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1.3 Combining Data: Data Blending vs. Cross-Database Joins

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Salesforce Certified Tableau Data Analyst Exam

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1 Chapter 1: Connecting to Data & The Logical Data Model

1.1 Data Connections: Live Connections vs. Hyper Extracts 1.2 The Tableau Data Model: Relationships vs. Physical Joins 1.3 Combining Data: Data Blending vs. Cross-Database Joins

2 Chapter 2: Tableau Prep Builder & Data Wrangling

2.1 Tableau Prep Architecture & Data Cleaning 2.2 Reshaping Data in Prep: Pivots, Splits & Wildcard Unions 2.3 Joins, Aggregations & Output Destinations in Prep

3 Chapter 3: Data Quality, Metadata & Field Configuration

3.1 Data Types, Roles, Organization & Default Properties 3.2 In-Desktop Data Cleaning: Splits, Data Interpreter & Source Replacement 3.3 Managing Slowly Changing Dimensions & Data Quality Warnings

4 Chapter 4: Tableau Fundamentals & Calculations

4.1 Dimensions vs. Measures & Discrete vs. Continuous 4.2 Mastering Dates: Discrete Date Parts vs. Continuous Date Truncations 4.3 Calculations: Logic, Strings, Nulls & Type Conversion 4.4 Aggregation Rules, Granularity & Dealing with the Asterisk (ATTR)

5 Chapter 5: Level of Detail (LOD) Expressions

5.1 Level of Detail Architecture & Scoping Syntax 5.2 FIXED LOD Expressions: Scoping & Baseline Benchmarks 5.3 INCLUDE & EXCLUDE LOD Expressions: Dynamic Granularity 5.4 Real-World LOD Patterns: Cohorts, First/Last Dates & Binning

6 Chapter 6: Table Calculations & Directionality

6.1 Table Calculation Mechanics: Addressing vs. Partitioning 6.2 Essential Table Calculations: Running, Window & Percent of Total 6.3 Navigation & Ranking Functions: LOOKUP, INDEX & RANK 6.4 Secondary & Nested Table Calculations: Multi-Level Scoping

7 Chapter 7: Statistical Analytics, Filtering & Order of Operations

7.1 The Tableau Order of Operations: From Extract to Display Filters 7.2 Sets, Set Actions & Combined Sets vs. Groups 7.3 Parameters, Parameter Actions & Dynamic What-If Analysis 7.4 Analytics Pane: Totals, References, Trends & Forecasts

8 Chapter 8: Chart Types & Comparative Visualizations

8.1 Comparison Visualizations: Bar Charts, Highlight Tables & Bullet Graphs 8.2 Part-to-Whole & Correlation Charts: Treemaps, Scatter Plots & Bubbles 8.3 Trend & Distribution Visualizations: Dual-Axis, Combos & Histograms

9 Chapter 9: Advanced Visualizations & Spatial Analysis

9.1 Specialized Visualizations: Waterfall, Gantt, Sparklines & Bump Charts 9.2 Spatial Data & Mapping: Geographic Layers & Spatial Functions 9.3 Visual Polish: Custom Palettes, Annotations & Viz in Tooltip

10 Chapter 10: Dashboard Design & Interactive Actions

10.1 Dashboard Layout, Devices & Visibility 10.2 Filter, Highlight & URL Actions 10.3 Advanced Interactivity: Parameter, Set & Navigation Actions 10.4 Stories & Visual Storytelling: Sequencing Analytical Narratives

11 Chapter 11: Tableau Server & Cloud: Architecture & Publishing

11.1 Tableau Server & Cloud: Sites, Projects, Roles & Permissions 11.2 Publishing Workbooks, Data Sources & Flows

12 Chapter 12: Data Governance, Security & Operations

12.1 Certification, Data Quality, Lineage & Permissions 12.2 Row-Level Security, Schedules, Subscriptions & Alerts

1.3 Combining Data: Data Blending vs. Cross-Database Joins

Key Takeaways Explain This Topic AI

Cross-database joins combine supported connections at row level inside one data source before visualization aggregation.

Blending queries primary and secondary sources separately and links their aggregated results for the view.

The primary source controls the domain of marks; an ATTR asterisk signals multiple secondary values at the current grain.

Secondary COUNTD and MEDIAN are conditionally supported when the required primary linking field is in the view and incompatible secondary detail is absent.

Tableau 2026.2 can compose supported published data sources with relationships; blending is no longer the only possible method in every case.

Last updated: September 2026

1.3 Combining Data: Data Blending vs. Cross-Database Joins

Tableau can combine data at different stages of the analytical pipeline. The important distinction is whether records become part of one data source before the view is aggregated or whether already aggregated results from separate data sources are combined for a worksheet.

Cross-database joins

A cross-database join places tables from supported connections inside one Tableau data source and joins them in the physical layer. The join operates at row level before the measures in the view are aggregated. This permits row-level calculations across the joined fields and supports inner, left, right, or full outer joins where the connectors allow them.

The usual risks of physical joins still apply. A one-to-many or many-to-many match can duplicate measures. An inner join can remove unmatched rows. A left join retains every left row but introduces nulls for unmatched right rows. Verify row counts and key totals before and after the join, and use relationships instead when tables have different grains and should retain their own level of detail.

Cross-database support depends on the connector and topology. Tableau may move data between systems or use local resources to complete a federated query, so large joins can incur transfer and compute costs. Prefer a governed warehouse model when the combination is broadly reused or too large for practical federation.

Data blending

Data blending keeps separate data sources. The first field placed in a worksheet establishes the primary source, marked with a blue check. A secondary source is marked with an orange check. Tableau queries each source and links the secondary result to the primary result through one or more fields, shown by active link icons. The primary source determines the domain of marks, so blending behaves conceptually like a left-side combination: secondary values with no matching primary mark do not create new marks by themselves.

Blending occurs after aggregation. That makes it useful when the sources cannot be joined directly, when a cube or another constrained source is involved, or when a small secondary source supplies targets at a different grain. It also creates important restrictions. Secondary fields are aggregated in the view, linking fields must have compatible data types and comparable values, and null or differently formatted keys do not match.

An asterisk produced by ATTR indicates that more than one secondary dimension value contributes to a primary mark. It is a grain diagnostic, not a literal value. Add the necessary linking field to the view, correct the relationship between grains, or aggregate to the intended level rather than replacing the asterisk with an arbitrary label.

Non-additive aggregates in a blend

COUNTD and MEDIAN from a secondary source are not categorically forbidden. Tableau can support these non-additive aggregates when the view includes the primary linking field and does not also use a nonaggregated secondary dimension that makes the required level ambiguous. If Tableau reports that a field cannot be used, expose the active linking field in the primary view, remove incompatible secondary detail, or reconsider which source should be primary. Treat the precise fields and grain in the scenario as decisive.

Published data sources

Older guidance often states that two published data sources must always be blended. Current Tableau 2026.2 capabilities allow authors to relate supported published data sources to one another and publish the resulting composite source when permissions and connector conditions are met. Blending remains available and may still be required for unsupported combinations or view-specific comparisons. When answering a version-neutral scenario, inspect whether it explicitly describes separate primary and secondary sources, link icons, or a supported composed data source.

Choosing the method

  • Requirement Prefer
  • Row-level calculations across sources One data source with a supported cross-database join
  • Preserve separate grains inside one source Relationships
  • Combine aggregate results from separate sources in one sheet Data blending
  • Reusable governed combination Warehouse model, relationship model, or supported composite published source
  • Keep every value from the secondary source Make it primary or use a row-level model with the needed join type
  • Example: sales and targets

Sales contains one row per order, while Targets contains one row per region and month. A physical join on Region alone repeats each monthly target for every matching order and inflates SUM(Target). A relationship on Region and Month keeps both grains and is usually the cleanest single-source design. A blend can also work: make Sales primary, link Region and Month, and aggregate Target in the view. If a region exists only in Targets, it will not appear until Targets becomes primary or a row-level model preserves it.

Validation checklist

Before approving a combination, state the grain of every table, confirm link or join field types, inspect unmatched keys, compare row counts, and reconcile an additive control total. Then test a dimension from each source, a non-additive aggregate, and the filters the final view will use. Correct output is more important than choosing the feature with the shortest setup time.