Tableau Guide

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Tableau is a comprehensive data visualization and analytics platform that combines intuitive user interfaces with powerful analytical capabilities. It provides a layered architecture encompassing data connectivity, preparation, and visual analysis components that efficiently process data using a columnar in-memory data engine. The platform supports diverse analytical workflows across desktop, server, and cloud environments while enabling sophisticated data modeling, interactive dashboarding, and enterprise-grade governance. Tableau's calculation language and visualization framework support both exploratory and explanatory analytics through direct manipulation interfaces optimized for speed of insight generation.

Tableau Architecture

Tableau Platform Architecture

Core components of the Tableau platform and how they interact

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Tableau Products

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Tableau Components

Tableau Desktop

Tableau Desktop

Technical Implementation

Technical

Tableau Desktop empowers business users and analysts to discover insights independently, accelerating data-driven decision making and reducing dependency on technical resources.

Business Capabilities

• Visual Data Exploration: Uncover patterns and insights through intuitive visual analysis
• Self-Service Analytics: Enable business users to answer their own questions
• Interactive Reporting: Create dynamic reports that allow users to explore the data
• Data Storytelling: Craft narratives that explain findings and drive decisions
• Advanced Analysis: Apply sophisticated analytical techniques without programming

Organizational Benefits

• Faster Time to Insight: Reduce the cycle from question to answer
• Analytical Autonomy: Decrease reliance on IT and data teams for analysis
• Knowledge Sharing: Distribute findings through interactive dashboards
• Data Literacy Growth: Increase organization-wide data understanding
• Decision Support: Provide evidence-based context for strategic choices

User Adoption Strategy

• Training Paths: Role-based learning from basic to advanced skills
• Starter Templates: Pre-built workbooks to accelerate adoption
• Community Building: Internal user groups to share knowledge
• Early Wins: Showcase impactful use cases to drive adoption
• Skills Development: Progressively build analytics capabilities

Business Value

Non-Technical

Tableau Desktop is the primary authoring tool for creating visualizations, dashboards, and data sources, featuring a powerful analytics environment for data exploration and insight discovery.

Key Technical Components

• Data Connection Framework: Native and ODBC/JDBC connectivity to diverse data sources
• VizQL Engine: Translates drag-and-drop actions into database queries and visual representations
• Hyper Data Engine: High-performance in-memory analytical database for data extracts
• Calculation Language: Formula language with table calculations, LOD expressions, and parameters
• Visual Canvas: Interactive workspace for building visualizations using marks and channels

Data Processing Architecture

• Live Connections: Direct queries to source databases, leveraging native query optimization
• Extracts: Compressed, columnar snapshots of data for high-performance analysis
• Query Federation: Ability to combine data from multiple sources
• Data Modeling: Logical layer with relationships, joins, and blends
• Performance Optimization: Query batching, parallel processing, and result caching

Technical Capabilities

• Visual Analytics: Interactive data visualization through drag-and-drop interface
• Advanced Analysis: Statistical functions, forecasting, clustering, and trend analysis
• Geospatial Analysis: Built-in mapping and spatial calculations
• Custom Visualization: Extensions API for custom visualizations
• Data Preparation: Basic data cleaning, pivoting, and transformation

File Format and Storage

• Workbook (.twb): XML document containing visualization definitions, calculations, and metadata
• Packaged Workbook (.twbx): Compressed archive containing workbook and local data sources
• Data Source (.tds): Saved connection information and metadata
• Packaged Data Source (.tdsx): Data source file with embedded extract
• Extract (.hyper): Proprietary columnar database file format for Tableau extracts

Tableau Server

Tableau Server

Technical Implementation

Technical

Tableau Server transforms how organizations share and collaborate on analytics, creating a secure, centralized environment for data-driven decision making across the enterprise.

Business Capabilities

• Content Distribution: Securely share analytics content throughout the organization
• Centralized Management: Single platform for governing all analytics assets
• Collaborative Analytics: Enable teams to build on each other's insights
• Automated Delivery: Schedule reports, alerts, and data refreshes
• Mobile Access: Deliver insights to users on any device

Organizational Benefits

• Analytics Democratization: Wider access to insights across roles and departments
• Decision Support: Data-driven decision making at all levels
• Knowledge Sharing: Preserve and distribute analytical expertise
• Data Governance: Consistent definitions and secure access controls
• IT Efficiency: Managed self-service reduces report development backlog

Implementation Strategy

• Governance Framework: Define roles, permissions, and content lifecycle
• Site Strategy: Organize by department, function, or audience
• License Planning: Appropriate mix of Creator, Explorer, and Viewer licenses
• Content Promotion: Clear paths from development to production
• Adoption Measurement: Track usage metrics to identify successful content

Business Value

Non-Technical

Tableau Server is an enterprise analytics platform for publishing, sharing, and managing Tableau content, providing centralized governance, security, and scalability for organizational analytics.

Architecture Components

• Gateway: Load balancer and authentication layer for client requests
• Application Server: Core services for visualization, content management, and user interactions
• VizQL Server: Processes that handle visualization requests
• Backgrounder: Handles scheduled tasks like extract refreshes and subscriptions
• Data Server: Manages published data sources and provides connection sharing
• File Store: Storage for extracts and workbook files
• Repository: PostgreSQL database storing metadata, permissions, and system information

Deployment Options

• Single-Node: All processes on one server for small deployments
• Distributed: Processes spread across multiple servers for scalability
• High Availability: Redundant components for failover and reliability
• External Load Balancer: Enterprise network integration for traffic distribution
• External Identity Providers: SAML, OpenID Connect, Active Directory integration

Technology Stack

• Web Platform: Java-based web application
• Metadata Storage: PostgreSQL database
• Data Processing: Hyper engine for in-memory analytics
• Authentication: SAML, OAuth, Kerberos, Active Directory, local authentication
• API Layer: REST API for programmatic interaction

Resource Management

• Resource Pools: Node allocation for different workloads
• Site Isolation: Separate content and users into logical groups
• Extract Refresh Scheduling: Optimization of background processing
• Connection Pooling: Efficient database connection management
• Caching: Multi-level caching for query results and visualizations

Tableau Prep

Tableau Prep

Technical Implementation

Technical

Tableau Prep extends self-service analytics to data preparation, empowering users to independently clean and structure data without specialized ETL skills or tools.

Business Capabilities

• Self-service Data Preparation: Enable analysts to prepare their own data
• Visual Data Quality Assessment: Identify and resolve data issues visually
• Repeatable Data Processes: Create standardized, reusable data preparation flows
• Data Transformation: Reshape data into analysis-ready formats
• Process Automation: Schedule routine data preparation tasks

Organizational Benefits

• Reduced Data Preparation Time: Visual interface accelerates cleaning tasks
• Improved Data Quality: Easily identify and fix data issues
• Analytics Agility: Faster path from raw data to analysis
• Reduced IT Dependency: Less reliance on ETL developers
• Process Documentation: Self-documenting flows for knowledge transfer

Implementation Strategy

• Skill Development: Train analysts on data preparation concepts
• Flow Libraries: Build reusable preparation patterns
• Data Quality Framework: Establish standards for prepared data
• Integration Plan: Coordinate with enterprise data management
• Governance Approach: Balance self-service with appropriate oversight

Business Value

Non-Technical

Tableau Prep is a data preparation tool that helps users visually combine, shape, and clean data for analysis in Tableau, providing a direct visual interface for ETL processes.

Core Components

• Prep Builder: Desktop application for creating data preparation flows
• Prep Conductor: Server component for scheduling and managing flows
• Input Connections: Native connectors to various data sources
• Flow Steps: Visual representation of transformation operations
• Data Grid: Interactive preview of data at each step

Data Transformation Capabilities

• Cleaning Operations: Filter, group, replace, remove, split, rename
• Structural Transformations: Pivot, unpivot, join, union, aggregate
• Data Profiling: Visual assessment of data quality and distribution
• Calculated Fields: Formula-based derived columns
• R & Python Integration: Custom scripts for advanced transformations

Technical Architecture

• Visual Flow: Node-based representation of the data preparation pipeline
• Data Sampling: Intelligent sampling for efficient processing of large datasets
• In-memory Processing: Hyper engine for data transformation operations
• Execution Model: Flow steps executed sequentially with data passing between nodes
• Smart Features: AI-driven recommendations for cleaning and grouping operations

Output Options

• Hyper Extract: Optimized Tableau data extract
• Published Data Source: Centralized source on Tableau Server
• CSV Export: Standard text file output
• Database Write: Direct write to supported databases
• Flow Output: Save results for use in other flows

Data Model

Tableau Data Model

Technical Implementation

Technical

An effective data model creates a business-oriented semantic layer that makes complex data accessible, consistent, and performant for all users.

Business Capabilities

• Business Metadata: Create user-friendly naming and documentation
• Unified Definitions: Standard calculations and metrics across reports
• Multiple Perspective Analysis: Examine data from different business angles
• Performance Optimization: Fast response times for complex analyses
• Governance Framework: Controlled, trusted data sources for decision-making

Organizational Benefits

• Single Version of Truth: Consistent metrics across the organization
• Analytical Agility: Faster development of new analyses
• Knowledge Capture: Business rules and definitions preserved in models
• Data Democratization: Complex data structures made accessible
• Cross-functional Analysis: Connect data across business domains

Implementation Strategy

• Business-First Design: Model based on analytical needs
• Star Schema Approach: Organize around facts and dimensions
• Naming Standards: Clear, business-friendly naming conventions
• Data Dictionary: Document definitions within the model
• Performance Testing: Validate with representative data volumes

Business Value

Non-Technical

Tableau's data model provides a flexible framework for defining relationships between tables, creating calculated fields, and optimizing query performance.

Data Model Components

• Physical Layer: Actual tables from data source with join relationships
• Logical Layer: Table groups connected by relationships, supporting multi-fact analysis
• Data Source Filters: Global filters applied at the data source level
• Calculated Fields: Formula-based columns using Tableau's calculation language
• Parameters: User-controlled values that can affect queries and calculations

Relationship Types

• Relationships: Logical connections between tables with automatic join selection
• Joins: Explicit table combinations (inner, left, right, full)
• Blends: Data combination across different data sources
• Unions: Vertical combination of similar tables
• Cross-database Joins: Connections between tables from different database systems

Performance Optimization

• Context Filters: Creating temporary tables for filter-heavy analyses
• Extract Filters: Limiting data pulled into extracts
• Aggregation: Pre-aggregating data for performance
• Indexed Fields: Identifying columns for extract indexing
• Query Optimization: Structure that minimizes query complexity

Advanced Features

• Level of Detail Expressions: Control aggregation granularity independently of visualization
• Table Calculations: Computations based on the result set
• Row-level Security: Data filtering based on user attributes
• Data Source Certification: Governance for trusted data sources
• Semantic Layer: Business-friendly metadata and naming

Tableau Calculation Language

Calculation Business Value

Tableau's calculation capabilities allow organizations to implement complex business logic, create standardized metrics, and develop sophisticated analyses without programming expertise.

Business Applications

• KPI Definition: Formalize calculation of key performance indicators
• Financial Analysis: Revenue, margin, growth, and profitability metrics
• Customer Analytics: Segmentation, lifetime value, and behavior metrics
• Sales Performance: Quota attainment, pipeline coverage, forecast accuracy
• Marketing Effectiveness: Campaign performance, attribution, conversion metrics

Analytical Use Cases

• Trend Analysis: Period-over-period comparisons and growth metrics
• Comparative Analysis: Benchmarking against targets, budgets, or prior periods
• Cohort Analysis: Tracking groups over time based on shared characteristics
• Segmentation: Dividing data into meaningful groups for comparison
• What-If Analysis: Parameter-driven scenarios for business planning

Business Benefits

• Metric Standardization: Consistent definition of business metrics
• Analytical Flexibility: Adapt analyses to evolving business questions
• Knowledge Capture: Business logic documented in calculations
• Self-Service Enablement: Complex metrics available to all users
• Governance Support: Central definitions for critical business measures

Implementation Strategy

• Calculation Library: Build reusable, documented calculation templates
• Business Glossary: Define and document standard metrics
• Skill Development: Train analysts on calculation capabilities
• Calculation Reviews: Process for validating critical business metrics
• Performance Monitoring: Identify and optimize resource-intensive calculations

Visualization and Dashboard Design

Technical Implementation

Technical

Visualization Business Applications

Effective visualization transforms data into actionable insights, making complex information accessible and enabling faster, more informed business decisions.

Business Communication Benefits

• Insight Discovery: Reveal patterns and relationships not apparent in raw data
• Decision Support: Provide clear context for business decisions
• Information Clarity: Make complex data understandable to all stakeholders
• Narrative Development: Create compelling data stories that drive action
• Time Efficiency: Accelerate understanding and analysis of business information

Dashboard Applications

• Executive Dashboards: High-level KPIs and business health indicators
• Operational Monitoring: Real-time or near-real-time performance tracking
• Analytical Deep Dives: Interactive exploration of business questions
• Performance Scorecards: Tracking metrics against targets and benchmarks
• Self-service Reporting: Interactive reports for business users

Design Strategy

• Purpose-Driven Design: Start with the business questions to be answered
• Audience Adaptation: Tailor complexity to user sophistication
• Visual Hierarchy: Guide attention to most important insights first
• Consistent Standards: Apply uniform visual language across dashboards
• Progressive Disclosure: Reveal details on demand through interaction

Implementation Best Practices

• Visual Literacy Training: Educate users on interpretation of visualizations
• Design System: Create organizational standards for visualization
• User Testing: Validate effectiveness with target audience
• Iterative Refinement: Continuously improve based on user feedback
• Mobile Consideration: Design for multiple device form factors

Business Value

Non-Technical

Visualization Technical Concepts

Tableau's visualization engine translates data into visual representations using a sophisticated system of marks, channels, and interactive controls.

Visual Encoding Framework

• Marks: Basic visual elements (bars, lines, points, shapes, text, etc.)
• Visual Channels: Properties that encode data (position, size, color, shape, etc.)
• Shelves and Cards: UI elements that control how fields are encoded
• Mark Types: Automatic and manual selection of appropriate visualizations
• Show Me: Intelligent visualization recommendation system

Interactive Elements

• Filters: Interactive data refinement through various control types
• Parameters: User-controlled values affecting visualizations
• Highlights: Emphasizing specific data points through interaction
• Actions: Inter-visualization interactions (filter, highlight, URL, etc.)
• Tooltips: Context-specific information on hover

Dashboard Components

• Sheets: Individual visualizations that compose a dashboard
• Containers: Horizontal, vertical, and floating layout elements
• Device Layouts: Responsive designs for different screen sizes
• Objects: Non-data elements like images, text, web pages, and buttons
• Extensions: Custom dashboard functionality through Extension API

Advanced Visualization Features

• Dual Axes: Multiple measures on different scales in a single view
• Combined Chart Types: Mixing visualization types (e.g., bar and line)
• Reference Lines: Statistical references and annotations
• Trend Lines: Statistical models showing relationships
• Forecasting: Time-series projections using exponential smoothing

Performance Optimization

• Mark Aggregation: Using aggregated values to reduce mark count
• Pre-aggregated Extracts: Summarizing data before visualization
• Filter Hierarchy: Using context and top/bottom filters strategically
• Fixed Dashboard Size: Limiting layout complexity
• Worksheet Hiding: Showing only relevant visualizations

Enterprise Deployment

Feature	Tableau Desktop/Public	Tableau Server	Tableau Online	Tableau Cloud
Deployment Type	Desktop application	On-premises or private cloud	Tableau-hosted SaaS	Salesforce-hosted SaaS
Infrastructure Management	Local installation	Self-managed hardware and software	Fully managed by Tableau	Fully managed by Salesforce
Scalability	Limited to desktop resources	Scalable with additional hardware	Auto-scaling with subscription tiers	Auto-scaling with subscription tiers
Content Sharing	Limited export options	Full sharing capabilities within organization	Full sharing capabilities with cloud access	Full sharing with enhanced Salesforce integration
Authentication Options	Local authentication	AD, LDAP, SAML, Kerberos, OpenID, local	SAML, MFA, Tableau ID	SAML, MFA, Tableau ID, Salesforce login
Data Connection Security	Direct connections	On-network connections or data extract	Cloud connections or through Tableau Bridge	Cloud connections or through Tableau Bridge
Governance Features	Limited	Comprehensive (permissions, certification, monitoring)	Comprehensive (cloud-based)	Comprehensive with enhanced governance
Licensing Model	Perpetual or subscription	Creator, Explorer, Viewer roles + core licenses	Creator, Explorer, Viewer roles	Creator, Explorer, Viewer roles with Salesforce licenses
Maintenance	Manual updates	Self-managed updates and maintenance	Automatically maintained by Tableau	Automatically maintained by Salesforce
Backup & Recovery	Manual backups	Self-managed backup procedures	Automated backups provided by Tableau	Automated backups with enhanced recovery options
Typical Use Case	Individual analysis	Enterprise deployment with specific security needs	Organizations preferring cloud deployment	Organizations using Salesforce ecosystem

Security Model

Tableau Security Model

A robust security model enables organizations to confidently share data and insights while maintaining appropriate access controls and supporting governance and compliance requirements.

Business Benefits

• Controlled Information Sharing: Share insights while protecting sensitive data
• Data Governance Support: Enforce organizational data access policies
• Regulatory Compliance: Meet industry and legal requirements for data protection
• User-appropriate Access: Show users only the data relevant to their role
• Risk Mitigation: Reduce the potential for data breaches or leaks

Governance Capabilities

• Content Certification: Identify trusted, validated dashboards and data sources
• Usage Monitoring: Track access and usage patterns for audit purposes
• Change Management: Control publishing and modification of content
• Data Lineage: Understand and document data sources and transformations
• Environment Separation: Maintain development, testing, and production environments

Implementation Strategy

• Security Requirements Analysis: Document access control needs by data and user types
• Group-based Structure: Design security model around functional roles
• Project Organization: Align content organization with security boundaries
• Training: Educate content creators on security implementation
• Audit Procedures: Regularly review and validate security model effectiveness

Enterprise Deployment

Enterprise Deployment Patterns

Enterprise deployment enables organizations to scale analytics capabilities across thousands of users while maintaining governance, reliability, and performance.

Organizational Benefits

• Standardized Analytics: Consistent approach across business units
• Centralized Governance: Organizational control while enabling self-service
• Operational Reliability: Enterprise-grade uptime and performance
• Deployment Efficiency: Automated processes for content management
• Cost Optimization: Appropriate resource allocation

Strategic Implementation Considerations

• Center of Excellence: Central team for standards and support
• Governance Framework: Policies for development, certification, and sharing
• Training Program: Role-based education for creators and consumers
• Content Lifecycle: Processes from development to retirement
• Adoption Measurement: Metrics for usage and business impact

Enterprise Rollout Approach

• Phased Implementation: Start with high-value use cases, expand methodically
• Pilot Projects: Prove value with targeted initial deployments
• Change Management: Support transition from existing reporting tools
• Executive Sponsorship: Secure leadership support for analytics initiative
• Success Showcases: Highlight wins to drive broader adoption

Performance Optimization

Performance Optimization

Performance optimization ensures analytics solutions provide fast, responsive user experiences and scale effectively as data volumes and user bases grow.

Business Impact of Performance

• User Adoption: Fast dashboards encourage regular usage
• Decision Velocity: Reduced wait times accelerate analytical processes
• Resource Efficiency: Optimized dashboards reduce infrastructure costs
• Scalability: Support more users without service degradation
• User Satisfaction: Responsive analytics increase user confidence

Performance Strategy

• Prioritization Framework: Focus optimization efforts on high-impact dashboards
• Performance SLAs: Establish response time targets for critical reports
• Optimization Reviews: Regular assessment of high-usage dashboards
• Monitoring Program: Proactive identification of performance issues
• Design Standards: Performance guidelines for dashboard developers

Implementation Approach

• Start With Requirements: Define necessary data before building
• Progressive Enhancement: Begin with core functionality, add features incrementally
• Regular Testing: Validate performance with realistic data volumes and user counts
• User Feedback: Collect input on performance pain points
• Iterative Improvement: Continuous optimization based on usage patterns

Integration with Enterprise Systems

Technical Implementation

Technical

Enterprise Integration Value

Integration capabilities allow organizations to embed analytics into business processes, extend Tableau's functionality, and create seamless user experiences that maximize the value of data.

Business Benefits

• Process Integration: Analytics embedded directly in workflow applications
• Single Source of Truth: Consistent data access across platforms
• User Experience: Seamless analytics within familiar interfaces
• Automation: Reduced manual effort through programmatic control
• Extended Functionality: Custom capabilities beyond standard features

Strategic Applications

• Customer-facing Analytics: Embedded insights in products and portals
• Internal Applications: Analytics integrated with operational systems
• Content Management: Automated publication and distribution
• Custom Analytics Solutions: Specialized implementations for unique needs
• Analytics Ecosystems: Tableau as a component in broader analytics architecture

Implementation Considerations

• Security Requirements: Authentication, data access, and network security
• Performance Needs: Load management and response time expectations
• Development Resources: Skills required for integration implementation
• User Experience Design: Seamless incorporation into applications
• Maintenance Strategy: Long-term support for custom integrations

Business Use Cases

• CRM Enhancement: Customer insights embedded in sales applications
• ERP Augmentation: Operational analytics within business systems
• Customer Portals: Self-service analytics for clients and partners
• SaaS Products: Analytics as a feature in software products
• Enterprise Portals: Consolidated analytics access for employees

Business Value

Non-Technical

Enterprise Integration Capabilities

Tableau provides extensive integration capabilities to connect with enterprise systems, extend functionality, and embed analytics into applications.

Data Integration

• Native Connectors: Pre-built connections to databases, cloud services, and applications
• JDBC/ODBC: Standard database connectivity for non-native sources
• Web Data Connector: Custom connections to web APIs and services
• Connector SDK: Build custom native connectors for proprietary systems
• Hyper API: Programmatically create and modify Tableau extracts

Authentication & Identity

• Active Directory: Integration with enterprise directory services
• SAML 2.0: Identity federation with enterprise identity providers
• OpenID Connect: Modern authentication protocol support
• OAuth: Delegated authentication for data sources
• SCIM: Automated user provisioning and management

API & Development

• REST API: Programmatic control of Tableau Server/Online
• JavaScript API: Embed and control visualizations in web applications
• Extensions API: Custom functionality within dashboards
• Analytics Extensions: Integration with R, Python, and MATLAB
• Metadata API: Access and manage metadata about Tableau assets

Embedding Options

• Simple Embedding: Basic iframe integration
• JavaScript API: Interactive control and customization
• Connected Apps: Secure embedding with JWT authentication
• Embedding Middleware: Custom solutions for complex requirements
• Embedding Analytics: Tableau-as-a-Service provider model

Integration Patterns

• Portal Integration: Embedding in enterprise portals and intranets
• Application Integration: Embedding in business applications
• Content Automation: Programmatic content management and distribution
• Data Pipeline Integration: Incorporating Tableau in ETL processes
• Hybrid Cloud/On-Premises: Bridging cloud and on-premises data

Tableau Implementation Path

1. Define Analytics Strategy

   • Align analytics goals with business objectives
   • Identify key metrics and KPIs
   • Determine governance approach
   • Plan for required resources and skills
   • Set success criteria and timeline

2. Establish Technical Foundation

   • Select deployment model (Desktop/Server/Online/Cloud)
   • Configure environment and security
   • Set up data connectivity and refreshes
   • Define folder structure and naming conventions
   • Create development standards and patterns

3. Develop Core Data Models

   • Create shared data sources for key business domains
   • Implement consistent data definitions
   • Define security model
   • Document data lineage and business rules
   • Validate performance with expected data volumes

4. Design Initial Dashboards

   • Build core dashboards addressing priority business needs
   • Establish visual standards and templates
   • Implement consistent navigation patterns
   • Create user documentation and training materials
   • Test with representative user groups

5. Deploy to Production

   • Set up content migration process
   • Establish quality assurance process
   • Create distribution strategy
   • Configure scheduled refreshes
   • Set up monitoring and alerting

6. Enable User Adoption

   • Conduct role-based training sessions
   • Create internal support resources
   • Identify and nurture power users
   • Gather and incorporate user feedback
   • Showcase successful implementations

7. Scale and Optimize

   • Monitor usage patterns and performance
   • Expand to additional business areas
   • Refine governance based on experience
   • Optimize for growing user base and data volumes
   • Continuously improve based on business feedback

8. Advanced Capabilities

   • Implement advanced analytics
   • Explore embedded analytics scenarios
   • Develop customizations for specific needs
   • Integrate with business processes and applications
   • Create automated workflows

Resources and Next Steps

To continue your Tableau journey, consider these resources and next steps:

1. Expand Your Knowledge: Explore our guides on Data Modeling for BI and Dashboard Design for deeper expertise.

2. Compare BI Tools: Understand how Tableau compares to other platforms in our BI Tool Comparison guide.

3. Official Resources:

   • Tableau Help
   • Tableau Community Forums
   • Tableau Public Gallery

4. Advanced Learning:

   • Tableau Conference recordings
   • Tableau User Groups
   • Tableau Certification programs