Top 35 Tableau Interview Questions for 2023 - IQCode
Tableau for Business Intelligence: Advantages and Basics
Tableau is a software used for business intelligence that presents data visually from various sources to generate interactive and easily shareable dashboards. It simplifies the process of analyzing data and evaluating product-market fit, offering built-in features for data exploration without overwhelming users. Let's explore some of the advantages of using Tableau:
Data Visualization: Instead of complex computations on Excel sheets, Tableau provides beautiful insights, data blending, and dashboarding derived from data.
Interactive Visualizations: Tableau offers drag-and-drop features to help users quickly interact with data. You can find templates created using Tableau on the Tableau gallery page, customize the options, and easily embed tons of information in infographics.
Ease of Implementation: Tableau is reportedly easy to use with its drag-and-drop options. With no coding background or experience in Python, Business objects, or DOMO, you can easily learn Tableau.
Handling Large Amounts of Data: Tableau can handle millions of rows without affecting the dashboard's performance.
Integration with Scripting Languages: With Tableau, you can perform complex data computations using scripting languages like Python and R by importing visuals or packages.
With so much hype around data analytics and visualization, it is crucial to get well-versed with the tools that simplify the data journey. There are several interview questions available ranging from beginner to expert level to help you land a job in your desired field. Take a free mock interview, receive instant feedback, and recommendations.
Basic Tableau Interview Questions:
1. What is data visualization in Tableau?
Differences between Tableau and other BI Tools
Tableau is a data visualization tool used for creating interactive and visually appealing dashboards. Compared to other Business Intelligence (BI) tools such as PowerBI, QlikView, and Spotfire, Tableau has the following advantages:
1. Easy to use: Tableau has a user-friendly interface that allows users to create visualizations without needing extensive knowledge of coding or data modeling.
2. Better connectivity: Tableau can connect to more than 60 data sources, both cloud-based and on-premises, making it easier to access data from various sources.
3. Interactive visualizations: Tableau provides interactive functionality that allows users to manipulate and explore data, creating a more engaging and informative experience.
4. Mobile readiness: Tableau dashboards are responsive and can be viewed on mobile devices, making it easier to access data on-the-go.
Overall, Tableau is an excellent choice for businesses that require a powerful and user-friendly data visualization tool.
Different Tableau Products
Tableau offers a range of products for data analysis and visualization, targeting a wide range of users with different needs and backgrounds. Some of its popular products include:
- Tableau Desktop: A powerful data analysis tool that allows users to connect, visualize and share data insights, create interactive dashboards and reports, and perform advanced calculations.
- Tableau Prep: A data preparation tool that helps users clean, reshape, and combine data from multiple sources before importing it into Tableau Desktop for analysis and visualization.
- Tableau Server: A scalable enterprise platform that enables organizations to securely share and distribute Tableau content, including dashboards, reports, and data sources, with internal and external stakeholders.
- Tableau Online: A cloud-based version of Tableau Server that provides secure, self-service analytics in the cloud, enabling users to access and share data from anywhere.
- Tableau Mobile: A mobile app that allows users to view, interact with and share Tableau dashboards and reports on-the-go, using their smartphones or tablets.
- Tableau Public: A free platform that allows users to publish and share interactive data visualizations and stories with a global community, to showcase their skills and insights.
These products are designed to meet the needs of different users, from individuals and small businesses to large enterprises and government agencies, and offer various features and benefits depending on the user's requirements and preferences.
What is a Parameter in Tableau?
In Tableau, a parameter is a user-defined value that can be used to change certain aspects of a visualization, such as a calculated field, filter, axis range, or reference line. Parameters add flexibility and interactivity to a dashboard by allowing users to change specific values without having to modify the underlying data or the workbook. They can be created by selecting "Create Parameter" from the "Analysis" menu or by right-clicking on a field and selecting "Create Parameter." Once created, parameters can be formatted and customized to fit the needs of the particular visualization.
Understanding Measures and Dimensions in Data Analysis
Measures and dimensions are essential concepts in data analysis. Measures are quantitative values that can be calculated and compared. Examples of measures include sales revenue, profit margins, and customer satisfaction ratings. On the other hand, dimensions are descriptive attributes that provide context for measures. Examples of dimensions include geographic location, product categories, and time periods.
In data analysis, measures and dimensions are used to create actionable insights that help businesses make data-driven decisions. By analyzing measures within different dimensions, businesses can identify trends, patterns, and areas for improvement. For example, a business might analyze sales revenue by product category to identify which products are driving revenue growth or decline. Similarly, analyzing customer satisfaction ratings by geographic location can help businesses identify areas where they need to improve their customer service.
It's important to understand the difference between measures and dimensions and how they can be used together to gain valuable insights. By doing so, businesses can make data-driven decisions that lead to better outcomes.
Continuous and Discrete Field Types
In data analysis, continuous and discrete field types are two main categories for variables. Continuous variables can take on any numerical value within a range, while discrete variables can only take on a countable number of distinct values. Continuous variables are used when measurements can be infinitely divided, while discrete variables are used when measurements can only be expressed as distinct values. Understanding the difference between these two field types is essential in selecting the appropriate statistical methods for data analysis.
Understanding Aggregation and Disaggregation of Data
Aggregation is a process of combining multiple data points into a summary, often for the purpose of analysis or reporting. For example, aggregating monthly sales data into quarterly or annual figures can provide a better overview of a company's performance over a longer period of time.
Disaggregation, on the other hand, involves breaking down summary data into its individual components. This can reveal more detailed insights that may be hidden in the aggregated data. For example, disaggregating total sales figures by product or geographic region can help identify specific areas of strengths and weaknesses in a company's operations.
Both aggregation and disaggregation are important tools for data analysis and can provide valuable insights into complex datasets. The choice between the two depends on the specific research question or business problem being addressed.
Different Types of Joins in Tableau
In Tableau, there are four types of joins that can be used to combine tables:
- Inner Join: This join returns only the matching data from the two tables.
- Left Join: This join returns all the data from the left table and the matching data from the right table.
- Right Join: This join returns all the data from the right table and the matching data from the left table.
- Full Outer Join: This join returns all the data from both tables.
To create a join in Tableau, drag the respective tables onto the canvas and then click on the join field to select the type of join to use. It is important to choose the appropriate join type to ensure the accuracy and completeness of the resulting data.
Different Ways to Connect with a Dataset
To access a dataset, there are various types of connections that can be established. Some common methods are:
1. File Connection: This involves accessing the data stored in a file, such as a CSV, Excel, or JSON file, using a programming language's file IO operations. 2. API Connection: Many modern datasets are stored on remote servers and are accessed via APIs. Establishing connection with the API endpoints and making authenticated requests is necessary to retrieve data. 3. Database Connection: For relational databases like MySQL, connecting to the database server using appropriate credentials, creating a database object, and querying the database for the required data is the way to obtain the data. 4. FTP Connection: In some cases, datasets are stored on remote servers and can be accessed using FTP (File Transfer Protocol) connections.
There may be other ways depending on the type of dataset and the tools or programming languages being used to access it.
Supported File Extensions in Tableau
Tableau supports various file extensions including:
This is Tableau's own proprietary data engine that enables fast and efficient data analysis. It is recommended for use with large datasets.
This is a Tableau Data Extract file, which is a subset of the .hyper file format. It allows for fast data access and analysis because the data is stored in a columnar format.
This is a Tableau Desktop file that contains only metadata about the data source. It specifies how to connect to the data source, what fields to use, and other information that Tableau needs to create a connection.
This is a Tableau Workbook file that contains one or more worksheets, dashboards, and stories. It is used to visualize the data and create reports.
This is a Tableau Packaged Workbook file that contains all the data necessary to open and view a workbook, including data extracts and external files.
Supported Data Types in Tableau
In Tableau, the supported data types include:
- String - Integer - Double - Boolean - Date - Datetime - Geospatial - Currency - Bin
It is important to note that the data type of a field in Tableau can affect how the field is displayed and analyzed. Therefore, it is essential to choose the correct data type for each field when setting up a Tableau data source.
Understanding Groups in Tableau
In Tableau, groups are created by combining related dimension members into higher-level categories. This helps to simplify views and perform analysis at a more aggregated level. Once grouped, Tableau treats the members of the group as a single item, making it easier to compare and contrast groups in visualizations. Groups can be created by selecting the desired dimension members and right-clicking to access the "Create Group" option. It is important to note that groups are mutually exclusive and exhaustive, meaning that each dimension member can only belong to one group and all members must belong to a group. Additionally, groups can be edited and deleted as needed.
Definition of Shelves
Shelves refer to horizontal surfaces, typically made of wood or metal, that are used for the purpose of storing or displaying various items. They are commonly found in homes, offices, libraries, and retail stores as a way to organize and showcase objects such as books, decorative items, and products for sale.
Data Blending in Tableau
Data blending in Tableau refers to the ability to combine data from multiple data sources and visually analyze it in a single view. In simpler terms, it enables users to bring data from multiple data sources together in a single view, allowing for more in-depth analysis and greater insights. This is particularly useful when dealing with data from disparate sources that cannot be combined easily through traditional joins. Tableau's data blending feature enables users to easily connect and blend data from multiple sources, including Excel, Access, SQL Server, and more. Blending data is a powerful technique that enables users to create more detailed and accurate reports, leading to more informed decisions.
Load Testing Tableau: Best Practices
To perform load testing in Tableau, follow these best practices:
- Start by identifying the maximum number of users who will be accessing the system concurrently.
- Create a set of detailed test scenarios that represent real-world usage patterns.
- Use a load testing tool, such as JMeter or LoadRunner, to simulate concurrent user traffic.
- Run the tests and track system performance, including response times and resource utilization.
- Analyze the results and identify potential bottlenecks or areas for optimization.
- Iterate and refine the tests as needed to improve system performance and ensure scalability.
By following these best practices, you can effectively load test your Tableau environment and ensure that it can handle the necessary user traffic without performance degradation.
Reasons for not using Tableau
While Tableau is a popular and powerful data visualization tool, there may be circumstances that would cause someone not to use it. Some potential reasons include:
- Cost: Tableau can be expensive, particularly for small businesses or individuals.
- Steep learning curve: Some users may find Tableau's interface and functions difficult to learn and master.
- Data privacy concerns: For sensitive or confidential data, some users may prefer not to use a third-party tool like Tableau.
- Limited customization: While Tableau offers many customizable features, some users may find that it doesn't offer enough flexibility for their specific needs.
- Compatibility issues: Tableau may not be compatible with certain data sources or systems.
What is Tableau Data Engine?
Tableau Data Engine is a columnar database technology that enables high-speed data analysis and query performance on large data sets. It stores and retrieves data in memory for faster processing and allows for interactive and real-time data exploration. The technology is integrated into Tableau's data visualization software to provide advanced analytics capabilities for business users and data analysts.
The Different Types of Filters in Tableau
Tableau provides several types of filters to help users analyze their data in various ways. Some of the most common filters include:
1. Dimension Filters: These filters allow users to select or exclude values from a specific dimension in their data. For example, filtering a "Region" dimension to only show data for the West region.
2. Measure Filters: These filters enable users to filter data based on the values of a specific measure, such as filtering sales data to only show values above a certain threshold.
3. Relative Date Filters: These filters allow users to filter data based on a relative date range, such as showing data for the last 30 days or the next 6 months.
4. Top/Bottom Filters: These filters allow users to filter data based on the top or bottom values in a specific measure, such as showing the top 10 products by sales.
5. Context Filters: These filters are used to create a temporary table that can be used to filter other views in the same worksheet. Context filters can improve performance in certain cases.
By understanding these different types of filters, users can easily select and manipulate their data to meet their analysis needs.
What are Dual Axes?
Dual axes refers to a chart that contains two y-axes and one x-axis. It allows for two different sets of data to be plotted using different scales, which can be useful for comparing data that varies greatly in magnitude or type. The use of dual axes should be approached with caution as they can often be misleading if not used properly.
Difference between Tree and Heat Map
A tree is a hierarchical data structure that branches into subtrees, while a heat map is a graphical representation of data using colors to indicate values.
In a tree, each node can have multiple child nodes, and the structure is typically used for organizing and visualizing hierarchical relationships.
On the other hand, a heat map shows data values using a color spectrum, with darker colors indicating higher values and lighter colors indicating lower ones. Heat maps are often used to represent data in a two-dimensional grid or matrix format.
Overall, trees and heat maps serve different purposes and represent different types of data, but both can be useful tools for visualizing and analyzing complex information.
Understanding Extracts and Schedules in Tableau Server
Tableau Server is a powerful tool that helps in sharing and managing Tableau content across the organization. Two important concepts in Tableau Server are Extracts and Schedules.
In Tableau, an extract is a snapshot of a subset of data that you can use to improve the performance of your visualization. Extracts can be created from live data connections or imported data sources. The data is extracted into a Tableau-specific format, which optimizes it for fast querying, filtering, and aggregation. You can schedule extracts to automatically refresh on a regular basis to ensure that your visualizations are using the most up-to-date data.
On the other hand, schedules enable you to automate several tasks in Tableau Server. When you create a schedule, you specify the tasks that need to be performed, such as refreshing extracts, sending email notifications, or running scripts. Schedules can be set up to run at specific times or intervals, depending on your requirements.
Using extracts and schedules in Tableau Server, you can ensure that your visualizations are always up-to-date, and you can automate several tasks to save time and improve efficiency.
Components of a Dashboard
A dashboard typically consists of several components, including charts, graphs, tables, and other visual aids. These components are designed to provide a quick and easy way to understand complex data sets and make informed decisions. Some common components of a dashboard may include:
- KPIs (Key Performance Indicators): These are metrics used to measure progress towards specific goals or objectives. - Charts and Graphs: These visual aids are used to display data and trends in a clear and concise manner. - Tables: Tables are used to display large amounts of data in an organized and structured format. - Filters: Filters allow users to drill down into specific data sets or segments. - Alerts: Alerts are used to notify users when certain thresholds or goals are met or exceeded. - Metrics: Metrics are specific data points that are used to calculate KPIs and other performance metrics.
Understanding TDE Files
A TDE file refers to a Tableau Data Extract file. It is a file format used by Tableau to store data in a compressed and optimized form. These files contain data extracts that Tableau uses to provide faster access to data for analysis and reporting purposes. The data extracts can be created from a variety of sources, such as spreadsheets, databases, and cloud-based storage solutions, among others, and can be refreshed on a regular basis to ensure that the data remains up-to-date.
Understanding the Concept of Story in Tableau
In Tableau, the concept of story refers to a way to present data insights in a visual and compelling manner. A story is essentially a sequence of visualizations that are linked together to communicate a message effectively. With story feature, the data analyst can guide the audience through a narrative flow and help them make better sense of the underlying data. Through this feature, the audience can better understand how data insights are derived, and how it impacts different aspects of the business.
#Example of how to create a story in Tableau:
1. Build a dashboard that includes all the visualizations you want to include in your story.
2. Click on the "New Story" button in the toolbar.
3. Drag the visualizations you want to include from the dashboard to the story canvas.
4. Arrange the visualizations in the order you want them to appear in the story flow.
5. Add captions and annotations to provide contextual information for the audience.
6. Preview the story and make any necessary adjustments to the flow or visualizations.
7. Publish the story to Tableau Server or Tableau Online to share it with others in your organization.
Different Tableau File Types
Tableau, a popular data visualization tool, supports several different file types. Some of the commonly used file types in Tableau are:
.twb: A Tableau workbook file that contains all the worksheets, dashboards, and stories in a single file.
.twbx: A Tableau packaged workbook file that includes a .twb file and all the data sources used in the workbook. This file type is useful for sharing workbooks with others as it includes all the necessary data.
.tde: A Tableau data extract file that contains a subset of the data from a larger data source. This file type is particularly useful when working with large data sets.
.tds: A Tableau data source file that contains information about the connection to a data source like server name, database name, user credentials, etc. This file can be used to share data source information without sharing the actual data.
.tfl: A Tableau preference file that contains user preferences like color schemes, font settings, etc.
Tableau Interview Questions for Experienced
27. Can you explain how to embed Tableau views into webpages?
<ul> <li>First, publish the view to Tableau Server or Tableau Online.</li> <li>Next, locate the Share button on the view and click on it.</li> <li>Select the Embed option and customize the embed code as needed.</li> <li>Copy the embed code and paste it into the HTML code of the webpage in which you want to embed the view.</li> </ul>
Note that the embedded view will be fully interactive and allow users to manipulate the visualization as they would in Tableau Desktop or on Tableau Server/Online.
What is the maximum number of rows that Tableau can handle at once?
Tableau can handle a maximum of 15 million rows of data in a single workbook. However, this capacity may vary based on factors such as computer resources and the complexity of the data being analyzed. It is recommended to optimize data sources and utilize Tableau's data extracts feature to improve performance when dealing with large datasets.
Difference between Published and Embedded Data Sources in Tableau
Tableau allows users to connect and work with data from various sources. There are two ways to work with data sources in Tableau: Published Data Sources and Embedded Data Sources.
Published Data Sources: These are data sources that are saved separately from your Tableau workbook. They are shared across multiple workbooks and can be edited and published to Tableau Server or Tableau Online. Published data sources are ideal when multiple workbooks are using the same data source.
Embedded Data Sources: These are data sources that are saved within your Tableau workbook. They are specific to that workbook and cannot be shared or used across other workbooks. Embedded data sources are ideal when you have a small amount of data or when you want to share a single data source and workbook with others.
In summary, the key difference between published and embedded data sources is the ability to share them across multiple workbooks. Published data sources can be shared and edited by multiple users, while embedded data sources are specific to a single workbook.
Drive Program Methodology
The Drive Program Methodology is a structured approach used to manage projects and ensure that project goals are achieved effectively. This methodology focuses on creating a project plan that outlines the scope, timelines, resources, risks, and deliverables of the project. It also establishes how the project will be monitored and evaluated to ensure that it stays on track and meets its objectives. The Drive Program Methodology involves the use of project management tools and techniques to manage the project from start to finish.
Using Groups in Calculated Fields
To use groups in a calculated field, first create the group that you want to reference in the field. Then, create a calculated field by selecting the appropriate aggregation function (e.g. SUM, AVG, COUNT) and specifying the group field as a variable in the calculation.
For example, suppose you have a dataset of sales transactions that includes a "region" field with values for different regions where the sales occurred. To calculate the total sales for each region as a percentage of the overall sales, you could create a group based on the "region" field and then create a calculated field using the following formula:
(SUM([Sales])/SUM([Sales] for all regions)) * 100
In this formula, [Sales] is the name of the field containing the sales data. The aggregation function SUM calculates the total sales for each region, while "SUM([Sales] for all regions)" calculates the overall sales for all regions combined. The result is then multiplied by 100 to obtain a percentage value.
By using groups and calculated fields together, you can gain valuable insights into your data and perform more complex analyses.
Understanding the Use of Joins and Blending in Tableau
When working with data in Tableau, one may come across situations where they need to combine data from multiple sources to create a more comprehensive view. In such scenarios, there are two options: Joins and Blending.
Joins bring data together by combining rows from two or more tables based on a common field. One should use Joins when they need to combine data from different tables into a single table to create a cohesive dataset.
On the other hand, Blending enables users to combine data from different data sources while keep them separate. Blending is useful when one has a primary data source and wants to add supplementary data from a secondary source without modifying the primary data. Blending helps users analyze and correlate data coming from different sources.
Choosing between Joins and Blending depends on the specific data needs of the user and the analysis requirements. Understanding the differences between Joins and Blending helps one choose the right method and create a cohesive view for data analysis.
Understanding Assumed Referential Integrity
Assumed referential integrity refers to a concept in database management where a relational database is designed in a way that it assumes the existence of relationships between tables. This concept assumes that there is no need to explicitly define relationships between tables in a database because the relationships exist based on the data stored in the tables.
In other words, assumed referential integrity assumes that each foreign key value in a table corresponds to a primary key value in a related table. This ensures that data is consistent throughout the database and prevents the creation of inconsistent data.
Assumed referential integrity is a powerful concept that helps to simplify database design and maintenance. However, it is important to note that it should be used carefully to avoid introducing errors into the database.
Understanding Calculated Fields and their Creation
A calculated field is a virtual field in a database that is derived from an expression or formula. It permits you to perform mathematical calculations on existing table data and return the results as a new column.
To create a calculated field in a database, you can follow these steps:
1. Open the database and navigate to the table where you wish to create the calculated field. 2. In the Table Design view, select the column where you want the calculated field to appear. 3. In the Field Properties section, select the Data Type option as Calculated. 4. In the Expression section of the property sheet, enter the formula that you want the calculated field to use. 5. Save the table changes.
You can use calculated fields to compute sums, products, ratios, or any mathematical operation that you want to perform on the existing table data. It saves you from the need to manually calculate and enter data for each record in the table.
Displaying Top Five and Bottom Five Sales in the Same View
To display the top five and bottom five sales in the same view, you can use a combination of the ORDER BY and LIMIT clauses in your SQL query.
Assuming you have a sales table with columns for sale ID and sale amount, you can use the following SQL query to display the top five sales:
SELECT sale_id, sale_amount FROM sales ORDER BY sale_amount DESC LIMIT 5;
To display the bottom five sales, you simply change the ORDER BY clause to ascending order:
SELECT sale_id, sale_amount FROM sales ORDER BY sale_amount ASC LIMIT 5;
To display both sets of results in the same view, you can use UNION ALL to combine the two queries:
SELECT 'Top Sales' as type, sale_id, sale_amount FROM sales ORDER BY sale_amount DESC LIMIT 5 UNION ALL SELECT 'Bottom Sales' as type, sale_id, sale_amount FROM sales ORDER BY sale_amount ASC LIMIT 5;
This will result in a view with columns for type, sale ID, and sale amount, with the top five sales listed first followed by the bottom five sales.
Understanding the Rank Function in Tableau
The Rank function in Tableau is used to assign a rank to each value in a given set of data. The values are ranked according to their position in the set, and the rank is assigned based on the order of these positions. The rank is determined by the values present in the data and the sort order specified. This function is particularly useful when you need to compare the relative position of a value within a set of data.
Example:<br> RANK(SUM([Sales]))<br> This will return the rank of each value in the Sales field within the specified data set.
Differences Between Tableau and Similar Tools like QlikView or IBM Cognos
Tableau, QlikView, and IBM Cognos are all data visualization and business intelligence tools. However, they differ in some ways:
- User Interface: Tableau has a more user-friendly and intuitive interface when compared to QlikView and IBM Cognos. - Data Connectivity: Tableau has a wide variety of connectors to different data sources, which is not the case for QlikView. - Cost: Tableau is more expensive than QlikView and IBM Cognos. - Aggregation: Tableau has a better aggregation performance than QlikView. - Big Data: Tableau is more compatible with Big Data than QlikView or Cognos.
In essence, each tool has its strengths and weaknesses, and the choice of tool depends on the specific requirements of the task, such as data volume and types of visualizations needed.