Evaluate Current State22% of the exam
Database tuning, extracts and relationship performance options
Find where an existing data structure loses time and pick the least invasive fix: isolate the network from the database, enable referential integrity and join culling, index and permission the database, extract files and custom SQL, slim extracts, and set relationship Performance Options only when the data's shape is known.
Evaluate existing data structures for performance risks and enhancement opportunities.
~7 min read · 10 practice questions ·
What the exam expects
- Isolate whether slowness comes from the network or the database before changing the design.
- Recommend database-side fixes: referential integrity for join culling, indexes, temp table permissions and spool space, and a database server instead of Access.
- Recommend extracts for file data and unavoidable custom SQL, and slim an extract with filters and hidden fields.
- Set relationship Performance Options (cardinality, referential integrity) only when the data's shape is known.
- Rewrite cross-table switching calculations to switch between aggregates.
Key terms:
- Join culling: querying only the joined tables a view needs.
- Referential integrity: every row in one table has a matching row in the other.
- Cardinality: how unique the values in the relationship fields are.
- Extract: a compressed snapshot of data, loaded into memory as needed.
- Temp tables: connection-specific tables Tableau creates in the database to improve performance.
Key facts
Join culling condition
- Detail
- Occurs only where referential integrity is defined
Assume Referential Integrity
- Detail
- Affects performance only on Tableau's end
Access indexing threshold
- Detail
- Tables with more than 200,000 rows
Access storage
- Detail
- Officially 2 GB, poor performance well below it
Native versus ODBC drivers
- Detail
- Native often significantly faster
Custom SQL
- Detail
- Tableau can't optimize it; extract so it runs once
Hidden fields
- Detail
- Not included when you create an extract
Default Performance Options
- Detail
- Many-to-Many; Some Records Match
Cardinality One on non-unique values
- Detail
- Duplicate aggregate values
Fast data types
- Detail
- Numbers and Booleans; strings and dates are slow
Sources for this section (5)
- Why is defining referential integrity in the database better than Tableau's Assume Referential Integrity?
- When does Tableau itself prompt you to extract a text file?
- How does Tableau mark Performance Options it found in the data?
- When are extract filters, aggregation, Top N and sampling unavailable?
The questions are free to read. Answers with their sources and self-grading are included with the pack.
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