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Tableau Consultant

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Tableau Consultant Study Checklist
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Checklist progressProgress

0/285Learned

  • Current-state discovery with the Data and Analytics Survey
  • Analytics value map from business goals to analytics capabilities
  • Prioritizing initial use cases and data sources for a Tableau deployment
  • Matching business needs to Desktop, Prep, Server, Cloud and Bridge
  • Mapping current-state report roles to Creator, Explorer and Viewer licenses
  • Live connections versus extracts for data freshness needs
  • Proactive delivery needs: subscriptions, data-driven alerts and Pulse
  • Sharing analytics with external audiences: embedding versus Tableau Public
  • Chart type selection for a stated analytical question
  • Audience-driven level of detail and context in dashboard design
  • Guided analysis versus show-everything dashboard design
  • Dashboard layout, sizing and device-specific design
  • Color strategy for alerting, highlighting and consistency in dashboards
  • Tableau Cloud operating model benefits versus self-hosted Tableau Server
  • Client requirements that favor keeping Tableau Server
  • Self-hosting Tableau Server on public cloud infrastructure versus Tableau Cloud
  • Shared responsibility and content backups in Tableau Cloud
  • Tableau Cloud compliance certifications and data residency
  • Licensing model differences between Tableau Server and Tableau Cloud
  • Tableau Cloud site capacity and site model in the migration decision
  • Data source compatibility review before migrating to Tableau Cloud
  • Tableau Bridge and private-network data, including Prep flows, after migration
  • Authentication and user provisioning changes in a Server-to-Cloud move
  • Embedding, guest access and customization differences in Tableau Cloud
  • Replacing Tableau Server repository monitoring with Admin Insights
  • View load performance on Tableau Cloud versus a tuned Tableau Server
  • Migration readiness assessment and premigration content cleanup
  • Choosing a Server-to-Cloud migration tool or service
  • Tableau Cloud site preparation before content migration
  • Migration limitations and content that needs rework after the move
  • Testing, validation and cutover for a Server-to-Cloud migration
  • Reasons and support options when recommending a Server upgrade
  • Assessing current deployment, capacity and dependencies before an upgrade
  • Target version selection and review of product changes
  • Upgrade cadence and downtime window planning
  • In-place versus Blue/Green upgrade approach for Tableau Server
  • Side-by-side TSM upgrade behavior and user downtime
  • Combining a Server upgrade with a move to new hardware
  • Pre-upgrade environment preparation for Tableau Server
  • Backup, non-production testing and rollback for a Tableau Server upgrade
  • Upgrading client and companion software alongside Tableau Server
  • Communication, education and support plans for a Server upgrade
  • Verifying and cleaning up after a Tableau Server upgrade
  • Gap analysis between required metrics and available, accessible data
  • Field data types, domains and values as analysis-readiness checks
  • Data granularity versus the level of detail the analysis requires
  • Combining existing sources stored at different levels of detail
  • Location data that Tableau can geocode for map requirements
  • Custom geocoding versus other remedies for unrecognized locations
  • Upstream and downstream impact analysis with Tableau Catalog lineage
  • Data Management licensing and Catalog enablement for lineage on Server versus Cloud
  • Notifying downstream owners and users about upstream data changes
  • Metadata API versus the Catalog Lineage tab for lineage reporting
  • Lineage gaps from custom SQL, cube and parameterized flow connections
  • Lineage count mismatches and permission-limited metadata in Catalog
  • Upstream shaping and pre-aggregation to match the analysis
  • Reducing data volume with data source filters and extract settings
  • Joins, blends and cross-database joins as performance trade-offs
  • Referential integrity settings for join culling and relationship queries
  • Native database connectors versus ODBC drivers for performance
  • Database-level tuning recommendations for Tableau query workloads
  • Isolating data source performance from workbook and network factors

2.1.1 Recommend an appropriate data transformation strategy

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Upstream ETL versus Tableau Prep versus in-workbook data shaping

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Reshaping wide data into a tall, tidy structure

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Data Interpreter versus Tableau Prep cleaning steps for messy source files

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Relationships versus joins versus unions versus blends for combining data

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Centralized published data sources versus self-service data preparation

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2.1.2 Specify the requirements for minimum level of granularity

0/4

  • Defining the grain of a data source and its row identifier
  • Deriving the minimum required grain from analytical requirements
  • Inflated totals from values repeated at a coarser grain
  • Relationships versus joins for tables at different levels of detail

2.2.1 Implement RLS and an entitlement table

0/10

  • Deepest granularity versus sparse entitlement models
  • Matching entitlement user values to Tableau identities
  • Deepest-granularity entitlement join and user filter
  • Sparse entitlement join logic with NULL as all-access
  • Denormalized entitlements view with users and roles mapping
  • Securing entitlement RLS in a published data source
  • Entitlement-table RLS with multi-table extracts
  • Join and filter performance for entitlement-table RLS
  • Virtual connection data policy design with an entitlement table
  • Writing a data policy condition

2.2.2 Identify group functions versus user functions

0/4

  • Group membership function versus user identity functions in Tableau
  • USERNAME versus FULLNAME for matching security columns
  • ISMEMBEROF group name arguments and membership behavior
  • Combining ISMEMBEROF and USERNAME in one row-level security filter

2.2.3 Compare RLS approaches

0/9

  • Row-level security versus content permissions for a data access requirement
  • Manual user filters versus dynamic user filters
  • Virtual connection data policies versus data source RLS filters
  • When to reuse existing database RLS instead of building RLS in Tableau
  • Impersonation, Kerberos delegation, SAML delegation and initial SQL compared
  • RLS approach fit for extracts versus live connections
  • RLS approach constraints on Tableau Cloud versus Tableau Server
  • User attribute functions for RLS in embedded content
  • Row-level security options for OLAP cube connections

2.3.1 Recommend an appropriate method to connect to data, such as Web Data Connectors, web extract APIs, custom SQL, or ODBC

0/7

  • Live connection versus extract in a connection recommendation
  • Native connector, plug-in connector or generic ODBC for an unlisted database
  • ODBC driver capability limitations and connection tuning
  • File-based data connections for Tableau Cloud and Server
  • When custom SQL is justified over canvas joins, unions and pivots
  • Web Data Connector SDK versus Hyper API versus Connector SDK
  • Hyper API for custom extract creation and update pipelines

2.3.2 Create connections by using Tableau Bridge

0/8

  • When Tableau Cloud needs Tableau Bridge versus a direct connection
  • Bridge live queries versus Bridge extract refreshes
  • Tableau Bridge network and security requirements
  • Connector support and driver setup for Tableau Bridge
  • Refreshing file-based data through Tableau Bridge
  • Bridge client pools and private network domain mapping
  • Bridge support for virtual connections to private network data
  • Planning Bridge client capacity and management model

2.3.3 Specify aggregation level and strategy for data sources in Tableau products

0/5

  • Choosing where aggregation happens across the Tableau data pipeline
  • Aggregated extracts and extract right-sizing options
  • Tableau Prep Aggregate step versus FIXED LOD calculations
  • Purpose-built summary data sources versus one consolidated detail source
  • Aggregated data sources to limit exposure of row-level detail
  • Advanced chart type versus a simpler standard chart for the audience
  • Sankey diagrams for flow between categorical stages
  • Chord diagrams versus Sankey for relationships among entities
  • Radar charts for comparing items across several metrics
  • Small multiples versus single dense or overlaid charts
  • Tile maps versus filled maps for state-level comparison
  • Data densification techniques and when to use them
  • Building advanced chart types in Desktop versus web authoring
  • Stages of the Tableau query pipeline
  • Data source and extract filters versus context filters
  • Top N, conditional filters and sets versus general dimension filters
  • LOD expression types relative to dimension filters
  • Table calculations versus dimension and measure filters
  • Top N and conditional filters combined with a general dimension filter
  • LOD benchmarks that ignore or follow a user filter unexpectedly
  • Row-level security data source filters and company-wide FIXED benchmarks
  • Keeping percent-of-total values stable when categories are filtered out
  • Table calculation filters that hide marks without changing results
  • Choosing the dashboard action type for an interactivity requirement
  • Inserting field, filter, and parameter values into dynamic URL actions
  • Web page objects versus browser tabs as URL action targets
  • URL action data value options and mailto email links
  • Troubleshooting URL actions that fail on Tableau Server or Cloud
  • Parameter action source field, aggregation, and clearing settings
  • Parameter actions versus filter actions for context-preserving comparisons
  • Dynamic zone visibility driven by parameter actions
  • Reducing returned rows through needed fields, aggregation and granularity
  • Custom SQL versus native tables for query efficiency
  • Live connection versus extract for slow query-bound workbooks
  • Database-side tuning for slow live-connection queries
  • Server-wide data cache refresh frequency on Tableau Server
  • Workbook data freshness policy versus the server cache default
  • Query cache sizing and memory settings on Tableau Server
  • Scaling Cache Server processes for heavy view traffic
  • View Acceleration for frequently used Tableau Server views
  • Workbook design choices that limit cache reuse
  • Native Tableau features versus calculated fields
  • Data type cost of calculation results and string comparisons
  • Integer-valued parameters with string display labels in conditional calculations
  • CASE versus nested IF and ELSEIF logic efficiency
  • Efficient and correct date conversion calculations
  • Relative cost of aggregation functions in calculations
  • LOD expression query cost and join culling
  • LOD expressions versus table calculations or blends for performance
  • Filtering on complex calculated fields
  • Calculations that must stay in Tableau versus upstream candidates
  • Materializing calculations in extracts with Compute Calculations Now
  • Pre-aggregating data upstream for expensive calculations
  • LOD and rank calculations in Tableau Prep flows
  • Choosing the upstream layer: database view, Tableau Prep flow or extract
  • Performance recording as the first diagnostic step
  • Enabling and starting a performance recording on Tableau Server
  • Reading the Performance Summary timeline and events views
  • Using the Detailed Views dashboard to isolate slow activities
  • Long Compiling query events and calculation complexity
  • Long Executing query events on live connections versus extracts
  • Using recorded query text to tune the database
  • Slow Connecting to data source events
  • Computing layouts versus server rendering events
  • Geocoding, data blending and extract generation events
  • Interpreting Null-worksheet and missing query events in a recording
  • Dashboard and workbook sheet count as query load
  • Query batching through a shared level of detail across dashboard sheets
  • Interactive filter count versus filter actions
  • Filter settings that add query cost
  • Fixed-size versus automatic dashboard sizing
  • Mark density, large crosstabs and complex polygons
  • Workbook size from images and custom geocoding
  • Workbook Optimizer guideline checks before publishing
  • Choosing an aggregation for a dimension in an aggregated view
  • Conditional aggregation with a dimension test inside the aggregate
  • Aggregate ratios versus row-level ratios across dimension levels
  • INCLUDE LOD expressions for finer-than-view aggregation
  • ATTR to stop a dimension from partitioning a table calculation
  • Dimension and non-additive aggregation limits in data blends
  • Addressing versus partitioning dimensions in table calculations
  • Table versus Pane compute-using scopes for multi-directional calculations
  • At the level and Restart every options with Specific Dimensions
  • WINDOW functions for moving and partition-wide calculations
  • LOOKUP versus PREVIOUS_VALUE for cross-row and recursive logic
  • Nested table calculations with independent Compute Using settings
  • Tableau Prep multi-row calculations versus Desktop table calculations
  • Fiscal year start at data source versus date field level
  • Date parts and truncations affected by a fiscal calendar
  • Fiscal requirements beyond the built-in Fiscal Year Start setting
  • ISO-8601 week-based calendar and iso date parts
  • Week start setting versus `start_of_week` in date functions
  • Period-to-date and prior-period comparison date calculations
  • Valid dimension declarations and aggregate expressions in LOD syntax
  • FIXED versus INCLUDE versus EXCLUDE when results must hold across views
  • LOD results coarser or finer than the view: aggregation versus replication
  • Table-scoped LOD expressions for row-to-overall comparisons and percent of total
  • FIXED LOD results used as dimensions for cohorts and frequency buckets
  • Nested LOD expressions for multi-level aggregation
  • LOD expressions to correct join duplication versus relationships
  • LOD expression limitations with data blending and data source constraints
  • Percent-of-total ratios combining aggregates with LOD expressions
  • Row-level flags and comparisons against LOD benchmarks
  • FIXED LOD and date functions for cohort retention measures
  • Table calculations computed over LOD expression results
  • Period-over-period comparisons combining date logic with table calculations
  • Parameter-driven calculations combined with LOD and table calculations
  • LOD expression versus table calculation versus aggregate for multi-step requirements
  • Mixing aggregate and non-aggregate arguments in a calculation
  • Asterisks and ATTR results in calculated values
  • Blank values at partition edges in offset table calculations
  • Grand totals and aggregated values that differ from displayed row values
  • Replicated and inflated values from LOD expressions in the view
  • EXCLUDE and INCLUDE dimension matching with date expressions
  • Isolating faults in complex multi-step calculations
  • Centralized, delegated and self-governing governance models
  • Data and content governance areas in a mixed governance model
  • Modern Analytics Workflow roles mapped to Tableau site roles
  • Virtual connections for centrally governed data access
  • Tableau Catalog versus Metadata API for metadata management
  • Closed permissions model and the Default project baseline
  • Project-level and group-level permission management
  • Locked versus customizable project permissions
  • Permission templates and capabilities for downloading and editing content
  • Project Leader capability for delegated project administration
  • Permission rule evaluation across user and group rules
  • Site role caps versus permission rules in effective access
  • Group sets for access that requires membership in multiple groups
  • Sites versus projects for isolating departments and sensitive content
  • Multi-factor authentication options for Tableau Cloud
  • Certifying published data sources to steer analysts to trusted data
  • Roles and capabilities required to certify data assets
  • Certification criteria and data review before data is trusted
  • Consolidated published data sources to limit data source proliferation
  • Data Management prerequisites for certification and quality warnings
  • Tableau Catalog discovery to prevent duplicate data sources
  • Data quality warning types and the assets that carry them
  • Visibility and downstream surfacing of data quality warnings
  • Monitoring quality warnings for extract refresh and flow run failures
  • Data quality warnings versus sensitivity labels and custom data labels
  • Insights that need an administrative view versus content-level usage tools
  • Administrative view access by site role and deployment scope
  • Tableau Server versus Tableau Cloud monitoring priorities
  • Admin views versus performance recordings for slow-content diagnosis
  • Admin views versus Admin Insights on Tableau Cloud
  • Traffic to Views versus Traffic to Data Sources for adoption measurement
  • Background task admin views for extract failures and schedule delays
  • Admin views and data sources for view load performance
  • Space usage, disk space and stale content admin views
  • User activity and sign-in monitoring with Actions views and Admin Insights
  • License utilization monitoring on Tableau Server and Tableau Cloud
  • Admin Insights Site Content data source for content governance
  • Admin Insights Permissions, Subscriptions and Tokens for security audits
  • Tableau Bridge monitoring admin views on Tableau Cloud
  • Custom administrative views from the Tableau Server repository
  • Sharing and alerting on administrative data beyond administrators
  • Tableau Public versus Tableau Server or Cloud for sharing content
  • Site roles and project capabilities required to publish content
  • Published versus embedded data sources when publishing workbooks
  • Embedded versus prompted credentials for published data
  • Sheet visibility, tabs and thumbnail options when publishing workbooks
  • Live connection versus extract when publishing to Tableau Cloud
  • Extract refresh schedules and refresh types for published content
  • Publishing workbooks with local files or private network data to Server versus Cloud
  • Publishing Tableau Prep flows and flow outputs to Server or Cloud
  • Embedding published Tableau views in webpages and portals
  • Sandbox versus certified projects in a content promotion workflow
  • Development, staging and production environments for content promotion
  • Pre-deployment workbook testing and performance validation
  • Content Migration Tool use cases, plan transformations and limitations
  • Single governed workbook versus duplicate filtered copies for distribution
  • Subscriptions versus data-driven alerts for distributing published views
  • Rolling back a published workbook with revision history
  • Protecting custom views when maintaining published workbooks
  • Impact analysis and user notification before changing shared content
  • Retiring stale content and reassigning ownership

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  1. Tableau Consultant Checklist
  3. Plan and Prepare Data Connections

Relationships versus joins versus unions versus blends for combining data

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Next conceptCentralized published data sources versus self-service data preparation

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  1. Tableau Consultant Checklist
  3. Plan and Prepare Data Connections
Relationships versus joins versus unions versus blends for combining data

Explainer

Learn More

Practice Question

Studying for this exam?

This is one of 285 concepts on the Salesforce Tableau Consultant exam.

Looking for practice exams? Click below to try one!

Keep going

Next conceptCentralized published data sources versus self-service data preparation

Explainer

When an analysis needs data from more than one table or data source, Tableau gives you four ways to combine it: relationships, joins, unions, and blends. They differ in two ways. The first is whether the data is merged into one fixed table or kept separate. The second is when Tableau decides how rows match up. Picking the right method decides whether the client's numbers come out complete and free of duplicates.
Core information
  • A Tableau data source has two layers. The top logical layer is where you connect logical tables with relationships (the flexible lines called "noodles"). The physical layer, which you open by double-clicking a logical table, is where you join or union physical tables. Logical tables act like containers for physical tables, and you must use relationships, not joins, between logical tables.
More details and nuances
  • Joins and blends aggregate at different times. A join combines the data and then aggregates; a blend aggregates and then combines. So in a blend, an asterisk (*) on a mark means the secondary source holds several dimension values for that mark. Fix it by making sure each primary mark has only one matching secondary value, possibly by swapping which source is primary.

Explainer

When an analysis needs data from more than one table or data source, Tableau gives you four ways to combine it: relationships, joins, unions, and blends. They differ in two ways. The first is whether the data is merged into one fixed table or kept separate. The second is when Tableau decides how rows match up. Picking the right method decides whether the client's numbers come out complete and free of duplicates.
Core information
  • A Tableau data source has two layers. The top logical layer is where you connect logical tables with relationships (the flexible lines called "noodles"). The physical layer, which you open by double-clicking a logical table, is where you join or union physical tables. Logical tables act like containers for physical tables, and you must use relationships, not joins, between logical tables.
More details and nuances
  • Joins and blends aggregate at different times. A join combines the data and then aggregates; a blend aggregates and then combines. So in a blend, an asterisk (*) on a mark means the secondary source holds several dimension values for that mark. Fix it by making sure each primary mark has only one matching secondary value, possibly by swapping which source is primary.