Difference between scattere plot vs Tree map?

TreeMaps: Treemaps we will display data in nested rectangles.We  use dimensions to define the structure of the treemap, and measures to define the size or color of the individual rectangles.Treemaps are a relatively simple data visualization that can provide insight in a visually attractive format. Treemaps cannot accommodate more than two measures—one to control size, the other to control color. Treemaps can accommodate any number of dimensions, but you can only use one dimension to diversify the view, by dragging it to Color.Other dimensions can be used only to multiply the number of rectangles in the view. 

Dimensions cannot be displayed in a hierarchy. We can not add the Trend Line in Tree Maps

Scatter Plot: Scatter plots provide an easy way to visualize relationships between numerical variables. In Tableau, you create a scatter plot by placing at least one measure on the Columns shelf and at least one measure on the Rows shelf. We can add the trend lines in Scatter Plot and we can show dimensions in hierarchy.

Max no.of records in a Table?

No limit rows/column . but 
In a database table  'n' number of rows and 255 columns. 

What is default Data Blending Join?

Getting the data from multiple sources is called Data Blending. To blend the data you should have relationship between two Data sources.

By default Data blending is inner join

What is Pageshelf?

The Pages shelf lets you break a view into a series of pages so you can better analyze how a specific field affects the rest of the data in a view. 


Ex: Drag&Drop Year column in page shelf, you will see drop down with list of year val if you select a value then it will take you to page contains selected year value.

OR

Page shelf is type of shelf which can break the data in to points & can represent the data in an animated format. In Page shelf you can even predict or forecast based up on the existing data.

what is Assume referential integraty?

In Database terms every row in fact table will contain a combination row in dimension table. Using this method only we build Primary and Forieng Keys for joining 2 tables.

The same option is even available in table under File Menu --> Data --> Assume Referential Intergrity.

If you unable this option for a .tds then tableau intelligently queries the tables and reduces the query retrieval time.


By selecting Assume Referential Integrity, you tell Tableau that the joined tables have referential integrity.
 
In other words, you are confirming that the fact table will always have a matching row in the Dimension table. 

Because that is true, Tableau doesn't need any information from the Dimension table in order to return these results. 

When you drag any fact column (Ex:AmountSales) field into the view, Tableau can simplify the query just with Fact Column.

How to display state borders for a country by geo coding?

Go to maps->Maps option we will find options like Base, coastline, country region borders etc.. based on that we can set border.

Difference between Groups and Sets?

Groups: You can combine several dimension members into a single member using groups. you can't use in groups in calculated fields.

example1: if you are working with a field that contains thousands of products, you can use Groups to combine those products into higher level categories. Grouping data is also really useful for cleaning up data inconsitencies. 

example2: your data may have California listed as CA, California, and Calif. When you try to see all sales in California your numbers will be split across these three rows. You can quickly group these three variants together to treat them as a single value.

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 specific data point in your view. You can use sets to compare and ask questions about a subset of data. 

For example, in a scatter plot showing product sales, you may select the products with the most sales and add them to a set. You may then create another set that contains the products with the highest return rates. With these two sets you can ask questions like "What percent of my total sales is from high returned products?" Or you may go further and compare the two sets to each other to create a new set that contains only high sales products that are also returned frequently.