The Ultimate Guide: 200+ Tableau Interview Questions and Answers to Secure a Tableau Role
Hello Vizards, I’m Sateesh Kumar, a Tableau Consultant living in Sydney, Australia. You can connect me on Linkedin. I’ve started my career in 2006 as a SAP Business Objects intern and worked up my way to Tableau Architect. I share my Data visualization/Storytelling and Tableau knowledge at Vizard. I wrote this Tableau Interview Questions guide based on my personal experience.
I’ve been on both sides of the table, as an interviewee and also as an interviewer. I have put together this comprehensive Tableau Interview Question and Answers guide to help anyone attending Tableau interviews to succeed.
How it all Started:
Few people in my Linkedin network started asking me how to answer some Tableau interview questions and I patiently explained everyone. But when I noticed an increase in requests, I thought to myself this is not going to scale, and then I put up a post asking if anyone was interested in the Tableau Interview Questions guide I was putting together.
Not in my wildest dreams did I expect such a terrific response to my Linkedin post (530k+ reach, 4000+ likes, and comments).
I originally intended to document my own Tableau interview questions experience (being a contractor I attend 10X more interviews than an employee would). But the terrific Linkedin post response brought me a new responsibility to meet your expectations so I started beefing up the content.
I dug the internet to find relevant content, gone through word by word to curate the best content (credited links and references) to make it more relevant, updated and useful for you.
The result of 130+ hours of time and effort and 28 versions of the document is this comprehensive guide covering 200+ Tableau interview questions consisting of Tableau technical, data visualization, behavioral, thought process, Client communication, HR, and culture fit questions to give you a complete 360-degree interview experience.
Note: This is a massive 22,000-word Tableau Interview Questions guide which might take few hours to read. For your convenience, I’ve put together the complete guide (article has only 112 technical questions, pdf has all 210) in a handy pdf for you to reference when you want. Grab it below.
Table of Contents
Table of Contents
There’s no way to predict what Tableau Interview Questions you’ll be asked. Also, it’s not possible to give a script for every scenario since every individual’s experience, situation, interviewer experience, and project requirement is different.
You find direct answers to technical questions below. But for HR/Behavioral/Culture fit/thought process questions (in the pdf) you may find guidelines how to answer them based on my own experience. Please prepare answers to those questions using those guidelines and find your own voice.
Answers below represents my own experience and thoughts, but not related to any of my employers or Tableau.com.
While I tried my level best to write accurate answers, inadvertently I would have missed or incorrectly answered a few. Appreciate if you can email me email@example.com so I can correct them.
Be at least 20 to 30 minutes early to the Tableau interview place so you are comfortable, calm and relaxed. While waiting in the lobby, take this opportunity to find some interesting stuff around. It could be a company magazine on the coffee table, a beautiful wall art, great decor, a bookshelf or something else which is worth talking about.
The point is, use that observation to spark a conversation when you meet and greet the interviewer. This trick not only helped me to start the pep talk but also made both of us comfortable to begin the interview on a positive note and keep the conversation going.
Before every interview, be prepared with the scenarios below.
- When you’re asked a question, take a deep breath — think and recollect the situations you would have faced. It’s ok to take a moment to think and answer the question.
- Have a thorough understanding of what you mentioned in your resume and projects. They might ask you some deeper questions related to what you mentioned.
- You might also be asked if you know anything about their company. And, you will definitely get brownie points for researching about the company. This shows that you’re keen on the role.
- Research about the company, their growth, any recent news, products/services they offer, role advertised and any associated information.
- It is good to read up on the job requirements and responsibilities so that you can align your answers based on what they are seeking.
- Check the interviewer LinkedIn profile, you get an idea of their work experience, their role and you could guess the type of questions you might expect.Show that you are confident the way you speak, listen and even sit.
- Draw on personal experiences and projects. Shows that you’re experienced.
- Admit to not knowing something and politely ask for the answer.
- Focus on identifying the problem instead of getting straight to the solution.
- Share relevant information instead of talking too much in detail about everything.
- Express towards the end that you’re very much interested in the role.
In one sentence, Interview is all about selling yourself to convince the interviewer you are worth hiring and you can bring value to the company.
I would like to set the stage with this key thing I would want you to keep in mind during the interview (or later at work).
What do clients look for in a Tableau interview or at the job?
We all intend to deliver our best work to the client once we join them, but how can you prove your worth even before you start working for them? how can you differentiate yourself from the rest of the candidates? that’s where you have an opportunity to shine in the interview.
You have to demonstrate and convince the interviewer you are worth hiring by providing value upfront during the interview. Your job at this point is to show your prospect client/employer how hiring you will result in them making more money or save money (or whatever the net result of your work is).
You need to STAND OUT in the interview. You need to blow their minds.
From my experience, counter-intuitively companies will not hire you for your ability to build dashboards/ your data visualization skills alone (although that’s important as part of your job), they hire you “expecting you to solve business problems for them”. That’s their underlying intention.
Regardless of your level of experience or skills, your client/interviewer wants to know “what value can you bring to the table”. I have over a decade experience, I know X Y Z tools, I’m a great learner, I love cats etc are great to know, but what is the “value you can bring to the table”?
It’s not about you and your skills, it’s about them (company, client, their products/services, their business).
I’m stressing this because this is often overlooked and so important to get the best outcomes in the interview/ project and great client relationship.
You have to zoom out a little bit and start looking at the forest for the trees. The bigger picture, why do/did client have to hire you? What’s the WHY behind their project?
They have some business problems to solve, they have to go from point A to point B. Help them get there and you will be a rock star. Speak their language. Speak what they want to listen.
It could be helping them understand where the revenue is leaking, or which department has most expenses and why, how are we projected for our sales next season or what can we do to improve our customer experience based on what we learned from data etc. you get the idea.
The better you understand the business/client’s pain points, the better you can solve them and make them happy. That’s the holy grail of great client relationship and more opportunities in the future. That’s how you will stand out from your peers and become a valuable resource client’s rave for.
Sateesh all good, but how can I show value upfront without knowing joining them/ knowing anything about their business?
There is an opportunity if you think out of the box. Do a thorough analysis on the company, their products or services, their challenges, google about the company and you may find some news or some media mention about them or you may know a friend who works/worked for that company or a quick search on Linkedin shows employees working at that company, connect to them with a custom request explaining your situation and request them for some information.
You should be able to find something that leads to some valuable discovery worth noting and talking about during the interview. When you are given a chance to ask questions at the end of the interview, talk about what you discovered, ask questions about their products/services, what business problems are they facing, what’s the WHY, the pain behind the project etc. and offer a mini solution for the problem they may have. It’s a quick win.
This shows your upfront work, your preparedness, curiosity, and keenness in the role.
You get brownie points here. This approach of offering upfront value and proving your worth to get hired makes you stand out among other candidates.
Remember our dashboard or report skills are means to an end, you will have to sell them on your ability to solve business problems.
1. What is data visualization?
Data Visualization is a form of visual communication. At its core, it’s about encoding aggregations and representing data visually, in order to gain insight from the data. Data visualization allows for immediate insight by tapping into our mind’s powerful visual processing system.
A primary goal of data visualization is to communicate information clearly and efficiently via graphs and charts. Effective visualization helps users analyze and reason about data and evidence. It makes complex data more accessible, understandable and usable.
Think of all the popular data visualization works out there—they all tell an interesting story. Maybe the story was to convince you of something. Maybe it was to compel you to action, enlighten you with new information, or force you to question your own preconceived notions of reality. Whatever it is, the best data visualization, big or small, helps you see what the data has to say.
2. How do you explain data visualization to a layman?
Data visualization is the process of displaying data in graphical charts so that businesses can find insights quickly and easily to make better decisions for good outcomes.
3. Why did you choose data visualization?
Data Visualization helps us solve business problems faster or better. They bring some new insights which often have a monetary value associated with it. Visualization often enables problems with the data to become immediately apparent.
A visualization commonly reveals things not only about the data itself but also about the way it is collected. With an appropriate visualization, errors and artifacts in the data often jump out at you. For this reason, visualizations can be invaluable in quality control too.
By practicing data visualization, it gives me an opportunity to make a direct impact on the businesses bottom line.
4. Why Do We Visualize Data?
Humans respond to and process visual data better than any other type of data. In fact, the human brain processes image 60,000 times faster than text, and 90 percent of information transmitted to the brain is visual.
We visualize data to harness the incredible power of our visual system to spot relationships and trends. Seeing the numbers visually gives us a quick understanding of how business is performing so we can make data-backed decisions.
5. How do you define a dashboard?
A dashboard is a visual display of information that helps us visually display, track and analyze key performance indicators (KPI) and metrics to monitor the health of a business.
A dashboard is also defined as a visual display of the most important information needed to achieve one or more objectives that have been consolidated on a single screen so it can be easily monitored and understood at a glance.
The dashboard gives the ability is to spot the exception, highs, and lows and then drilling into the specifics all in one place.
6. Briefly explain the process you follow in a data visualization project from start to finish?
- First begin with understanding client business, technical and business requirements.
- Understand the context of the visualization and audience who we intend to build.
- Capture user stories/business problems they are trying to address.
- Understand the data model, data analysis, and data preparation.
- Storyboard/sketch initial prototype to solve the above problems.
- Choose an appropriate visual that effectively brings the story live and answers the business questions.
- Remove unnecessary elements, keep it clean and simple.
- Showcase the visualization to stakeholders, seek feedback and iterate.
- Data reconcile to make sure the numbers in the visualization are matching to the numbers in the source.
- Unit testing, System testing, UAT, approval and deploying to production.
- User training, support, and maintenance.
7. How do you choose a visual to represent data?
First, we have to think about what it is that we want our audience to be able to do with the data (function) and then create a visualization (form) that will most efficiently and most accurately convey the data’s meaning.
When you display the data visually using the right chart, you’ll be able to easily uncover meaningful patterns and correlations from a set of otherwise indecipherable numbers.
Below are the four data relationships that are used when visualizing the data:
A comparison tries to set one set of variables apart from another and display how those two variables interact, like the number of visitors to five competing websites in a single month.
A composition tries to collect different types of information that make up a whole and display them together, like the search terms that those visitors used to land on your site, or how many of them came from links, search engines, or direct traffic.
A distribution tries to lay out a collection of related or unrelated information simply to see how it correlates, if at all, and to understand if there’s any interaction between the variables, like the number of bugs reported during each month of a beta.
A relationship tries to show a connection or correlation between two or more variables through the data presented, like the market cap of a given stock over time versus overall market trend.
Here is a handy diagram created by Dr.Andrew Abela that can be used to determine the right chart type to use in your data visualization.
8. What is a Column chart?
A column chart visualizes data as a set of rectangular columns, their lengths being proportional to the values they represent. The vertical axis shows the values, and the horizontal axis shows the categories they belong to. In multi-series column charts, values are grouped by categories.
9. What is a Bar chart?
A bar chart visualizes data as a set of rectangular bars, their lengths being proportional to the values they represent. The horizontal axis shows the values, and the vertical axis shows the categories they belong to.
So, the bar chart is a vertical version of the column chart. In multi-series bar charts, values are grouped by categories. They are also preferred when the category names are long or also to show ranking.
10. What is a Line chart?
A Line Chart is the most popular type of the data visualization. As a rule, it is used to emphasize trends in data over equal time intervals, such as months, quarters, fiscal years, and so on.
It displays information as a series of data points called ‘markers’ connected by straight line segments. The X-axis holds the categories while the Y-axis holds the values.
11. What is a Stacked Bar chart?
The Stacked Bar Chart is composed of multiple Bar series stacked horizontally one after another. The length of each series depends on the value in each data point. Stacked Bar Charts make it easier to follow the variation of all the variables presented, side by side, and watch the change in their total.
12. What is a Stacked Column Chart?
The Stacked Column Chart comprises several Column series stacked vertically, one on another. Each series’ length is determined by the value in each data point. Stacked Column Charts are a great option if you need to observe the change in each of several variables simultaneously and in their sum.
You should pick this type of chart only in case the number of series is higher than two. With just one series, it would be the Column Chart.
13. What is a Scatter Plot?
A scatter plot chart will show the relationship between two different values. This is particularly useful to identify outliers or to understand the correlation in the data.
The datasets need to be in pairs with a dependent variable and an independent variable. The dependent (the one the other relies on) becomes the y-axis and the independent, the x.
When the data is distributed on the plot, the results show the correlation to be positive, negative or nonexistent. Adding a trend line will help show the correlation and how statistically significant the correlation is. Example: With trend lines, you can answer such questions as whether profit is predicted by sales, or whether average delays at an airport are significantly correlated with the month of the year.
Read more here
14. What is an Area Chart?
An area chart is a chart type based on the line chart: it also shows information as a series of data points connected by straight line segments, but the area between the X-axis and the line segments is filled with color or a pattern.
The area chart emphasizes the magnitude of change over time and can be used to highlight the total value across a trend. For example, an area chart displaying profit over time can emphasize the total profit.
15. What is a Choropleth Map?
Choropleth Map displays divided geographical areas or regions that are colored, shaded or patterned in relation to a data variable. This provides a way to visualize values over a geographical area, which can show variation or patterns across the displayed location.
16. What is a TreeMap?
A tree map is a visualization that displays hierarchically organized data as a set of nested rectangles, parent elements being tiled with their child elements. The sizes and colors of rectangles are proportional to the values of the data points they represent.
17. What is a Slope Graph?
A slope graph is a lot like a line graph, in that it plots change between points. However, a slope graph plots the change between only two points, without any kind of regard for the points in between.
It is based on the idea that humans are fairly good at interpreting changes in direction. Decreases and quickly rising increases are easily detected.
18. What is a Waterfall chart?
The Waterfall Chart type is generally used to understand the influence of several positive and negative factors on the initial value. Points are utilized to display the process of the initial value change and can be in one of the three states: increase, decrease, or total (subtotal).
The first and last columns in a Waterfall Chart usually represent totals. The intermediate ones stand for the changes
19. What is a Heat Map?
A heat map is a visual representation of data that uses color-coding to represent different values in a matrix.
Essentially, this type of chart is a data table with rows and lines denoted by different sets of categories. Each table cell can contain a numerical or logical value that determines the cell color based on a given color palette.
Heat maps are convenient data visualization for comparing categories, using color to emphasize relationships between data values that would be much harder to understand in a simple table with numbers.
20. What is a Bullet graph?
A bullet graph is a variation of a bar graph developed to replace dashboard gauges and meters. A bullet graph is useful for comparing the performance of a primary measure to one or more other measures. It shows a distribution showing progress towards a goal behind the bar.
21. What is a Gantt Chart?
The Gantt Chart is a type of bar diagram used to illustrate plans and activity schedules of any project. It is a project management tool. Gantt Chart consist of bars stretched along the time axis.
In this type of diagram, each bar represents a certain task within the framework of the project in question. The ends of the bar stand for the task’s start and finish time, and the length reflects duration. The Gantt Chart’s vertical axis is a task list.
22. What is a Histogram chart?
A Histogram visualizes the distribution of data over a continuous interval or certain time period. Each bar in a histogram represents the tabulated frequency at each interval/bin. The total area of the Histogram is equal to the number of data.
Histograms help give an estimate as to where values are concentrated, what the extremes are and whether there are any gaps or unusual values. They are also useful for giving a rough view of the probability distribution.
23. What are some of the charts you avoid and why?
3d charts– They skew the visual perception of the numbers making them difficult or impossible to interpret or compare. It also introduces unnecessary chart elements like side and floor panels which is not only a cognitive load on the brain but also inaccurate.
Pie charts – This is not being as accurate as bar charts or position-based visuals. With pie, we are judging areas and angles which is much more difficult than the length in a bar chart. Our eyes cannot ascribe quantitative values to areas and angles properly.
Donut charts– This is similar to the pie chart, but with a hole cut out in the middle so that it looks like a donut. Because there’s a hole in the middle, we don’t judge values by angle anymore. Instead, we have to compare one arc length with to another arc length. Our eyes cannot ascribe quantitative values to arc length properly.
24. Can you explain some Data Visualization design best practices?
From Storytelling with data book.
- Form follows function: First, we want to think about what it is we want our audience to be able to do with the data (function) and then create a visualization (form) that will allow for this with ease.
- Don’t assume that two different people looking at the same data visualization will draw the same conclusion.
- Leverage pre-attentive attributes to make those important words stand out.
- Offer your audience visual affordances as cues for how to interact with your visualization.
- Highlight the important stuff and eliminate distractions.
- Make it legible and clean.
- Label and title as appropriate, so there’s no work going back and forth between a legend and the data to decipher what is being graphed.
- Whatever data is required for context, but doesn’t need to be highlighted, push it to the background.
- Employ attributes like color, thickness, size, position, labeling, text and annotation to emphasize and de-emphasize components throughout the visual.
- If there is a conclusion you want your audience to reach, state it in words.
From The Functional Art book
- Adding tons of special effects to a graphic will not make it any better if it lacks good information. The special effects take away space that could have been used to highlight other angles of the story.
- Don’t create fancy visualizations with tons of bubbles, lines, bars, and filters and expect readers to figure the story out by themselves and draw conclusions from the data. That’s not an approach to information graphics. Not all readers are data analysts.
- Don’t just show stuff; explain the main points, focusing the reader’s attention on the most interesting parts of the information.
From Good Charts book
- Include four elements in all charts: title, subtitle, visual field, and source line. Within the visual field include axes, labels, and sometimes captions and legends.
- Make all the elements support the visual. Use them to highlight the idea, not to describe the chart’s structure.
- Remove ambiguity. Make sure each element has a single purpose that can’t be misinterpreted.
- Minimize the number of colors you use. Gray works for contextual and second-level information and for structural elements such as grid lines.
- Limit eye travel. Place labels and legends in close proximity to what they describe.
Tableau Desktop Interview Quesitons
25. Why did you choose to work on Tableau or What makes it different from other tools?
There are many reasons why Tableau is the best data visualization tool. Here are a few.
- Tableau helps us see and understand the data easily which enable us to make data-driven decisions.
- It has the best implementation speed with simple drag and drop and enhanced analytic workflow experience for users.
- Powerful, intuitive yet Simple
- Fast turnaround time and variety of features.
- Makes discovering deep insights for management easy, which in turn drives Organizations.
- It brings the creative and logical side together to tell the story of the data.
- Clear visuals, user-friendly and easy to understand.
- It enables pure self-service and reduces dependency on a central IT Team.
- It helps data paint meaningful pictures.
- Connecting to multiple data sources is such an ease.
- Creates stunning visualizations in seconds and helps to connect dots and create a fulfilling story.
- A great community around to support and learn from.
26. What are interactive dashboards?
Dashboards which enable us to interact with various elements like filters, parameters, actions, and slice and dice the data to get better insights or answer complex questions.
27. What is Show Me feature in Tableau?
Show Me is a useful feature in Tableau which creates a visual based on the fields already used in the view and any fields you’ve selected in the Data pane.
28. What is a Tableau data pane?
A pane on the left side of the workbook that displays the fields of the data sources to which Tableau is connected.
29. What are different ways of connecting data?
You can connect Live or as an Extract. It depends on the project requirements.
In general, if the business needs up to minute/ live data updates to the dashboard, we use Live connections. But it comes with performance impact due to constant expensive data refresh operations.
In all other cases, extracts are preferred. Extract files are a local copy of a subset or entire data source that you can use to share data, work offline, and improve database performance. They can be scheduled to run at off-peak hours avoiding slow load times.
30. How do you prepare data for reporting?
The first step towards exploring your data with Tableau is by looking at how the data is structured. The more granular the data is the easier it is for us to work with, visualize and answer business questions. If the data is already aggregated, we will be limited by the capability of visualization.
31. What is marks card in Tableau?
A card to the left of the view where you can drag fields to control mark properties such as type, color, size, shape, label, tooltip, and detail.
32. What are shelves in Tableau?
They are named areas to the left and top of the view. You build views by placing fields onto the shelves. Some shelves are available only when you select certain mark types. For example, the Shape shelf is available only when you select the Shape mark type.
33. What is a Published data source?
It contains connection information that is independent of any workbook and can be used by multiple workbooks.
34. What is a data source page?
A data source page is where you can set up your data source. The Data Source page generally consists of four main areas: left pane, join area, preview area, and metadata area.
35. What is a format pane in Tableau?
A format pane contains formatting settings that control the entire worksheet, as well as individual fields in the view.
36. When do you use horizontal and vertical containers?
Horizontal and vertical containers are used to organize different visual components in a dashboard. It’s easy to format and keep visuals consistent within containers. These containers create an area in the dashboard where objects automatically adjust their size and position based on the other objects in the container.
37. What is Page shelf?
Tableau provides a distinct and powerful tool to control the output display known as Page shelf. As the name suggests, the page shelf fragments the view into a series of pages, presenting a different view on each page, making it more user-friendly and minimizing scrolling to analyze and view data and information. You can flip through the pages using the specified controls and compare them at a common axle.
38. How many ways to use parameters in Tableau
We can use parameters with filters, calculated fields, actions, measure-swap, changing views and auto updates.
39. What is a Hierarchy?
When you connect to a data source, Tableau automatically separates date fields into hierarchies so you can easily break down the viz. You can also create your own custom hierarchies. For example, if you have a set of fields named Region, State, and County, you can create a hierarchy from these fields so that you can quickly drill down to levels in the viz.
40. What are Automatic Hierarchies?
When you connect to a data source, Tableau automatically separates date fields into hierarchies so you can easily break down the view by year, quarter, month, etc. We can observe a plus symbol adjacent to the field those are part of the hierarchy.
41. What is an Analytics Pane?
The Analytics pane provides quick and easy access to common analytic objects in Tableau. You can drag reference lines, forecasts, trend lines, and other objects into your view from the Analytics pane, which appears on the left side of the workspace.
42. What are groups?
Group simplifies large numbers of dimension members by combining them into higher-level categories. You can create a group to combine related members in a field. For example, if you are working with a view that shows average test scores by major, you might want to group certain majors together to create major categories. English and History might be combined into a group called Liberal Arts Majors, while Biology and Physics might be grouped as Science Majors.
43. What are sets?
Sets are custom fields that define a subset of data based on some conditions. A set can be based on a computed condition, for example, a set may contain customers with sales over a certain threshold. Computed sets update as your data changes. Alternatively, a set can be based on a specific data point in your view.
44. What are the differences between groups and sets?
Groups simplify large numbers of dimension members by combining them into higher-level categories.
Sets create a custom field based on existing dimensions that can be used to encode the view with multiple dimension members across varying dimension levels.
45. What is a bin?
Sometimes it’s useful to convert a continuous measure (or a numeric dimension) into bins.
Any discrete field in Tableau can be considered as a set of bins. For example, suppose you create a view with Profit on Rows and State on Columns.
You could consider the State field as a set of bins -each profit value is sorted into a bin corresponding to the state from which the value was recorded. But if you want to see values for Profit assigned to bins without reference to a dimension, you can create a numeric bin, with each individual bin corresponding to a range of values.
46. How do you handle Null Values?
Null values along an axis are indicated in the lower right corner of the view. Click the indicator to open a dialog box that will help you decide how to handle these values. The options available in the dialog box depend on the type of data in the view.
47. What are Rank and Percentile?
Use rank when you are interested in seeing the order of values in a list. For example, you could apply a rank table calculation to a table to see which product category ranked highest in sales over a four-year period.
Percentile is available as an aggregation and as a table calculation. To use one of the built-in percentile aggregations, right-click a measure in the view and select Measure > Percentile and then choose one of the numeric options.
48. What is a Secondary Axes?
In addition to blending multiple measures on the same axis, you can add a secondary axis or dual axis to a view to better compare measures of different scales.
49. What are Aggregated Extracts?
You can optimize your extracts by only including the data you need. Specifically, you can exclude columns, create filters to limit the number of rows, aggregate data, and roll updates.
50. What are Incremental Extract Updates?
When you import all or some of your data into Tableau’s data engine, you create a data extract. After you create the initial extract, you can set up an incremental refresh so that importing new data doesn’t require you to rebuild the entire extract. An incremental refresh can be defined by the values in a specified column. For example, if you’ve created an extract that has date values, you can define the incremental refresh to only add new rows if there are additions in the date column.
51. How to use Data Interpreter to Prepare the Data Source for Analysis?
Sometimes, the format of your data makes it difficult to analyze in Tableau. For example, your data might include sub-tables, hierarchical headers, extraneous headers, and footers, or blank rows and columns.
The Data Interpreter draws out sub-tables and removes some of that extraneous information to help prepare the data source for analysis. Note: You are prompted to use the Data Interpreter only if Tableau detects sub-tables or extraneous information and if your connector supports it.
52. What are the different products Tableau offers?
Tableau offers 5 products.
Tableau Desktop: Tableau desktop is considered as “the gold standard” in visual analytics. It is a self-service data visualization tool that lets you analyze virtually any type of structured data and produce highly interactive, and stunning dashboards, and reports in just minutes.
With tableau desktop, you can directly connect to data from multiple data sources: data warehouse, flat files, cloud application or web sources. Tableau desktop brings all your data together into one single view. It helps identify patterns, opportunities, and ask questions to drive business profitability. Tableau makes analyzing data and making better decisions faster.
Tableau Server: Tableau Server lets you centrally manage, collaborate and securely host the dashboards published from Tableau desktop. It empowers the business with the freedom to explore data in a trusted environment—without limiting them to pre-defined questions, or chart types.
Users can edit and update the workbooks and dashboards online or Server but cannot create new ones. However, there are limited editing options when compared to desktop.
Tableau server connects to any data source securely – whether on-premise or in the cloud. Publish and share your data sources as live connections or extracts for everyone to use.
The Tableau platform is easy to deploy, scale, and monitor. Easily track and manage content, users, licenses, and performance. Quickly manage permissions for data sources and content and monitor usage visually.
Tableau Online: Tableau Online is an analytics platform fully hosted in the cloud. It enables you to publish dashboards and share your discoveries with anyone. Invite colleagues or customers to explore hidden opportunities with interactive visualizations and accurate data. All easily accessible from a browser or on the go with Tableau mobile apps.
Tableau Reader: Tableau Reader is a free desktop application that you can use to open and interact with data visualizations built in Tableau Desktop. Filter, drill down and discover. But this needs the packaged workbook file to be shared across the users which not only is a security issue but also loses track of the correct version.
Tableau Public: Tableau Public is a free service that lets anyone publish interactive data visualizations to the web. Visualizations that have been published to Tableau Public can be embedded into web pages and blogs, they can be shared via social media or email, and they can be made available for download to other users.
53. What type of data limitations does Tableau Public have?
Tableau Public can only connect to few data sources like Microsoft Excel, Microsoft Access, and multiple text file formats. It has a limit of 1,000,000 rows of data that is allowed in any single file.
54. Explain the difference between Tableau Workbook, Story, Dashboard, and Worksheets?
Workbooks and sheets: Tableau uses a workbook and sheet file structure, much like Microsoft Excel.
A Workbook contains your views, dashboards, and stories, and data connection. You can include local resources, such as background images and custom geocoding if they reside in a location that the server or other Tableau users cannot access.
A worksheet contains a single view along with shelves, legends, and the Data pane. A worksheet is where you build views of your data by dragging and dropping fields onto shelves.
A dashboard is a collection of several worksheets and supporting information shown in a single place so you can compare and monitor a variety of data simultaneously. For example, you may have a set of views that you review every day. Rather than flipping through each worksheet, you can create a dashboard that displays all the views at once.
A story is a sheet that contains a sequence of worksheets and dashboards that work together to convey information. Use stories to make your case more compelling by showing how facts are connected, and how decisions relate to outcomes. You can then publish your story to the web, or present it to an audience.
Within a workbook, you can create new sheets, clear an entire worksheet, duplicate sheets, hide or show a worksheet, and delete a sheet. Tableau has several ways to view and organize the sheets in your workbook.
From Worksheet, we can access Source Data Dimensions, Measures, and Custom Fields.
From Dashboard, we can access Worksheet but we cannot access dimensions measures directly.
From Story, we can access Dashboard and Worksheets but we cannot access dimensions measures directly.
55. Difference between Tiled and Floating in Tableau Dashboards
A dashboard is a collection of worksheets and objects on a single sheet so you can compare and monitor a variety of data simultaneously. Dashboard objects can be tiled or floating. Tiled objects are arranged in a grid while floating objects can be layered on top of other objects.
56. What is an embedded data source and when it is used?
When you publish a workbook to Tableau Online or Tableau Server, you can publish the data it connects to in 2 ways.
As an integrated part of the workbook which is embedding data source connection information within the workbook. This is used when we know a data source is not shared with other dashboards.
As separate, standalone data source which is used when we have to share the same data source with multiple dashboards.
57. How to automate reports using Tableau software?
When you publish workbooks that connect to extracts, you can set up a schedule for updates (refreshes) for those extracts, so the views in those workbooks stay current.
The ways you can set up and manage extract refresh schedules depends on where you publish and on the original data type.
When you publish to Tableau Server, the schedule runs and is managed on the server.
When you publish to Tableau Online: If extracts are from cloud data (for example, Google Analytics or Salesforce), schedules run and are managed on Tableau Online.
If extracts are from on-premises or web data connector (WDC) data, you set up and manage refresh schedules using Tableau Bridge.
Read more here
58. When do we use Join vs Blend?
Use JOIN when you have multiple tables/views from the same data source to be used in the dashboard. This happens at the source level.
Use BLEND when you have multiple tables/views from different data sources to be used in the dashboard. This happens locally.
59. What is a Dimension?
Tableau treats any field containing qualitative, categorical information as a dimension. This includes any field with text or dates values.
60. What is a Measure?
A measure is a field that is dependent on a value of one or more dimensions. Tableau treats any field containing numeric (quantitative) information as a measure.
61. What are the differences between the blue pills and the green pills in Tableau? or What is the difference between discrete and continuous in Tableau?
There are two types of data roles in Tableau – discrete and continuous.
Discrete data roles are values that are counted as distinct and separate and can only take individual values within a range. Examples: names of customers. Discrete values are shown as blue pills on the shelves and blue icons in the data window.
Continuous data roles are used to measure continuous data and can take on any value within a finite or infinite interval. Examples: unit price, revenue or order quantity. Continuous variables behave in a similar way in that they can take on any value. Continuous values are shown as green pills.
62. What are Filter Actions?
Use the Filter Actions filter to show related information between a source sheet and one or more target sheets. This type of action works well when you are building guided analytical paths through a workbook or in dashboards that filter from a master sheet to show more details.
63. What are some of the new features of Tableau 10.5 version?
Here are some of the new features in Tableau 10.5
Hyper: Hyper is Tableau’s new in-memory data engine technology, designed for fast data ingest and analytical query processing on large or complex data sets. With up to 3X* faster extract creation, you always have the latest data. Up to 5X* increase in query performance keeps you in the flow.
Viz in Tooltip: Engage with your data at a deeper level and maximize dashboard real estate with Viz in Tooltip. Hover over a mark to display details-on-demand while staying in context of the original view.
Nested projects: You can now nest projects within projects, and customize permissions at each level, or choose to use a top-down permission structure. Now it’s easier to organize your workbooks, so everyone in your organization can find what they are looking for.
Power trend lines: Better understand power relationships between variables with drag and drop power trend lines, bringing light to your physics, biology, astronomy, economic data, and more.
Improved workbook version compatibility: Now you can revert your workbooks to an older version with improved workbook version compatibility. Other users in your organization can open and edit a workbook even when they aren’t using the latest version of Tableau Desktop (down to Tableau 10.2).
You can know more here.
64. What formats can we download Views or Workbooks from the server?
The download formats available to you depend on permissions granted by Tableau content owners and site administrators.
Image: Downloads an image of the view in a .png format.
Data: Opens a new tab in the browser window and displays the view’s data in summary and detail. You can then download the data as a comma-separated value (.csv) file.
Crosstab: Downloads the view, or the selected sheet in a dashboard, as a .csv file you can open in Microsoft Excel.
PDF: Downloads a PDF of selected sheets. If you’re downloading a dashboard to PDF format, web page objects aren’t included.
Tableau Workbook: Downloads a workbook you can open with Tableau Desktop.
Note: Downloading extremely large amounts of data can affect server performance and might not complete successfully. If you encounter these issues, try exporting the data directly from the underlying data source.
65. What are the different file extensions in Tableau?
Below are some of the file type’s extensions in Tableau.
Workbooks (. twb)
The. twb is the most common file extension used in Tableau, which presents an XML format file and comprises all the information present in each dashboard and sheet like the fields are used in the views, styles, and formatting applied to a sheet and dashboard. But this workbook in itself does not contain any data. Workbooks hold one or more worksheets, plus zero or more dashboards and stories.
Packaged Workbooks (.twbx)
Tableau packaged workbooks have the .twbx file extension. A packaged workbook is a single zip file that contains a workbook along with any supporting local file data sources and background images. This format is the best way to package your work for sharing with others who don’t have access to the data. Save all the sheets, their connection information and any local resources (e.g., local file data sources, background images, custom geocoding, etc.)
The primary advantage to using .twbx files is that analysis can be performed without network/internet connections to your data because your data is already present on your computer in the packaged file.
Data Source (.tds)
Tableau data source files have the .tds file extension. Data source files are shortcuts for quickly connecting to data sources that you use often. Data source files do not contain the actual data but rather the information necessary to connect to the data source as well as modifications you’ve made in the Data pane such as default properties, calculated fields, groups, and so on.
Packaged Data Source (.tdsx)
Tableau packaged data source files have the .tdsx file extension. A packaged data source is a zip file that contains the data source file (.tds) described above as well as any local file data sources such as Extract files (.tde), text files, Excel files, Access files, and local cube files. Use this format to create a single file that you can then share with others who may not have access to the original data stored locally on your computer.
Extract (.hyper or .tde)
Depending on the version the extract was created in, Tableau extract files can have either the .hyper (10.5+) or .tde file extension. Extract files are a local copy of a subset or entire data set that you can use to share data with others when you need to work offline and improve performance.
Tableau bookmark files have the .tbm file extension. Bookmarks contain a single worksheet and are an easy way to quickly share your work.
Tableau Map Source (.tms)
You can import a Tableau Map Source (.tms) that someone has shared with you into a workbook, and then use it to create custom map views.
Tableau Preferences (.tps)
Whenever you have to create custom palettes to match your company branding (besides the default Tableau color palettes), you can also create and use your own custom color palettes by modifying the Preferences.tps file that comes with Tableau Desktop.
When you save the workbook and restart Tableau Desktop, the color palette names you added to Preferences.tps appear in the Select Color Palette drop-down list which you can use a new palette like you would any other.
Tableau Datasource Customization (.tdc)
When you use a connector for a database that supports SQL, Tableau generates SQL statements that are tuned for that database. Because Tableau has no representation of the SQL dialect used by an ODBC data source, it must infer the supported syntax through a variety of tests.
You can use Tableau customizations to fine tune this connection information in order to significantly improve performance during the execution of Tableau’s complex SQL statements.
For example, customers can define the SQL statements in which a schema identifier can be used or the level at which their data source conforms to the SQL standard, changing how Tableau queries for metadata, whether it can use temporary tables, how many concurrent queries the data source can support etc. These customizations are made using Tableau’s Datasource Connection (TCD) files.
66. What are some of the Tableau limitations you are aware of?
Here are some limitations in Tableau.
- Parameters don’t accept multiple selections.
- Parameters don’t update dynamically when it is connected to a database field.
- Cannot reference Measure Names/Measure Values in calculations.
- Cannot define the width of columns individually.
- Cannot control the display, selection, order, sorting of columns in view data option for a worksheet view.
67. How is a workbook version controlled in Tableau?
Version control of a workbook can be done in 2 ways.
One at the desktop level where you keep copies of changes on a file system or a 3rd party version control system like Microsoft TFS.
Second is at the server level when you publish a workbook or a data source, a version is saved in the revision history for Tableau Server and Tableau Online. You can revert to a previous version at any time.
68. What is the difference between filter and parameter?
A filter acts directly on a dimension or measure and restricts the domain of the field. For example, to only show California or New York in a State dimension or only show Sales between $100 and $200. There are a lot of options for filters. You can include or exclude members of a dimension, use a wildcard for the member name, choose the top N, given another measure, or use a condition (true/false) to choose what is in and what is out.
A parameter, like is a variable. You can then use that variable inside calculations to change the calculation. If you filter by a calculated field, you essentially have a parameter controlling a filter. Parameters have almost the same UI options as filters, but they are single-valued, so you have options for radio buttons, but not checkboxes. There are also sliders and drop downs. Parameters are global, so can affect calculations for all data sources and connections in a workbook.
Read more here
69. What are Table Calculations
Table Calculations are computations that are applied to the values in the entire table and are often dependent on the table structure itself. For example, in a sales environment, you can use table calculations to compute the running total of sales across a specified date range or to compute each product’s contribution to the total sales in a quarter.
Top 10 table calculations here
70. What is a LOD expression?
Level of detail expressions supports aggregation at dimensionalities other than the view level. With the level of detail expressions, you can attach one or more dimensions to an aggregate expression.
Unlike table calculations, totals, or reference lines, level of detail expressions are computed in the data source. On the plus side, this lets you avoid the overhead of bringing all the data from the database to your computer over the network. With large data sources, this can be a huge performance gain. On the minus side, this can cause Tableau to run more complicated queries (for example, containing many joins), and if the underlying data source is slow, performance can suffer.
71. Explain Types of Filters in Tableau – Quick, Global, Context, At Source, On Dimensions and Measures?
Quick Filter: Quick Filter Creates an interactive selection panel on the right top corner of the data view. We can implement Quick Filter on Dimensions or Measures. The interactive selection panel provides the options based on the source data.
Example: Quick Filer on Order Date with Yearly Consolidation provides the list of YEARS available from SOURCE DATA for user selection.
Global Filter: Global Filter helps to share the same filter using the same source data across multiple (or) all worksheets within a workbook. We can apply a global filter for all the worksheets using the same data (or) It can be applied to only selected worksheets.
Example: Filter with Complex calculation in one worksheet can be applied to some other worksheet which using the same source data to avoid repetitive work.
Context Filter: Context Filter helps to pass the criteria of the filter from one level to another related level.
Example: Top 10 Customers in the country by their purchases; here the context is ‘Top 10’. We can pass this context to know ‘Top 10’ Customers by Purchases in a State within the same country.
Filter at Source: After connecting with the data source, filtering the data before getting into the worksheet is called filtering at the source. This helps in improved performance because the unwanted data eliminated.
Example: Selecting only one year worth of data from few years of historical data after connecting with the source
Filter on Measure: This filter provides options – a range of values, at least, at most and special. We can select any option that helps to select the right data or eliminate the unwanted data. A special option is to deal with Null values.
Example: Filter on Sum of Sales.
Filter on Dimension: This filter provides options – General, Wildcard, Condition and Top/Bottom. We can select any option that helps to select the right data or eliminate the unwanted data. We can also create own formula then use it in this filter options – Condition and Top/Bottom for the data selection needs. Condition and Top/Bottom provides a channel from dimension to measure to get the required data.
Example: Filter on Order Date then selecting sales through dimension.
72. How do we do performance testing in Tableau?
Create a Performance Recording to record performance information about the main events you interact with the workbook. Users can view the performance metrics in a workbook created by Tableau. Help> Settings and Performance> Start Performance Recording Help> Setting and Performance > Stop Performance Recording.
Reviewing the Tableau Desktop Logs located at C:\Users\\My Documents\My Tableau Repository. For live connection to the data source, you can check log.txt and tabprotosrv.txt files. For an extract, check tdeserver.txt file.
73. How can we do load testing in Tableau?
You can do performance testing on Tableau with Tableau Server load-testing solution called TabJolt. You can use TabJolt to push a heavy workload onto Tableau Server to give it a jolt and study how the server bends or breaks under load. While TabJolt is not supported by tableau directly, it has to be installed using other open source products.
74. How do you improve dashboard performance?
- Use an extract Extracts are a quick and easy way to make most workbooks run faster.
There is nothing else that comes close to the efficiency gained using an extract. If you don’t absolutely need live data, extracting is the best bet.
- Reduce the scope whether you’re creating a view, dashboard, or story, it’s tempting to pack a lot of information into your visualization because it’s so easy to add more fields and calculations to the view and more sheets to the workbook. But the result can be that the visualization becomes slower and slower to render.
Remember that each worksheet runs one or more queries against the data, so the more sheets, the longer it takes to render the visualization.
- Reduce the number of marks on the view While there is no hard and fast rule on what defines “too many marks,” be aware that more marks mean that more processing power and memory is required to render them. You can find the number of marks by looking at the lower left of the Tableau Desktop window in the status bar.Watch out for large crosstabs and for maps with complex custom polygons. Keep in mind that too many data points on a view can also reduce the visual analytics value by causing information overload.
To avoid this problem, compile related views and connect them with action filters so that you can go from an overview to a more granular view as you explore the data.
- Use Booleans and integers output in calculations when you create calculated fields; the data type you use has a significant impact on the calculation speed. Integers and Booleans are generally much faster than strings. If your calculation produces a binary result (for example, yes/no, pass/fail, over/under), be sure to return a Boolean result rather than a string.
- Limit the data being introduced to each worksheet if you are not planning on using a set of rows, you should filter them out of the data set as early as possible. If your table contains all sales, and you only want to look at US sales, use a data source filter to restrict data to the US alone. Less data is quicker execution.
- Use Context filter creating one or more context filters improves performance as users do not have to create extra filters on a large data source, reducing the query-execution time.
You can create by dragging a filed into ‘Filters’ tab and then, Right-Click that field and select ‘’Add to Context”.
You can create by dragging a filed into ‘Filters’ tab and then, Right-Click that field and select ‘’Add to Context”.
- Hide unused fields Even though we bring all fields from the tables; we end up only using few of them. So, it’s better to remove them from the extract by selecting “Hide All Unused Fields” in worksheets. Bringing fewer data should always improve performance.
- Eliminate any non-essential components from the visualization.
This refers to values that would appear on the Pages, Filters, and Level of Detail Shelves. If they are purely there for the user to see if they scroll over a point, then they aren’t not only not adding any value but also impacting the performance.
- Reduce filters usage Take it easy on parameters and quick filters. Despite the name, too many quick filters will actually slow you down. Particularly if you set them to use ‘Only Relevant Values’ and you’ve got lots of discrete lists. For every Quick filter which is added, Tableau has to execute the query in the database to find out which values to display.
Alternatively, you can use Action filters which do not create any additional database query.Avoid quick filters with Dropdown or Multi-select on high-cardinality columns (columns which has more number of unique values; example: e-mail ids) as it takes too much amount of time to return the result. Alternatively, you can use wildcard match option.
Alternatively, you can use Action filters which do not create any additional database query. Avoid quick filters with Dropdown or Multi-select on high-cardinality columns (columns which has more number of unique values; example: e-mail ids) as it takes too much amount of time to return the result. Alternatively, you can use wildcard match option.
- Assuming Referential Integrity In some cases, you can improve query performance by selecting the option to Assume Referential Integrity from the Data menu. When you use this option, Tableau will include the joined table in the query only if it is specifically referenced by fields in the view.Using this setting is appropriate when you know that your data has referential integrity but your database is not enforcing or cannot enforce referential integrity. If you have the option of configuring referential integrity in your database that is a better option than using this setting because it can improve performance both in the database and in Tableau. The Assume Referential Integrity option in Tableau can only affect performance on Tableau’s end. If your data does not have referential integrity and you turn on this setting, query results may not be reliable.
- Avoid using custom SQL When you connect to multiple tables in your database, Tableau writes a query that has been optimized for that data source. Unless you are very experienced with join operation optimization, writing custom SQL may cause Tableau’s connection to slow down.
- Remove unnecessary calculations, parameters, and sheets.
- Use indexes in the database and use those fields for filtering
- Push heavy lifting (calculations) to database
- Keep report simple and answering specific questions, it not only puts a cognitive load on the reader but also load on tableau engine.
More resources here
75. How does version compatibility work in Tableau?
From Tableau 10.5, now you can revert your workbooks to an older version with improved workbook version compatibility. Other users in your organization can open and edit a workbook even when they aren’t using the latest version of Tableau Desktop (down to Tableau 10.2).
76. How does Compatibility between Tableau Desktop and Tableau Server work?
A workbook created with any version of Tableau Desktop can connect to any supported version of Tableau Server. But once you are connected, compatibility can be an issue depending on the version you are using and the action you’re performing.
77. How can you restrict access at the row level?
When you share workbooks with others by publishing them to Tableau Server or Online, by default, all users who have access to the workbooks can see all of the data shown in the views. You can override this behavior by applying a type of filter that allows you to specify which data “rows” any given person signed in to the server can see in the view.
Read more here
78. What is Aggregation & Disaggregation?
The process of viewing numeric values or measures at higher and more summarized levels of the data is called aggregation. When you place a measure on a shelf, Tableau automatically aggregates the data, usually by summing it. You can easily determine the aggregation applied to a field because the function always appears in front of the field’s name when it is placed on a shelf.
According to Tableau, disaggregating your data allows you to view every row of the data source which can be useful when you are analyzing measures that you may want to use both independently and dependently in the view. For example, you may be analyzing the results from a product satisfaction survey with the Age of participants along one axis. You can aggregate the Age field to determine the average age of participants or disaggregate the data to determine at what age participants were most satisfied with the product.
79. What is Assume referential integrity?
Referential integrity is a concept which ensures that relationships between databases/tables remain consistent, i.e. it ensures that the references to data are valid. You can improve query performance by selecting this option from the data menu. When you use this option, Tableau will include the joined table in the query only if it is specifically referenced by fields in the view.
80. Where can you use global filters?
Global filters can be used in sheets, dashboards and in stories.
81. What is Context Filter?
Context filter establishes a filtering hierarchy where all other filters present refer to the context filter for their subsequent operations. The other filters now process data that has been passed through the context filter.
Creating one or more context filters improves performance as users do not have to create extra filters on a large data source, reducing the query-execution time.
You can create by dragging a filed into ‘Filters’ tab and then, Right-Click that field and select ‘’Add to Context”
82. What are the Limitations of context filters?
The context filter is not frequently changed by the user – if the filter is changed the database must re-compute and rewrite the temporary table, slowing performance.
When you set a dimension to context, Tableau creates a temporary table that will require a reload each time the view is initiated.
83. How many maximum tables can you join in Tableau?
The maximum number of 32 tables can be joined in Tableau. A table size must also be limited to 255 columns (fields).
84. Explain about Actions in Tableau?
Using Actions, we can create navigation between high-level details to low-level details to support analysis. This navigation can take place between worksheets (or) within dashboard between worksheets (or) between two different workbooks.
85. What are the types of Tableau Actions? and how are they different?
There are three options to implement Actions in Tableau: Filter, Highlight, and URL
Filter: The filter option helps to navigate from source worksheet to target worksheet. One worksheet can carry high-level details and the target worksheet can have detail level information in relation to the source worksheet.
Example: Source worksheet can have category level information and target worksheet can have the category, subcategory details. Upon selection of a category, we can navigate to target worksheet to see sub-category level details.
Highlight: The highlight option helps to navigate within dashboard between source worksheet and target worksheet. One worksheet can carry high-level details and the target worksheet can have detail level information in relation to the source worksheet.
Example: Dashboard with Sales by Category, Sub-category. Assume there are two worksheets in the dashboard. One worksheet can have category level information and target worksheet can have a category, subcategory details. Upon hover of a category, we can highlight the sub-category level details in the target worksheet.
URL: The URL option helps to navigate from one published workbook to another published workbook. When report gets published in the Tableau Server it gets a unique URL. One Workbook can have high-level details and the other workbook can have low-level details.
Example: Workbook with Sales by Category. Another workbook with Sales by Category, Sub-category. Upon selection of a category in one workbook the other workbook URL opens.
86. How to view SQL which is generated by Tableau Desktop?
Tableau Desktop Log files are located in C: Users\My Documents\My Tableau Repository. If you have a live connection to the data source, check the log.txt and tabprotosrv.txt files. If you are using an extract, check the tdeserver.txt file. The tabprotosrv.txt file often shows detailed information about queries.
87. Explain Tableau Trend Analysis? Explain Tableau Forecasting?
Trend Analysis: It is about finding the behavior of historic activity based on the detailed historical information. The more detailed and accurate information we provide in Trend models the results will be that much accurate. The following are available models in Tableau for Trend Analysis.
- Linear • Logarithmic • Exponential • Polynomial
Forecasting: It is about predicting future trends based on the historic trend. Indirectly Forecast uses Trend Analysis. The more detailed and accurate historical information we provide in Forecast models the results will be that much accurate. The following are available models in Tableau for Forecast.
- Additive • Multiplicative
Tableau Server Interview Questions
88. Describe the Tableau Architecture?
Tableau Server has a highly scalable, n-tier client-server architecture that serves mobile clients, web clients, and desktop-installed software. Each of the components is explained below.
89. Data Layer:
One of the fundamental characteristics of Tableau is that it supports your choice of data architecture. Tableau does not require your data to be stored in any single system, proprietary or otherwise. Most organizations have a heterogeneous data environment: data warehouses live alongside databases, whether on-premise or in the cloud. Cubes and flat files like Excel are still very much in use. Tableau can work with all of these simultaneously.
90. Data Connectors:
Tableau includes over 40 optimized data connectors for data sources such as Microsoft Excel, SQL Server, Google BigQuery, Amazon Redshift, Oracle, SAP HANA, Salesforce.com, Teradata, Vertica, Cloudera, and Hadoop. There is also a generic ODBC connector for any systems without a native connector.
Tableau provides two modes for interacting with data: live connection or in-memory. Users can switch between a live and in-memory connection as they choose.
91. Live connection:
Tableau’s data connectors leverage your existing data infrastructure by sending dynamic SQL or MDX statements directly to the source database rather than importing all the data. This means that if you’ve invested in a fast, analytics-optimized database like Vertica, you can gain the benefits of that investment by connecting live to your data. This leaves the detail data in the source system and sends the aggregate results of queries to Tableau.
Tableau offers a fast, 64-bit ready, columnar in-memory Data Engine that is optimized for analytics. You can connect to your data and then, with one click, extract your data to bring it in-memory to perform queries in Tableau up to 100x faster. Tableau’s Data Engine fully utilizes your entire system to achieve fast query response on hundreds of millions of rows of data on commodity hardware.
Tableau Server Main Components:
The work of Tableau Server is handled with the following four server processes:
93. Application Server:
Application Server processes (wgserver.exe) handle content browsing, server administration and permissions for the Tableau Server web and mobile interfaces. When a user opens a view in a client device, that user starts a session (workgroup_session_id) on Tableau Server. The default timeout for this session is easily configuration by an administrator.
94. VizQL Server:
Once the user is authenticated via the application server, the user can open a view. The client sends a request to the VizQL process (vizqlserver.exe). The VizQL process then sends queries directly to the data source, returning a result set that is rendered as images and presented to the user. In many cases, Tableau Server leverages client-side rendering and caching to reduce the load on the server.
95. Data Server:
Unlike traditional approaches to metadata management, the Tableau Data Server is a key component that allows IT administrators, to enable monitoring, meta data management and control for IT, while enabling self-service analytics for business users. It lets you centrally manage and store Tableau data sources and provides end users with secure access to trusted data in a self-service analytics deployment.
The backgrounder refreshes scheduled extracts, delivers notifications and manages other background tasks. The backgrounder is designed to consume as much CPU as is available so as to finish the background activity as quickly as possible.
97. Gateway/ Load Balancer:
The Gateway routes requests to other components. Requests that come in from the client first hit an external load balancer, if one is configured, or the gateway and are routed to the appropriate process. In the absence of an external load balancer, if multiple processes are configured for any component, the Gateway will act as a load balancer and distribute the requests to the processes.
98. Clients: Web Browsers and Mobile Apps
Tableau Server provides interactive dashboards to users via zero-footprint HTML5 in a web or mobile browser, or natively via a mobile app.
99. Tableau Desktop
Tableau Desktop is the rapid-fire business analytics authoring environment used to create and publish views, reports and dashboards to Tableau Server. Using Tableau Desktop, a report author can connect to multiple data sources, explore relationships, create dashboards, modify metadata, and finally publish a completed workbook or data source to Tableau Server
Read more from this whitepaper.
100. What are the different data security levels?
Tableau provides several ways for you to control which users can see which data. For data sources that connect to live databases, you can also control whether users are prompted to provide database credentials when they click a published view. The following three options works together to achieve different results:
Database login account: When you create a data source that connects to a live database, you choose between authenticating to the database through Windows NT or through the database’s built-in security mechanism.
Authentication mode: When you publish a data source or a workbook with a live database connection, you can choose an Authentication mode. Which modes are available depends on what you choose above.
User filters: You can set filters in a workbook or data source that control which data a person sees in a published view, based on their Tableau Server login account.
Read more here
101. What is Authentication on Server?
Authorization refers to how and what users are allowed to do with content hosted on Tableau Server (including projects, sites, workbooks, data sources) after authentication has been verified.
These actions are determined by a combination of the user’s site role and permissions associated with content.
102. What are different site roles we can assign to a user?
Site roles are permission sets that are assigned to a user, such as System Administrator, Publisher, or Viewer. The site roles define collections of capabilities (delete, save, view, and others) that can be granted to users or groups on Tableau Server.
General capabilities allowed with each site role
- Server Administrator: The server administrator has full access to all servers and site functionality, all content on the server, and all users.
- Site Administrator: Site administrators can manage groups, projects, workbooks, and data sources for the site.
- Publisher: Publishers can sign in, interact with published views and publish dashboards to Tableau server from the desktop.
- Interactor: Interactors can sign in, browse the server, and interact with the published views, but are not allowed to publish to the server.
- Viewer: Viewers can sign in and see published views on the server but cannot use any interactive features like filtering and sorting.
- Unlicensed: Unlicensed users cannot sign in to the server. Users are assigned this role when licenses are not available.
103. What are permissions and how can we set them on various resources?
Permissions determine whether a given user is allowed or denied to perform a specific action on a specific resource.
There are six areas where permissions can be set:
Site: Sites are at the top of the content hierarchy and provide a way to partition your server into separate environments for users. Each site is administered separately and has its own Groups, Users, Projects, Workbooks and Data Sources.
Project: Projects exist within Sites, and are also used to separate content in the server and typically used to split content into logical groupings, for example around department or function etc.
Group: Groups are collections of Users within a site. They help to organize and manage sets of users for administration purposes.
User: Users are the individual named users accessing a Site. Every user added to Tableau Server must have an associated site role. The site role determines the levels of permissions allowed for a user, including whether a user can publish, interact with, or only view content published to the server.
Workbook: Workbooks use the default permissions from their project. Unless permissions are locked, the publisher of the dashboard has final control over who sees their data when it is published.
Data Source: Similar to Workbooks, Data Source permissions can be determined by either the Publisher if publishing to an unlocked project, or by the project permissions if publishing a data source to a locked project.
104. Why do you publish a data sources and workbooks?
Suppose you create a view that exposes a new range of questions in the data you’re using, and you want to share this analysis with other people using this data. Or maybe you are your team’s Data Steward, in charge of building the data models approved for use by analysts, and meeting your organization’s requirements for security, compliance, performance, and so on.
You can share your work with the rest of your team by publishing it to Tableau Server or Tableau Online. After it’s published, you and your team can access it through your web browser or the Tableau mobile app. Publishing data sources can also help you to centralize data management.
105. What are the Best Practices for Published Data Sources
Publishing data sources to Tableau Online or Tableau Server is integral to maintaining a single source for your data. Publishing also enables sharing data among colleagues; including those who don’t use Tableau Desktop but have permission to edit workbooks in the web editing environment.
Updates to a published data source flow to all connected workbooks, whether the workbooks themselves are published or not.
Read more here
106. What makes up a published data source
The data connection information that describes what data you want to bring in to Tableau for analysis. When you connect to the data in Tableau Desktop, you can create joins, including joins between tables from different data types. You can rename fields on the Data Source page to be more descriptive for the people who work with your published data source.
An extract, if you decide to create one. Guidelines for when to create an extract are included below, as well as in the additional resources.
Information about how to access or refresh the data. The connection also includes access information.
107. How can you schedule the Reports in tableau?
When you publish workbooks that connect to extracts you can schedule the extracts to be refreshed automatically. That way you don’t have to republish the workbook every time the underlying data is updated and you can still get the performance of a data extract.
This increases performance and avoids queries to the live database. Then you can add that workbook to a schedule so that the extract is refreshed at regular intervals with updated data from the data warehouse.
Schedules are created and managed on the server by an administrator. However, an administrator can allow you to add a workbook to a schedule when you are publishing from Tableau Desktop.
- As you are publishing a workbook, in the Publish Workbook to Tableau Server dialog box, click Scheduling & Authentication.
- In the Scheduling & Authentication dialog box, select a schedule for the workbook:
All data sources that require authentication must have an embedded password so that the extract can be refreshed. This includes data sources that are not extracts
108. What is Hyper?
Hyper is a high-performance in-memory data engine technology that helps customers analyze large or complex data sets faster, by efficiently evaluating analytical queries directly in the transactional database. A core Tableau platform technology, Hyper uses proprietary dynamic code generation and cutting-edge parallelism techniques to achieve fast performance for extract creation and query execution.
With Hyper, transactions and analytical queries are processed on the same column store, with no post-processing needed after data ingestion. This reduces stale data and minimizes the connection gap between specialized systems. Hyper’s unique approach allows a true combination of read-and write-heavy workloads in a single system. This means you can have fast extract creation without sacrificing fast query performance.
109. What are the performance benefits of Hyper?
Starting in 10.5, Hyper technology integrates with Tableau Data Engine to give you the following key benefits:
Faster extract creation: With Hyper technology, it generates extracts almost as fast as the source system can deliver data, no sorting needed.
Support for larger extracts: Prior to this release, you might not have been able to get all your data into a single extract. With the new Hyper technology, much larger amounts of data are included in a single extract.
Faster analysis of extracts: In many cases, you will see faster querying of data for larger extracts, or workbooks with complex calculations.
Hyper technology is memory-optimized. This means that when needed, all data lives in memory. This results in fast data access times.
Hyper is a compiling query engine. Queries are either interpreted or compiled to the machine code for maximum performance and allowing the Data Engine to get the most performance out of modern hardware (CPU, large main-memory capacities).
Read more here
110. What is VizQL?
At the heart of Tableau is a proprietary technology that makes interactive data visualization an integral part of understanding data. A traditional analysis tool forces you to analyze data in rows and columns, choose a subset of your data to present, organize that data into a table, then create a chart from that table.
VizQL skips those steps and creates a visual representation of your data right away, giving you visual feedback as you analyze. As a result, you get a much deeper understanding of your data and can work much faster than conventional methods–up to 100 times faster.
111. Where can we find Tableau Repository?
Tableau Repository will be found under the “Documents. It contains a number of folders. It will be automatically created while we install Tableau in our system. Those folders contain logs, where every hit of Tableau records logs in the notepad files and also it contains some of the information like map sources, Bookmarks, etc.
112. What is an increment refresh and full refresh?
Incremental refresh and Full refresh are the classifications of scheduling. Incremental refresh will refresh only the incremental records of data during refresh in scheduling whereas the full refresh will refresh the full data during refresh in scheduling.
Questions 113 to 210 details below
Note: This article has only 112 Tableau technical questions, there are total 210 questions in the pdf that covers HR, Culture fit, Behavioral, Thought process and Client communication. Feel free to download the handy pdf for you to reference when you want. Grab it below.
Showcase your Portfolio
Tableau/Data visualization is a visual subject. Leverage Tableau public to create and share your creative work with the world.
Please do so by participating challenges like MakeoverMonday, WorkoutWednesday, IronViz, Vizforsocialgood, Storytellingwithdata and involving in Tableau community. The more you put yourself out, collaborate with the community and practice, the better Tableau resource you would become.
Seek help when needed and don’t forget to help others along the way.
Regardless of our tremendous preparation and hard work we put in for Tableau interview, we could still fail. It’s just the fact. Sometimes it may not be anything to do with our skills or how we did in the interview but some other external factors which we can’t control might influence the decision.
Don’t worry too much about the outcome, put your best efforts and work hard.
If not this, there is always the next opportunity, this is not the end of the world. A job that you truly love awaits you. Until then inspire from rejected.us. You are not alone. Be positive.
If you find this content useful or helped in your Tableau interview, please drop me a message on Linkedin, I will be very happy and it motivates me to write more useful content for the Tableau community.
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Your well-wisher, Sateesh