Optimizing Your Tableau Dashboards for Speed. Data source optimization involves enhancing the performance and efficiency of data retrieval and integration processes. Here are five key points explaining data source optimization. Extracts vs. Live Connections: Consider using data extracts (hyper files) for large datasets. Extracts are pre-aggregated and can provide faster query response times compared to live connections to databases. Data Source Filters: Apply data source filters to limit the data retrieved. Filters reduce the amount of data transferred, improving query and dashboard performance. Aggregation: Use aggregation functions (e.g., SUM, AVG) at the data source level. Aggregating data in the source reduces the amount of data transmitted to Tableau, improving query speed. Optimized Queries: Craft optimized SQL queries when connecting to relational databases. Well-optimized queries fetch only the necessary data, minimizing query execution time. Incremental Updates: Implement incremental data updates when possible. Incremental updates add only new or modified data, reducing the volume of data transferred during refreshes.

1. TABLEAU
BEST
PRACTICES
FOR
PERFORMANC
E
Anita Behera

2. INTRODUCTIO
N
Tableau is a powerful
tool for data
visualization but can
suffer from
performance issues if
not optimized. It needs
to be optimized for
Speed.
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3. PERFORMANCE IN DATA VISUALIZATION
MATTERS SIGNIFICANTLY FOR SEVERAL
REASONS
• User Experience: Slow-loading or unresponsive dashboards frustrate users and hinder
their ability to interact with and explore data effectively. A responsive and fast dashboard
enhances user satisfaction.
• Decision-Making: Timely access to accurate data is critical for informed decision-making.
Performance issues can delay decision-making processes, potentially leading to missed
opportunities or poor choices.
• Productivity: Efficient data visualization tools and dashboards boost productivity. Users
can quickly extract insights, reducing the time spent on data preparation and analysis.
• Scalability: As data volumes grow, performance becomes increasingly important. Well-
performing visualizations and dashboards can handle larger datasets without compromising
speed or responsiveness.
• Data Accessibility: High performance ensures that data is accessible to a broader
audience within an organization. Slow dashboards may discourage users from exploring
data, limiting data-driven insights across departments.
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4. SLOW DASHBOARDS CAN LEAD TO A POOR
USER EXPERIENCE AND HINDER DECISION-
MAKING
• Frustration: Slow-loading dashboards frustrate users, leading to a negative user
experience. Users may abandon the dashboard or lose interest in exploring the data due to
the delays.
• Inefficiency: Sluggish dashboards slow down data analysis and exploration. Users
spend more time waiting for results, reducing their efficiency in extracting insights from the
data.
• Delayed Insights: Timely decision-making relies on quick access to up-to-date data.
Slow dashboards delay the delivery of critical insights, potentially leading to missed
opportunities or delayed actions.
• Data Staleness: In rapidly changing environments, slow dashboards may present
outdated information, making decisions based on stale data. This can lead to inaccurate
assessments of the current situation.
• User Disengagement: A poor user experience can discourage users from actively
engaging with data and dashboards. This disengagement can result in underutilization of
valuable data resources, impacting the organization's ability to make data-driven decisions.
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5. DATA SOURCE OPTIMIZATION
DATA MODELING
VISUALIZATION OPTIMIZATION
DASHBOARD DESIGN
PERFORMANCE TESTING
TABLEAU SERVER
PERFORMANCE
PERFORMANCE MONITORING
CASE STUDIES
AGENDA
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6. DATA SOURCE
OPTIMIZATION
• Extracts vs. Live Connections:
• Consider using data extracts (hyper files) for large datasets.
• Extracts are pre-aggregated and can provide faster query response times compared to live connections to
databases.
• Data Source Filters:
• Apply data source filters to limit the data retrieved.
• Filters reduce the amount of data transferred, improving query and dashboard performance.
• Aggregation:
• Use aggregation functions (e.g., SUM, AVG) at the data source level.
• Aggregating data in the source reduces the amount of data transmitted to Tableau, improving query speed.
• Optimized Queries:
• Craft optimized SQL queries when connecting to relational databases.
• Well-optimized queries fetch only the necessary data, minimizing query execution time.
• Incremental Updates:
• Implement incremental data updates when possible.
• Incremental updates add only new or modified data, reducing the volume of data transferred during refreshes.
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7. DATA MODELLING
• Data Simplification:
• Effective data modeling simplifies complex data structures, reducing the computational
load on Tableau, which leads to faster query performance.
• Aggregation and Granularity:
• Well-structured data models allow for the appropriate level of data aggregation. This
ensures that visualizations and calculations use the right level of granularity, optimizing
query performance.
• Data Reduction:
• By eliminating unnecessary data and optimizing data structures, data modeling reduces
the volume of data Tableau needs to process, resulting in quicker response times.
• Relationship Optimization:
• Properly defining relationships between tables in the data model ensures that Tableau
performs joins efficiently, reducing the time required to retrieve data.
• Indexing and Sorting:
• Data modeling can
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8. VISUALIZATION
OPTIMIZATION
• Reducing Mark Complexity:
• Simplify visualizations by reducing the number of marks (data points) displayed, which
improves rendering speed and responsiveness.
• Aggregation and Granularity:
• Use aggregation at an appropriate level to reduce the volume of data processed,
resulting in faster query execution.
• Limiting Calculated Fields:
• Minimize the use of complex calculated fields that can slow down rendering and query
performance. Opt for calculated fields only when necessary.
• Optimizing Map Visualizations:
• For maps, consider using background images or map caching to reduce the need for
constant rendering and improve map performance.
• Dashboard Simplification:
• Streamline dashboards by removing unnecessary elements and focusing on the most
critical visualizations to maintain a responsive and user-friendly experience.
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9. DASHBOARD DESIGN
• Chart Selection: Choose appropriate chart types that best convey the data while
minimizing rendering complexity. Avoid overly intricate visualizations that can slow down the
dashboard.
• Filter and Action Optimization: Use filters and actions judiciously to limit the data
rendered on the dashboard. Avoid excessive interactivity that can lead to slow responses.
• Layout Simplification: Keep dashboard layouts clean and uncluttered. Remove
unnecessary elements, and focus on displaying critical insights to reduce rendering time.
• Parameter Usage: Use parameters to enable user interaction with the data. Parameters
can be more efficient than filters for certain scenarios, as they don't trigger as many queries.
• Dashboard Size: Consider the size of the dashboard. Smaller dashboards tend to load
faster. If possible, break down large dashboards into smaller, focused ones for better
performance.
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10. TABLEAU SERVER
PERFORMANCE
Optimizing Tableau Server performance is critical to delivering a smooth user experience and
efficient data access. Here are five key points to consider for Tableau Server performance:
•Hardware and Scalability: Ensure that Tableau Server is hosted on hardware with sufficient
resources, including CPU, memory, and storage. Plan for scalability by adding nodes or
resources as the user base and data volume grow.
•Cache Settings: Configure caching options to store frequently accessed data and
visualizations in memory. This reduces the need for constant data retrieval, improving response
times.
•Concurrency Management: Implement concurrency controls to manage the number of
concurrent users accessing the server. This prevents overloading and maintains system stability.
•Load Balancing: Use load balancing techniques to evenly distribute user requests across
multiple Tableau Server nodes. Load balancing helps distribute the workload efficiently and
improves fault tolerance.
•Regular Maintenance: Perform routine maintenance tasks, such as data source refresh
optimization, server monitoring, and log analysis, to identify and resolve performance
bottlenecks proactively.
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11. TABLEAU SERVER
PERFORMANCE
Optimizing Tableau Server performance is critical to delivering a smooth user experience and
efficient data access. Here are five key points to consider for Tableau Server performance:
•Hardware and Scalability: Ensure that Tableau Server is hosted on hardware with sufficient
resources, including CPU, memory, and storage. Plan for scalability by adding nodes or
resources as the user base and data volume grow.
•Cache Settings: Configure caching options to store frequently accessed data and
visualizations in memory. This reduces the need for constant data retrieval, improving response
times.
•Concurrency Management: Implement concurrency controls to manage the number of
concurrent users accessing the server. This prevents overloading and maintains system stability.
•Load Balancing: Use load balancing techniques to evenly distribute user requests across
multiple Tableau Server nodes. Load balancing helps distribute the workload efficiently and
improves fault tolerance.
•Regular Maintenance: Perform routine maintenance tasks, such as data source refresh
optimization, server monitoring, and log analysis, to identify and resolve performance
bottlenecks proactively.
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12. PERFORMANCE
MONITORING
• Performance monitoring is essential to ensure that your Tableau environment operates efficiently and delivers
optimal user experiences. Here are five key points related to performance monitoring in Tableau:
• Dashboard Usage Tracking: Monitor which dashboards and visualizations are most frequently accessed by
users. Identify popular content and areas where performance improvements may be needed.
• Query Performance Analysis: Analyze query performance to identify slow-performing queries and data
sources. Use query logs and performance dashboards to pinpoint bottlenecks.
• Resource Utilization Monitoring: Keep an eye on resource utilization, including CPU, memory, and disk usage
on Tableau Server nodes. Utilization spikes may indicate the need for hardware upgrades.
• Alerting and Thresholds: Set up alerting mechanisms for critical performance metrics, such as response times
exceeding predefined thresholds or resource utilization reaching critical levels. Receive notifications to address
issues promptly.
• Regular Reviews and Tuning: Conduct periodic reviews of performance data and user feedback to fine-tune
dashboards, data sources, and server settings. Continuously optimize your Tableau environment for better
performance.
• By monitoring and actively managing performance, you can ensure that Tableau delivers timely insights and
meets the needs of your users effectively.
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13. KEY TAKEAWAYS - CONT
Performance Matters:
• Performance is crucial for a positive user experience and timely decision-making.
• Slow dashboards can lead to frustration, inefficiency, delayed insights, data staleness, and user
disengagement.
• Data Source Optimization:
• Choose between extracts and live connections based on data volume.
• Implement data source filters, aggregation, and optimized queries to reduce data retrieval time.
• Data Modeling for Efficiency:
• Simplify complex data structures to reduce computational load.
• Focus on aggregation, granularity, data reduction, relationship optimization, and indexing.
• Visualization Optimization:
• Simplify visualizations by reducing the number of marks.
• Use appropriate aggregation and limit calculated fields for faster rendering.
• Dashboard Design for Speed:
• Select suitable chart types and streamline layouts.
• Use filters, actions, and parameters judiciously to limit data rendering.
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14. KEY TAKEAWAYS
• Performance Testing:
• Define test scenarios, use representative data, and conduct load testing.
• Analyze response times and iteratively refine performance based on benchmarks.
• Tableau Server Performance:
• Ensure sufficient hardware resources, configure caching, and implement load balancing.
• Monitor resource utilization and maintain regular server maintenance.
• Performance Monitoring:
• Track dashboard usage, query performance, resource utilization, and set up alerting.
• Continuously review and optimize dashboards based on performance data.
• Case Studies:
• Real-world case studies demonstrate the tangible benefits of Tableau best practices.
• Examples include sales optimization, inventory management, customer insights, and more.
• Continuous Improvement:
• Regularly revisit and optimize dashboards and data sources.
• Prioritize user feedback and monitor performance for ongoing enhancement.
• By summarizing these key takeaways, your audience can leave with a clear understanding of
the critical factors that contribute to better Tableau performance and how to implement best
practices effectively.
•
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15. THANK YOU
anitalkb@rediffmail.com
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