Tableau 10 version new features

Tableau 10 version new features

Tableau got a makeover!  I’m loving the slick new look and feel.  From a design perspective, Tableau’s keeping up with times and is following the trend of a clean and crisp interface.  The difference between blue and green pills (or active fields, if we’re being fancy) is more obvious than ever.

The World Of Clean Visualizations

The user interface wasn’t the only thing with an enhancement.  When working with client views, we try to instill the “Less is More” idea.  A best practice for visualizations is to remove non-data ink – any extra pixels that aren’t helping to tell your story.  Tableau’s new “Smooth” default visualizations eliminate most of the tedious steps required in cleaning up a visualization.  This will save SO much time in the sometimes-difficult-to-conquer formatting menu.  You can see in the time series below that the vertical grid lines are removed and the axes are faded.  This allows the reference points to fade away and focus only on the seasonal pattern of our Sales.

Connect To More Data

Want more data?  Tableau continues to give us new connectors with Google Sheets and Kognitio.  Direct connections to the answers you need – faster than ever!

Filter Across Data Sets

“Analytics at the speed of thought!” was a key phrase I remember hearing in the Tableausphere, but there are still a few times I run across a feature and think, “hmm, this could be done in less clicks”.

Imagine a scenario where you have two separate data sources with Zip Code level detail.  As you analyze this data, you’d like to drill down into a specific zip code.  However, we’ve been taught that a quick filter only applies to its original data source.  Bummer.  Historically, this meant we’d have to create a dashboard action or parameter to filter across both sources at once.  Too many clicks.

NOT ANY MORE!

As I built out my two maps, I could choose to use my quick filter for “All related data sources”.  I was so excited to use this new feature and BOOM!

It didn’t work. :(

After a bit of investigation I realized that the field I was using for my quick filter was not named exactly the same in each data set (Zip vs. Zip Code).  Similar to the hiccup we experience in Data Blending, I figured it was worth a shot to try creating a custom relationship between the two fields.  A few steps after Data-> Edit Relationships and BAM!

It works!

Filtering across data sets will eliminate many unnecessary clicks/workarounds/hacks that were required to look at data together.  As Tableau is so forgiving with its unlimited undo button, we should never be afraid that adding new data to enhance the story will cause HOURS of extra work.  Cross data source filters make this more of a reality than ever.  

Click To Create New Areas

Speaking of maps…many times to represent the total amount in a geographic region, we relied on custom polygons that may have required outside tools or extra data prep.

NOT ANY MORE!

As I travel around the country people commonly ask WHERE in Louisiana I’m from.  Geographically, it’s pretty central but culturally the area is known as Acadiana (or land of the Cajuns).  I set out to find an estimated population for everyone living in Acadiana in 2014.  In my original map I was able to find the population per Parish, but what if I wanted to see the total population for a group of Parishes?

Tableau has given us the ability to create quick, custom areas.  Simply make geographic groups in your view, then remove the original geographic field from the Marks card.

TA-DA!  We turned 22 parishes into Acadiana !  Now go there and eat some tasty food – seriously!

Cross Database Joins

Bye-Bye Data Blending?

This is the feature I was most excited about at the TC15 conference in Vegas!  With the opportunity to join data from different sources, Tableau’s helping us to find answers quicker.  Primary v. Secondary sources?  Forget about it.  

It’s now easy vizzing to combine Sales values from two separate files.

So why the maybe?  Well…it doesn’t seem super fast and it doesn’t join sources which are “extract-only”. Yet. But remember, this is just the FIRST beta.  We’re excited to see the speed of this feature increase with new releases and it will prove to be a highly valuable addition to the tool.

How to create Donut chart in Tableau.?

Here's how I would create a Donut chart in Tableau using a single worksheet. The final product looks like this:
Step 1: Create a two-slice pie chart. This example uses actual sales and sales left to hit the goal.

Step 2: Drag the Number of Records measure to the Rows shelf and change the aggregation to an average. Repeat this a second time and then right-click on the second pill and choose "Dual Axis". Finally, remember to synchronize the axes.

Step 3: Double click on the left axis to bring up the Edit Axis window. Uncheck Include Zero.

Step 4: Do a bit of formatting: (1) Remove Zero line, (2) Remove Row & Column dividers, (3) Hide the headers

Step 5: On the secondary axis, remove Measure Names from color and Measure Values from Angle. Click the Color shelf and choose white. Adjust the size of the pie down slightly to reveal the donut.

Step 6: Add Region to the Columns shelf and then hide the headers.

Step 7: On the first pie chart on the Marks card, add Region to the Label shelf, set the alignment to the top and make the font bigger.

Step 8: On the second pie chart on the Marks card, add a % to goal measure to the Label shelf and customize the font.

Step 9: Sort Region by the % to goal metric in descending order.

Tableau Interview Questions

1. What are the Security Levels?
2. What is the Data modeling?
3. How to display to 5 and last 5 in same view?
4. What are the different  types of connecting data?
5. What is mean by rightback?
6. What is the difference between discrete and continuous?
7. What is the crasstab?
8. What is mean by interactive dashboard?
9. What are the limitations of tableau public?
10. How to reduce complexity of calculated fields?
11. What to do if we do not have latitude and longitude of a location?
12. What type of data connections you used?

Tableau interview questions

1.  What is the difference between filter and parameter?
2.  What are the different types of Actions?
3.  What is architecture of tableau?
4.  What are the different security levels?
5.  What is data modelling?
6.  How to display lable of a country in map when it selected?
7.  There are is map and quick filters if we select one country in map it should get selected in quick filter. how to do same?
8.  How to display top 5 and last 5 in same view?
9.  Can we use parameters in filters?
10. What kind of reports you prepare?
11. What are diffrent types of connecting data?
12. How to refresh data extracts?
13. what is rightback?
14. If we connect live will it refresh automatically or do we need to refresh manually?
15. What is difference between discrete and continuous?
16. What is crasstab?
17. What do you mean by interactive dashboards?
18. What is dashboard life cycle?
19. What are the limitations of tableau public?
20. What is difference between tableau public and desktop?
21. What are different products of tableau?
22. what is the use of tableau reader?
23. How to reduce complexity of calculated field?
24. What kind of functions you used?
25. What to do if we don't have latitude and longitude of a location?
26. What type of data connections you used?

Difference between tableau extensions .twb and .twbx

Twbx:

1. .twbx file is a Tableau Packaged Workbook, meaning it is the original .twb file grouped together with the datasource(s) in one package. 
2.  .twbx files can be considered analogous to specialized zip files, in which these “zip” files contain all the information necessary to work in Tableau.   
3. 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 this packaged file.

Twb:

1. The .twb file alone is not enough to perform any analysis because it only contains Tableau’s instructions for interacting with a datasource.  
2. In actuality, .twb files are XML files specially tailored to interact with datasources.
3. They are custom built to make the awesome visualizations that Tableau generates.  Here you can see a picture of a .twb file opened in Notepad++.

What is the Dimensions and Measures?

Dimension: A dimension is a field that can be considered an independent variable.

Dimensions typically produce headers when added to the rows or columns shelves in the view. By default, Tableau treats any field containing qualitative, categorical information as a dimension. This includes, for instance, any field with text or dates values.

This means that a measure can be aggregated for each value of the dimension. For instance, you might calculate the Sum of “Sales” for every “State”. In this case the State field is acting as a dimension because you want to aggregate sales for each state. The values of Sales are dependent on the State, so State is an independent field and Sales is a dependent field.

Measure:A measure is a field that is a dependent variable; that is, its value is a function of one or more dimensions.

Measures typically produce axes when added to the rows or columns shelves. By default, Tableau treats any field containing numeric (quantitative) information as a measure.

This means that a measure is a function of other dimensions placed on the worksheet. For instance, you might calculate the Sum of “Sales” for every “State”. In this case, the Sales field is acting as a measure because you want to aggregate the field for each state. But measures could also result in a non-numeric result. For instance, you might create a calculated measure called “Sales Rating” that results in the word “Good” if sales are good and “Bad” otherwise. In this case the “Sales Rating” field acts as a measure even though it produces a non-numeric result. It is considered a measure because it is a function of the dimensions in the view.

What is KPI in Tableau.?

We can easily create a view that shows Key Progress Indicators (KPIs). To do this, you complete the following tasks:

• Create the base view with the fields you want to measure.
• Build a calculated field that establishes the figure from which you measure progress for the data you’re measuring.
• Use shapes that Tableau provides that are designed specifically for KPIs.

This example shows how to build a KPI view that shows a green check mark for any sales figure over $125,000, and a red X for any sales figure under $125,000.

Preparing data for Tableau.

1. Cleanup dimensions and measure names.
2. Set attribute aliases.
3. Set default colors
4. Set default measure aggregations.
5. Create calculated fields.

Is Parameter have it's dropdown list..?

Yes, But it will be called as Compact list.

What is the criteria to blend the data from multiple data sources.?

There should be a common dimension to blend the data source into single worksheet.

For example, when blending Acutal and Target sales data, the two data sources may have a Date field in common. The Date field must be used on the sheet. Then when you switch to the secondary data source in the Data window, Tableau automatically links fields that have the same name. If they don’t have the same name, you can define a custom relationship that creates the correct mapping between fields.