Extract optimization in Tableau - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main purpose of extract optimization in Tableau?

Extract optimization improves performance by reducing the size and complexity of data extracts, making dashboards faster and more responsive.

beginner

How does filtering data before creating an extract help optimization?

Filtering data limits the extract to only the necessary rows, reducing extract size and improving query speed.

intermediate

What is the benefit of using incremental refresh in Tableau extracts?

Incremental refresh updates only new or changed data, saving time and resources compared to full refreshes.

beginner

Why should unused fields be excluded from Tableau extracts?

Removing unused fields reduces extract size and speeds up data processing and visualization.

intermediate

What role does aggregation play in extract optimization?

Aggregating data before extract creation reduces the number of rows, which improves performance and lowers extract size.

Which method helps reduce Tableau extract size?

A Using full refresh every time

B Adding all available fields

C Filtering unnecessary rows

D Duplicating data sources

Filtering unnecessary rows limits data to what is needed, reducing extract size and improving performance.

What does incremental refresh do in Tableau extracts?

A Refreshes all data every time

B Deletes old data permanently

C Creates a backup of the extract

D Updates only new or changed data

Incremental refresh updates only new or changed data, saving time and resources.

Why exclude unused fields from an extract?

A To slow down queries

B To reduce extract size and improve speed

C To increase extract size

D To add more complexity

Excluding unused fields reduces extract size and speeds up data processing.

Which action can improve dashboard load time in Tableau?

A Using aggregated data in extracts

B Including all raw data

C Avoiding filters

D Using live connections only

Aggregated data reduces rows and speeds up dashboard loading.

What is a key benefit of extract optimization?

A Faster data queries and dashboards

B Slower report refresh

C Larger extract files

D More complex data models

Optimized extracts improve query speed and dashboard responsiveness.

Explain how filtering and aggregation help optimize Tableau extracts.

Think about how less data means faster processing.

Describe the difference between full refresh and incremental refresh in Tableau extracts and why incremental refresh is beneficial.

Consider how often data changes and how much needs updating.