimization?

The EXPLAIN plan shows how the database runs a query step-by-step. It helps find slow parts like full table scans or bad join orders.

beginner

Why is incremental loading useful in dbt for optimization?

Incremental loading updates only new or changed data instead of rebuilding the whole table, saving time and resources.

What does query profiling help you identify in dbt?

A Data quality issues

B Syntax errors in SQL code

C Slow or costly parts of a query

D User access permissions

Query profiling focuses on performance, showing which parts of a query take the most time or resources.

Which dbt feature helps avoid rebuilding entire tables on every run?

A Macros

B Incremental models

C Seeds

D Snapshots

Incremental models update only new or changed data, improving efficiency.

What does the EXPLAIN plan show?

A Query execution steps and costs

B List of tables in the database

C User roles and permissions

D Data schema definitions

EXPLAIN shows how the database runs a query, helping find optimization opportunities.

Which of these can slow down a dbt query?

A Missing indexes on join columns

B Using incremental models

C Selecting only needed columns

D Breaking queries into smaller models

Missing indexes cause the database to scan more data, slowing queries.

What is a good practice to optimize dbt models?

A Use SELECT * in all queries

B Join tables without conditions

C Run all models as full refresh every time

D Avoid selecting unnecessary columns

Selecting only needed columns reduces data processed and speeds up queries.

Explain how you would profile a dbt query and use the results to optimize it.

Think about tools and steps to find and fix slow parts.

Describe the benefits of using incremental models in dbt for query optimization.

Focus on how incremental loading changes the work done.