This is the code repository for Mastering Tableau 2023 - Fourth Edition, published by Packt.
Implement advanced business intelligence techniques, analytics, and machine learning models with Tableau
The authors of this book are - Marleen Meier
This edition of the bestselling Tableau guide will teach you how to leverage Tableau's newest features and offerings in various paradigms of the BI domain. Updated with fresh topics, including the newest features in Tableau Server, Prep, and Desktop, as well as up-to-date examples, this book will take you from mastering essential Tableau concepts to advance functionalities. A chapter on data governance has also been added.
Throughout this book, you'll learn how to use Tableau Hyper files and Prep Builder to easily perform data preparation and handling, as well as complex joins, spatial joins, unions, and data blending tasks using practical examples. You'll also get to grips with executing data densification and explore other expert-level examples to help you with calculations, mapping, and visual design using Tableau extensions.
Later chapters will teach you all about improving dashboard performance, connecting to Tableau Server, and understanding data visualization with examples. Finally, you'll cover advanced use cases, such as self-service analysis, time series analysis, geo-spatial analysis, and how to connect Tableau to Python and R to implement programming functionalities within Tableau. By the end of this book, you'll have mastered Tableau 2023 and be able to tackle common and advanced challenges in the BI domain.
- Learn about various Tableau components, such as calculated fields, table calculations, and LOD expressions
- Master ETL (Extract, Transform, Load) techniques using Tableau Prep Builder
- Explore and implement data storytelling with Python and R
- Understand Tableau Exchange by using accelerators, extensions, and connectors
- Interact with Tableau Server to understand its functionalities
- Study advanced visualizations and dashboard creation techniques
- Brush up on powerful self-service analytics, time series analytics, and geo-spatial analytics
- Find out why data governance matters and how to implement it
- Based on, and expanded to fully cover, the 2023 version of Tableau
- Covers Tableau Exchange as well as Tableau Desktop and Server
- Renewed focus on real-world scenarios
- New and improved coverage of programming language integration
This exciting fourth edition Mastering Tableau 2023 has new expanded hands-on exercises with a focus on solving real-world problems. It has been fully updated to cover everything you need to know to get the most out of Tableau 2023: including Tableau Desktop, Tableau Server, and Tableau Exchange. This edition also contains a brand-new chapter on data governance, as well as new and expanded coverage of Python and R integration, the Optimize Performance tool, and Tableau Prep.
This edition of the bestselling Tableau guide will teach you how to leverage Tableau’s newest features and offerings in various paradigms of the BI domain. Updated with fresh topics including the newest features in Tableau Server, Prep, and Desktop, as well as up-to-date examples and solutions to real-world challenges, this book will take you from mastering essential Tableau concepts to advanced functionalities.
By the end of this Tableau book, you will have mastered Tableau 2023 and you will be able to tackle common and advanced challenges in the data analytics space.
- Reviewing the Basics
- Getting Your Data Ready
- Using Tableau Prep Builder
- Learning about Joins, Blends, and Data Structures
- Introducing Table Calculations
- Utilizing OData, Data Densification, Big Data, and Google BigQuery
- Practicing Level of Detail Calculations
- Going Beyond the Basics
- Working with Maps
- Presenting with Tableau
- Designing Dashboards and Best Practices for Visualizations
- Leveraging Advanced Analytics
- Improving Performance
- Exploring Tableau Server and Tableau Cloud
- Integrating Programming Languages
- Developing Data Governance Practices
This chapter serves as an introductory guide to essential Tableau concepts, catering to both beginners and advanced users. It underscores Tableau's prominence in addressing business intelligence and analytics challenges. The chapter not only delves into foundational terminologies but also offers practical insights into creating worksheets and dashboards. It emphasizes default behaviors and the ability to customize them, all while adhering to best practices. Crucial principles like Measure Names and Measure Values are covered, paving the way for a comprehensive understanding of dimensions and measures, along with intricate calculations at various levels. The chapter notably distinguishes between data source engine computations for row- and aggregate-level calculations, and Tableau's role in handling table-level calculations. The integration of Tableau with diverse devices, including tablets and mobile devices, is also explored.
Key Insights:
- The chapter highlights Tableau's significance as a leading tool for solving business intelligence (BI) and analytics challenges.
- The chapter assists newcomers in quickly getting up to speed with Tableau's basic concepts, serving as a foundational reference.
- While catering to beginners, the chapter also covers enough to make it a comprehensive guide suitable for readers at different proficiency levels.
- The chapter covers essential topics, including creating worksheets and dashboards, connecting data, understanding Measure Names and Measure Values, and grasping crucial Tableau concepts.
- Differentiates between row-, aggregate-, and table-level calculations.
- The chapter addresses the adaptation of Tableau dashboards to various devices, such as smartphones and tablets, showcasing Tableau Mobile in action.
- The chapter sets the stage for the upcoming chapter's focus on data preparation using Tableau's data handling engine, Hyper. It hints at exploring useful models and techniques for data preparation.
Chapter two presents a comprehensive overview of data preparation, a crucial step in the data analysis process. The chapter emphasizes the importance of data cleanliness and introduces the concept of data cleaning as a prerequisite for meaningful analysis. The initial theoretical portion establishes the foundation for understanding data preparation, encouraging careful consideration for deeper insights. The latter part of the chapter offers practical exercises focused on data preparation techniques, enhancing the reader's proficiency in handling data effectively. Topics covered include an exploration of Tableau's data-handling engine, an examination of data preparation techniques using Tableau, and a spotlight on data cleaning using regular expression functions. The chapter also introduces Tableau Prep Builder as a powerful tool for data preprocessing, setting the stage for further exploration in the next chapter.
Key Insights:
- The chapter underscores the critical role of data preparation in data analysis and introduces data cleaning as an essential practice.
- The initial section offers a theory-oriented approach to understanding data preparation, emphasizing its importance and encouraging careful consideration for deeper insights.
- The latter part of the chapter features practical exercises aimed at enhancing the reader's proficiency in data preparation techniques.
- The chapter introduces the concept of using regular expression functions for data cleaning, highlighting their intriguing capabilities that warrant further exploration.
- The discussion starts with an exploration of Tableau's data-handling engine, emphasizing its flexibility and importance for intelligently directing data mining efforts.
- The focus then shifts to utilizing Tableau for data surveying and cleansing, demonstrating its capabilities in handling these critical data preparation tasks. -The chapter lays the groundwork for understanding Tableau Prep Builder, a dedicated interface for efficient data preprocessing, and hints at its forthcoming exploration in the next chapter.
- The chapter briefly introduces Hyper, a feature within Tableau Desktop 10.5, setting the stage for readers to gain an initial understanding of this capability.
- The chapter underscores the goal of becoming adept "data stewards," responsible for ensuring data quality and readiness for analysis.
This chapter delves into the functionalities and applications of Tableau Prep Builder, a robust Extract-Transform-Load (ETL) tool that complements Tableau Desktop. Introduced alongside Tableau Desktop version 2018.1, Tableau Prep Builder, or "Prep," is primarily designed for data preparation. One of its notable advantages is its seamless compatibility with both Tableau Desktop and Tableau Server, enabling effortless execution of data cleaning tasks through visual representations. By providing a Graphical User Interface (GUI), Prep facilitates intuitive data insight generation, allowing users to quickly assess the suitability of datasets for analysis. The chapter covers essential topics such as data connection, the Prep GUI, data quality enhancement, supplementary functionalities, and data exporting.
Key Insights:
- The chapter introduces Tableau Prep Builder as the ETL counterpart to Tableau Desktop. It emphasizes Prep's role in data preparation and its compatibility with both Tableau Desktop and Tableau Server.
- The core purpose of Prep is data preparation. It's highlighted that this tool enables users to clean, refine, and enhance their datasets before analysis, ultimately leading to more reliable insights.
- Prep's visual interface is stressed, similar to Tableau Desktop, enabling users to visualize each step of the data preparation process. This visual representation enhances user understanding and streamlines data cleaning tasks.
- The Graphical User Interface (GUI) of Prep is explored, along with its data connection capabilities. Users can connect their data to Prep and navigate its interface with ease.
- The chapter discusses exercises related to data preparation, focusing on five main components: data cleaning, unions and joins, aggregation, pivoting, and scripting. These steps collectively enhance data quality and its suitability for analysis.
- Beyond basic data preparation, the chapter delves into advanced options. These include incorporating steps from other workflows and performing incremental refreshes, showcasing Prep's versatility in data manipulation.
- The chapter addresses data exporting options in Prep. Processed datasets can be saved in various formats, returned to external databases, or directly transferred to Tableau Server, ensuring flexibility in sharing and utilization.
- The chapter emphasizes that Prep seamlessly integrates with Tableau Desktop, optimizing the transition from data preparation to analysis, and ultimately enhancing the Tableau user experience.
This chapter delves into the intricacies of data manipulation in Tableau, focusing on the decision-making process between utilizing relationships, joins, or blends to integrate datasets. Because connecting Tableau to data often involves more than a single table from a single source, the options of joins become relevant. Additionally, the practice of joining extends beyond similar sources, encompassing disparate data sources and unionizing data with comparable metadata structures. When data lacks a common row-level key for direct joining, blending emerges as an alternative, enabling amalgamation of distinct datasets while preserving detailed information. Notably, the chapter underscores the versatility achieved by manipulating data structures, enabling diverse analytical perspectives from a singular dataset.
Key Insights:
- The chapter highlights the pivotal decision-making process when combining data in Tableau, offering three primary strategies: relationships, joins, and blends. The choice between them depends on factors such as data sources, common key values, and the need for maintaining granularity.
- Joins are essential for merging datasets with matching key values. They can be used not only within the same data source but also across disparate sources, facilitating a unified view of information. The chapter emphasizes the efficiency of Tableau's join mechanisms and reveals the query logic behind join operations.
- Data blending is introduced as a solution for merging data that lacks a common row-level key. This method is especially useful when combining datasets of varying levels of detail, ensuring data integrity while accommodating differences in granularity.
- Manipulating data structures is a powerful technique highlighted in the chapter. It underscores how altering the data's layout can open up varied analytical perspectives from the same dataset, allowing for more nuanced insights and visualizations.
- The addition of the "relationships" feature in Tableau version 2020.2 simplifies data integration by offering a dynamic approach that combines aspects of both joins and blends. This feature enables seamless data combination without the need for explicit method selection.
- Unions are presented as a method to append identically structured data from multiple sheets or sources. This approach streamlines the integration process when dealing with data fragments stored across different locations.
- The concept of scaffolding is introduced, showcasing its role in enhancing data blending outcomes. Scaffolding involves creating auxiliary connections to facilitate effective data blending and enriching the insights derived from the process.
- The discussion on data structures emphasizes the transformational power of pivoting. By rearranging data, users can simplify complex visualizations, making intricate insights easily accessible and enabling better decision-making.
This chapter delves into the intricate realm of table calculations, focusing on their utilization within Tableau. It emphasizes the complexity and depth of this subject. The chapter invites readers to explore the array of options associated with different table calculation functions and their diverse applications. The intention is to provide readers with a conceptual framework for comprehending table calculations, offering practical examples that could prove beneficial in their professional endeavors.
Key Insights:
- The chapter underscores the intricate nature of table calculations.
- The chapter aims to provide readers with a conceptual framework for comprehending table calculations. It encourages readers to explore various table calculation functions and their applications, offering practical examples for real-world contexts.
- A fundamental concept, the text thoroughly discusses partitioning and addressing. It distinguishes between directional and non-directional approaches to addressing, with the Edit Table Calculation interface serving as the pivotal space for configuring these aspects.
- The chapter introduces a range of unique table calculation functions, demonstrating how they can be applied through partitioning and addressing. It provides practical scenarios, including comparing measures over different time periods and generating visualizations like standard deviation bands and rank-based charts.
- Table calculations are applicable in diverse scenarios, including comparing measures over time, analyzing parts-to-whole relationships, and creating visualizations like bump charts. Their utility extends to statistical analysis and enhancing data visualization.
- The understanding gained about partitioning and addressing serves as a foundational knowledge base for delving into advanced topics like data densification and handling big data. This chapter lays the groundwork for upcoming explorations.
This chapter delves into the effective utilization of OData, Data Densification, Big Data, and Google BigQuery as tools for harnessing big data solutions and understanding the concept of data densification. Addressing questions commonly posed by Tableau newcomers, the chapter not only tackles technical aspects like data preparation and blending, but also addresses concerns related to handling null dates in visualizations and comprehending table calculations. The focal points of the chapter include insights on working with big data in Tableau, ensuring optimal performance with extensive data queries, and accessing external resources such as Tableau's help pages, which offer valuable guidance. Furthermore, the chapter introduces key topics such as the OData connector, data densification techniques like domain completion and domain padding, and explores the realm of big data through an illustrative connection between Tableau and Google BigQuery.
Key Insights:
- The chapter emphasizes the importance of the OData connector as a gateway to publicly available datasets, with a specific example using the city of Chicago's data. It highlights how this connector streamlines the process of data analysis by enabling a direct connection between Tableau and OData, showcasing the potential of easily accessing and exploring various data sources.
- The concept of data densification is introduced, with a focus on two techniques: domain completion and domain padding. These techniques are explored in detail, revealing their individual deployment strategies, advantages, and potential challenges. The chapter underscores the importance of understanding and implementing these techniques to enhance data visualizations and analysis.
- Addressing common concerns of Tableau newcomers, the chapter clarifies how Tableau interacts with big data. It discusses strategies to ensure optimal performance even when querying large datasets containing millions of rows. This insight is crucial for users looking to harness the power of big data while maintaining efficient visualization and analysis processes.
- The chapter directs readers to valuable external resources, such as Tableau's help pages and additional links related to data densification and big data. This insight provides readers with avenues for expanding their knowledge and skill set beyond the chapter's content, enhancing their proficiency in handling complex data scenarios.
- The chapter provides a practical use case by demonstrating how Tableau can connect to Google BigQuery. This example showcases the integration of Tableau with a powerful big data solution, enabling users to visualize and analyze large datasets residing in external data warehouses.
This chapter delves into the practice of Level of Detail (LOD) calculations in Tableau, offering insights into this intricate subject that can significantly impact data aggregation for specific computations. The chapter introduces the concepts of FIXED, INCLUDE, and EXCLUDE expressions within LOD calculations, providing a means to create calculations targeting distinct granularity levels. In contrast to earlier Tableau versions where data granularity was constrained by view dimensions, the new LOD approach empowers users to generate calculations at desired levels without the need to include unnecessary dimensions. The chapter underscores that LODs streamline complex scenarios previously addressed through convoluted techniques, offering a more straightforward solution and enabling previously unattainable outcomes.
Key Insights:
- The chapter introduces three essential LOD expressions – FIXED, INCLUDE, and EXCLUDE. These expressions provide users with the flexibility to create calculations that target specific levels of granularity, enabling a more refined analysis of data.
- Prior to LODs, data granularity in Tableau was determined by the dimensions present in a view. The new approach allows users to perform calculations at varying levels without including unnecessary dimensions, offering more accurate and purpose-driven data aggregation.
- In earlier versions, achieving specific outcomes often required convoluted combinations of techniques such as table calculations and data blends. LODs simplify these scenarios by providing a direct and efficient way to perform calculations that were previously challenging or even impossible.
- It's important to understand the order of operations among LOD expressions. FIXED LODs take precedence over EXCLUDE and INCLUDE LODs. This hierarchy influences how LODs and filters should be applied to ensure the desired outcomes.
- Unlike the default logic that tied calculations to visible dimensions in the view, LODs allow users to compute measures based on any dimension, regardless of its presence in the view. This customization empowers analysts to explore data in ways that weren't feasible before.
This chapter delves into advanced visualization concepts and introduces readers to Tableau Exchange, Extensions, Accelerators, and Connectors. Assuming familiarity with fundamental chart types, the chapter aims to enhance commonly used visualizations and introduce lesser-known but valuable chart types. The chapter underlines the significance of aesthetics in dashboard design, promoting Beauty Bias and Design Driven Consumption principles. The associated workbook offers an array of visualization types for exploration, including bar charts, scatterplots, treemaps, Gantt charts, and more. The chapter also highlights the importance of selecting the right visualization technique to amplify the narrative of the data.
Key Insights:
- Introduces readers to advanced visualization concepts and the Tableau Exchange platform, which houses extensions, accelerators, and connectors. This section assumes familiarity with fundamental chart types and aims to enhance the visualization techniques readers already use while introducing new, valuable chart types.
- The chapter underscores the importance of aesthetics in dashboard design. The author emphasizes the concepts of Beauty Bias and Design Driven Consumption, suggesting that a well-designed visualization can enhance the impact of the content. While content remains paramount, a visually appealing design can potentially make the data more engaging and persuasive.
- The associated workbook offers readers the opportunity to explore a wide range of visualization types beyond the basics. From bar charts and scatterplots to Gantt charts and treemaps, the chapter provides an extensive collection of visualization examples. The emphasis is on choosing the most appropriate visualization technique that effectively conveys the story within the data.
- The chapter delves into creating custom background images for visualizations, including techniques to generate grids using XML-generated SVG files. It also explores the integration of polygons onto background images, showcasing innovative ways to enhance visual elements within a dashboard.
- The latter part of the chapter delves into Tableau Exchange, a platform offering extensions, accelerators, and connectors. It briefly introduces readers to Einstein Discovery, a separate product by Salesforce. This section provides a glimpse into more advanced visualization tools and resources beyond the core Tableau offerings.
- A recurring theme is the significance of selecting the right visualization technique to effectively convey the data's narrative. The chapter advises readers to choose the visualization that aligns with their data's characteristics and the story they aim to tell. To aid in this decision-making process, a reference to a Tableau Public dashboard is provided, which showcases visualization types based on the nature of the data.
This chapter delves into Tableau's mapping capabilities, both internal and external, showcasing their potential through custom polygons, heatmaps, and layered maps. The author shares their experience of conducting Tableau classes, where mapping demonstrations consistently garner appreciation and intrigue from newcomers. The chapter emphasizes that Tableau's default mapping functions are potent and adaptable, enabling significant achievements without exiting the user interface. However, the author contends that these capabilities only scratch the surface. With expert guidance, users can unleash the true potential of their workbooks, both in terms of functionality and aesthetics.
Key Insights:
- The chapter underscores how Tableau's default mapping functionality serves as a significant attraction for users. Placing geographical locations on a Tableau worksheet generates informative maps instantly. Adding data metrics, such as sales figures, to the Color shelf dynamically adjusts map colors, illustrating a strong out-of-the-box mapping foundation.
- While Tableau's basic mapping tools are impressive, the author highlights that many users only scratch the surface of its capabilities. By providing expert guidance, the chapter encourages readers to explore less apparent features and techniques, unveiling the platform's potential for creating intricate and visually appealing maps.
- The chapter emphasizes that users can significantly expand Tableau's mapping capabilities without leaving the platform's interface. By integrating Google for latitude and longitude data lookup, users can enrich their maps without switching between applications.
- The chapter explores advanced techniques such as crafting custom polygons and creating heatmaps. By using .shp files, users can define their own geographic areas of interest, enabling a higher degree of map customization.
- Readers are introduced to the power of map layering and dual axes. This feature enables the creation of multi-layered map visualizations, providing deeper insights into geographic data relationships.
- The chapter goes beyond Tableau's built-in capabilities by showcasing the integration of external technologies. By connecting to WMS servers and exploring tools like Mapbox, users can access additional mapping resources and features.
- The chapter offers readers an excursion into the realm of polygons. This section dives into the intricacies of working with polygons on maps, providing insights into how to manipulate and utilize these shapes effectively.
This chapter is dedicated to the art of using Tableau for compelling presentations. It emphasizes that Tableau authors are essentially storytellers, using data analysis to uncover narratives that matter. Regardless of the topic, from Airbnb to the Zika virus, the objective remains consistent: crafting a meaningful story. This chapter aims to equip readers with a diverse set of tools and techniques to harness Tableau's potential for creating engaging, impactful presentations. The discussed topics encompass optimizing image quality, seamless integration with PowerPoint, embedding interactive Tableau instances within presentations, leveraging animations, and employing story points and dashboards to enhance storytelling. The chapter underscores that effective presentation is a fusion of creativity and data-driven insights, setting the stage for a deeper exploration of advanced dashboarding techniques in the subsequent chapter.
Key Insights:
- The chapter underscores that Tableau authors are akin to storytellers, emphasizing that data analysis is not just solving puzzles but uncovering impactful narratives. Whether the subject is light-hearted or life-changing, the core purpose is to tell a compelling story that resonates.
- The chapter equips readers with an array of tools and methods to elevate their Tableau presentations. From optimizing image quality to seamlessly integrating Tableau with PowerPoint, it emphasizes the fusion of creativity and data-driven insights to engage and captivate audiences.
- A standout insight is the possibility of embedding live Tableau visualizations directly within PowerPoint. This integration bridges the gap between data analysis and presentation, allowing real-time interaction and enhancing engagement during presentations.
- The chapter introduces Tableau animation as a means to bring data to life. Animations not only add visual appeal but also aid in conveying complex data trends and patterns, making the information more accessible and understandable.
- The significance of utilizing story points and dashboards in presentations is highlighted. These tools offer dynamic ways to guide audiences through data narratives, enhancing the flow and impact of the presentation.
This chapter is a comprehensive guide that navigates readers through the intricacies of creating compelling and informative data visualizations. The chapter immerses readers in the fundamental principles of data visualization, concentrating on the realm of dashboard design. By amalgamating insights from influential figures in the field, such as Alberto Cairo, Stephen Few, and Edward Tufte, the chapter embarks on an exploration of visualization design theory, formatting techniques, color guidelines, and optimal visualization types. The chapter further delves into the art of simplifying visualizations and elucidates key dashboard design principles, including favored layouts like the Golden Rectangle, quad, and small multiple. Techniques for leveraging sheet selection, implementing actions and download buttons, organizing item hierarchies, and utilizing the "Used In" feature are dissected to enhance user experience and facilitate effective dashboard organization. The culmination of this knowledge is demonstrated through an analysis of successful Makeover Monday dashboards, highlighting the practical application of the principles discussed.
Key Insights:
- "Designing Dashboards and Best Practices for Visualizations" is an instructive guide that emphasizes the importance of aesthetics and informative data presentation in the realm of data visualization.
- The chapter is dedicated to exploring essential principles and best practices for designing effective dashboards. It draws inspiration from prominent figures in data visualization such as Alberto Cairo, Stephen Few, and Edward Tufte, among others.
- The chapter covers a range of topics, including visualization design theory, formatting rules, color guidelines, and optimal visualization types. It underscores the significance of simplicity in visualizations and introduces key principles for effective dashboard design.
- Three popular layout styles – the Golden Rectangle, quad, and small multiple – are discussed, each offering distinct advantages in organizing and presenting data.
- Techniques to enhance user experience and organization within dashboards are elucidated. These include sheet selection methods, action implementation, download buttons, item hierarchies, and the strategic use of the "Used In" feature.
- The chapter culminates in a practical analysis of successful Makeover Monday dashboards, providing real-world context for the application of the principles covered.
This chapter delves into the realm of advanced self-service analytics, presenting it as a potent form of business intelligence where individuals can independently query datasets for insights, bypassing the traditional IT backlog. The chapter offers an insightful exploration of three distinct use cases, each illuminating the potential of self-service analytics. The first case involves unraveling the correlations among global indices, shedding light on intricate market dynamics on a global scale. The second case employs geo-spatial analytics to dissect Chicago traffic violations, identifying patterns and opportunities for enhancement. Expanding on this, the third case employs distance measures in geo-spatial analysis to pinpoint optimal housing locations based on key factors. Throughout these scenarios, a central lesson emerges: meticulous planning, data familiarity, and adaptive strategies form the bedrock of successful self-service analytics projects. The chapter concludes by urging readers to continuously refine their dashboards, embrace flexibility, and apply the gained insights to their own analytics pursuits.
Key Insights:
- Self-service analytics serves as a powerful form of business intelligence, enabling individuals to directly interact with datasets rather than relying on IT teams. This empowerment leads to quicker insights and data-driven decision-making.
- The chapter presents three compelling use cases. First, the analysis of global indices' correlations unveils complex market dynamics. Second, geo-spatial analytics applied to Chicago traffic violations uncovers patterns for potential improvements. Third, extending geo-spatial analysis with distance measures demonstrates data-driven decision-making for optimal housing locations.
- The success of self-service analytics projects hinges on a well-structured approach. Meticulous planning, data familiarity, and employing descriptive statistics are vital steps to ensure meaningful results.
- Dashboards are iterative and evolving creations. Readers are encouraged to start with a minimum viable product, deliver basic functionality, gather feedback, and continue development based on changing requirements and stakeholder needs.
- The chapter emphasizes that there are diverse paths to achieving self-service analytics goals. Embracing flexibility and adapting to varying scenarios are essential traits for success.
- Readers are urged to reflect on the insights gained and consider applying them to their own self-service analytics endeavors. Learning from the author's experiences, they're reminded that there's no one-size-fits-all approach.
- While self-service analytics empowers individuals, collaboration with IT remains important for more complex implementations that may be beyond the scope of self-service.
This chapter delves into the optimization of Tableau dashboards to ensure swift loading and seamless user interactions. Beyond mastering Tableau's functionalities, users frequently encounter performance concerns, symbolized by the frustrating moments spent waiting for data to load or queries to execute. However, the chapter offers a solution-oriented approach. It asserts that, with proper design, Tableau dashboards can exhibit impressive performance, even when dealing with substantial datasets. The chapter's primary objective is to equip readers with techniques for crafting workbooks that exhibit rapid loading times and responsive behavior. Topics explored encompass the significance of the performance-recording dashboard, hardware considerations, efficient data source connections, adept handling of extracts, judicious application of filters, streamlined calculations, and miscellaneous strategies for enhancing overall performance. The chapter systematically covers these themes, ensuring a comprehensive understanding of how to tackle performance challenges.
Key Insights:
- The chapter highlights that after gaining familiarity with Tableau's features, users often encounter performance issues that can lead to slow-loading dashboards and sluggish user interactions. This challenge is relatable to those who have experienced the frustration of waiting for data to load or queries to execute.
- The primary objective of the chapter is to provide readers with practical techniques for optimizing Tableau dashboards. It underscores that well-designed dashboards can exhibit excellent performance even when handling substantial amounts of data.
- The chapter emphasizes the significance of the performance-recording dashboard. This tool assists in identifying the root causes of performance bottlenecks, enabling users to pinpoint sources of slowness and inefficiency.
- The chapter provides insights into the role of hardware in achieving good Tableau performance. Additionally, it offers techniques to maximize performance even in scenarios where optimal hardware is lacking, demonstrating resourceful ways to enhance performance regardless of the computer's capabilities.
- Efficient handling of data sources is a critical aspect of performance optimization. The chapter discusses best practices for connecting to data sources, joining and blending data, and working with extracts. It aims to equip users with strategies to manage data efficiently and reduce loading times.
- The impact of filters and calculations on dashboard performance is explored comprehensively. The chapter provides guidance on using filters effectively to minimize data processing demands and explores ways to create efficient calculations that don't compromise dashboard responsiveness.
- Beyond the core topics, the chapter addresses miscellaneous performance-related considerations. It offers insights into setting performance expectations and provides further strategies for enhancing overall dashboard responsiveness.
This chapter delves into the distinct features and functionalities of Tableau Server and Tableau Cloud, which serve as platforms for sharing, collaborating, and distributing content developed in Tableau Desktop or Tableau Public. Both solutions facilitate secure access to real-time and scheduled data visualizations, with Tableau Server requiring user maintenance and Tableau Cloud being a fully cloud-hosted service supported by Amazon Web Services. This chapter primarily focuses on the interaction between Tableau Desktop authors and Tableau Server. The chapter covers subjects such as publishing data sources to Tableau Server, web authoring, workbook maintenance, and additional features of Tableau Server. By emphasizing the transition of dashboards from Tableau Desktop to Server, the chapter guides users through essential steps for leveraging Tableau Server effectively.
Key Insights:
- The chapter provides an in-depth overview of Tableau Server and Tableau Cloud, highlighting their significance as platforms for sharing, distributing, and collaborating on content created in Tableau Desktop and Tableau Public. These solutions enable secure access to real-time and scheduled data visualizations, catering to different needs and deployment preferences.
- The chapter emphasizes a key distinction between Tableau Server and Tableau Cloud. While both serve similar purposes, Tableau Server requires self-maintenance, whereas Tableau Cloud is fully hosted in the cloud by Tableau and supported by AWS infrastructure. This distinction holds importance for organizations deciding between the two options.
- The primary focus of the chapter is guiding Tableau Desktop authors in transitioning their dashboards to Tableau Server effectively. It walks users through essential steps, including publishing data sources to Tableau Server, utilizing web authoring capabilities, and maintaining workbooks on the platform.
- The chapter emphasizes the role of user filters in Tableau Server, enabling authors to control data access based on user clearance. This functionality enhances data security and ensures that users can only access the data relevant to their roles and permissions.
- Practical features like alerts, subscriptions, and commenting are discussed, showcasing how Tableau Server facilitates collaboration and engagement among users. These features enhance communication and interaction around the shared data visualizations.
This chapter delves into the powerful potential of integrating R and Python with Tableau, expanding the realm of analytics capabilities. After a comprehensive exploration of Tableau's functionalities, this chapter introduces the game-changing aspect of programming integration. Through the integration of R and Python, users can tap into limitless possibilities for data manipulation and analysis that go beyond Tableau's native features. Two scenarios illustrate this advantage: first, addressing complex financial calculations by utilizing the capabilities of R or Python; and second, seamlessly incorporating sentiment analysis within Tableau through programming integration. The chapter covers the installation, integration, and basic syntax of both programming languages within Tableau, equipping readers to leverage functions ranging from simple calculations to advanced techniques like regressions and even custom machine learning models. Although the chapter provides a foundation, it also acknowledges the vast unexplored potential of programming integration within the business intelligence community.
Key Insights:
- The chapter underscores the significance of integrating programming languages, R and Python, with Tableau. This integration provides users with an array of advanced analytics capabilities that surpass the limitations of Tableau's native features. It introduces the idea that when Tableau's built-in calculated fields fall short, users can turn to programming to harness the potential of R and Python.
- The text illustrates the advantages of programming integration through practical scenarios. By utilizing R or Python, users can seamlessly perform calculations outside Tableau and integrate the results into their dashboard. Similarly, sentiment analysis is presented as another use case where external Python calculations can be seamlessly integrated into Tableau, providing a consolidated solution.
- The chapter guides readers through the installation and integration process for both R and Python within the Tableau environment. It empowers readers to establish connections between Tableau and these programming languages, setting the foundation for further exploration.
- The chapter empowers readers by imparting essential skills to connect to and work with R and Python from within Tableau. It emphasizes that readers, after completing the chapter, will possess the ability to write basic syntax and leverage functions from both programming languages. This skill set enables users to perform tasks ranging from basic calculations to complex regressions and machine learning implementations.
- While the chapter introduces the basics of programming integration, it acknowledges that the true potential of this capability remains largely unexplored in the business intelligence community. The chapter encourages readers to explore beyond the covered topics and highlights the broader scope for innovative applications of R and Python integration in Tableau.
The final chapter delves into the crucial domain of data governance and its alignment with regulatory compliance using Tableau Server. The chapter distinguishes itself by shifting focus from exploring specific Tableau features to an examination of data governance's intricacies within Tableau. This entails determining data accessibility, tracking dashboard interactions, identifying dashboard data elements, tracing data origins, and more. Key topics covered encompass the essence of data governance, fundamental principles underpinning it, its integration within the Tableau framework, and practical illustrations for enhanced understanding.
Key Insights:
- The chapter underscores the transformation of data governance from a limited IT task focused on data cataloging to a multifaceted responsibility shared across an organization. The growth in data storage, analyst roles, and data utilization has catalyzed the need for comprehensive rules and guidelines to ensure effective data management and regulatory compliance.
- The increasing prominence of data governance is exemplified by the implementation of data protection laws and regulations, such as GDPR for the EU. The chapter emphasizes that companies are now liable to fines for insufficient data governance and highlights the media attention drawn by data security breaches, reinforcing the significance of robust data protection measures.
- The chapter delves into how data governance principles integrate into Tableau's functionalities. It explores facets like data accessibility, dashboard interactions, data components identification, and data provenance. This bridges the gap between data governance's theoretical framework and its practical implementation within Tableau's analytical environment.
If you feel this book is for you, get your copy today!
With the following software and hardware list you can run all code files present in the book.
| Chapter | Software required | Free/Proprietary | Download Link to the software | Hardware specifications | OS required |
|---|---|---|---|---|---|
| 1 and 2 | Tableau Desktop | 14 days free trial | Tableau Desktop | Any | Windows/macOS |
| 3 | Tableau Prep Builder | 14 days free trial | Tableau Prep Builder | Any | Windows/macOS |
| 4 to 13 | Tableau Desktop | 14 days free trial | Tableau Desktop | Any | Windows/macOS |
| 14 | Tableau Server and Tableau Cloud | 14 days free trial | Tableau Server | Any | Windows/macOS |
| 15 | R, Rstudio, Python, TabPy and Tableau Server | 14 days free trial | R, Rstudio, Python, TabPy, Tableau Server | Any | Windows/macOS |
| 16 | Tableau Server | 14 days free trial | Tableau Server | Any | Windows/macOS |
You can get more engaged on the discord server for more latest updates and discussions in the community at Discord
If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost. Simply click on the link to claim your free PDF.
Free-Ebook
We also provide a PDF file that has color images of the screenshots/diagrams used in this book at GraphicBundle
Marleen Meier is an accomplished analyst and author dedicated to unlocking the power of statistics and data. She studied at Westfälische Wilhelms University in Germany, and later contributed her skills to both the University and the Vrije Universiteit Amsterdam. She worked as a data analyst at Ingenico (formerly Global Collect), then moved to ABN AMRO Clearing where she continues today. She has shared her Tableau insights at conferences in the Netherlands and the UK and is co-leader of the Dutch Tableau User Group. She is an expert in risk analysis, data visualization, ML, A.I, and data quality and control. Marleen has lived and worked in Amsterdam, Chicago, and Singapore, and enjoys sport, time with friends and family, and board games