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 are the major differences between tableau version 7.0
and tableau version 8.0?
- New visualizations are
introduced like treemap, bubble chart and box and whisker plot
- We can copy worksheet
directly from one workbook to another workbook
- Introduced R script
What is page self?
eg: If a dimension (country) is placed on the page shelf- each country will have one page
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.
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 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.
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.
Can we use Groups and Sets in calculation field.?
- Groups: No, we can not use Groups in calculation fields.
- Sets: Yes, we can use Sets in calculation fields.
Difference
between Grouping and Sets.?
· Groups – Combine dimension
members 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.
Can we have multiple value selection in
parameter?
No
What is Dual Axis.?
You can compare multiple
measures using dual axes, which are two independent axes that are layered on
top of each other. Dual axes are useful when you have two measures that have
different scales.
For example, the view below shows Dow Jones and NASDAQ close values over time.
To add the measure as dual axis drag the field to the right side of the view and drop it when you see a black dashed line. You can also select Dual Axis on the field menu for the measure.
For example, the view below shows Dow Jones and NASDAQ close values over time.
To add the measure as dual axis drag the field to the right side of the view and drop it when you see a black dashed line. You can also select Dual Axis on the field menu for the measure.
The
two axes are independent scales but the marks are layered in the same pane.
Can
we use Parameter in Filter.?
Yes, We
can use.
How
many types of filters are there in Tableau.?
In Tableau, there are three types of filters. More
explicitly, there are three different ways to limit the data that is displayed
by your graph. Each of these has its own strengths and weaknesses, and we will
look at them one at a time.
These types are
These types are
1.
Custom SQL "Filters"
2.
Context Filters
3.
Traditional Filters.
Custom
SQL Filters:
Custom SQL "Filter" is a WHERE clause that is placed
in the SQL that queries the data to be used in the workbook.
"Filter" is a Tableau term that technically applies only to
Context and Traditional Filters; however, the Custom SQL "Filter"
emulates the behavior of a global Context Filter, so we will refer to it as
such. By construction, Custom SQL "Filters" are always global.
The most common reason for using a Custom SQL "Filter" is to
limit the size of a data extract. The smaller your data extract, the more
quickly your charts will load. In other words, you can make more complex
charts without sacrificing efficiency.
One of the ways to create a Custom SQL "Filter" is
during the Server Connection process.
Context Filters:
a Context Filter is a filter in Tableau that affects the data
that is transferred to each individual worksheet. Context Filters are
great when you want to limit the data seen by the worksheet. When a
worksheet queries the data source, it creates a temporary, flat table that is
uses to compute the chart. This temporary table includes all values that
are not filtered out by either the Custom SQL or the Context Filter. Just
like with Custom SQL "Filters", your goal is to make this temporary
table as small as possible.
Context Filters have a few advantages over Traditional Filters.
First, they execute more quickly than Traditional Filters. They are
also executed before Traditional Filters and can be executed all at once, which
further improves efficiency. However, they do have one drawback. It
takes time for the filter to be placed into context. A rule of thumb,
from Tableau's KnowledgeBase, is to only place a filter into context if it
reduces the data by at least 10%.
A Context Filter is created by dragging a field onto the
"Filters" Shelf and editing the filter. Then, you can Right-Click
the field on the shelf and select "Add to Context." If you have
multiple context filters, you can CTRL-Select them all and add them to context
in a batch. This will improve the efficiency of your filter.
Traditional Filters:
Traditional Filter is exactly what most people think of when
they think of filters. When Tableau is creating the visualization, it
will check to see if a value is filtered out by a Traditional Filter.
Since this is not performed at the table level, it is the slowest of all
filter types. However, it does have the advantage of being performed
after the Context Filters. This is a necessity if you are dealing with
complex "Top N" filters. A Traditional Filter can be created by
simply dragging a field onto the "Filters" Shelf.
What
is data blending..? When do you use this.?
Data blending is when you blend data from multiple data sources
on a single worksheet. The data is joined on common dimensions. Data Blending
does not create row level joins and is not a way to add new dimensions or rows
to your data.
We use this when we want to fetch data from different sources and make use in single worksheet.
We use this when we want to fetch data from different sources and make use in single worksheet.
When do you use horizontal and vertical
components?
We can use these when we want to have all sheets or filter to
move in single shot.. however we can still create the dashboard without
this also.. this allows us to make our work simple
Name the components of dashboard?
·
Horizontal
·
Vertical
·
Text
·
Images etc
How Does Tableau Work?
While Tableau lets you analyze databases and spreadsheets
like never before, you don’t need to know anything about databases to use
Tableau. In fact, Tableau is designed to allow business people
with no technical training to analyze their data efficiently.
Tableau is based on three simple concepts:
1.
Connect - Connect Tableau to any database that
you want to analyze. Note that Tableau does not import the
data. Instead it queries to the database directly.
2.
Analyze - Analyzing data means viewing it, filtering it, sorting
it, performing calculations on it, reorganizing it, summarizing it, and so on.
Using Tableau you can do all of these things by simply arranging
fields of your data source on a Tableau worksheet. When you drop a field on a
worksheet, Tableau queries the data using standard drivers
and query languages (like SQL and MDX) and presents a visual
analysis of the data.
3.
Share - You can share results with others
either by sharing workbooks with other Tableau users, by pasting results into applications
such as Microsoft Office, printing to PDF or by using Tableau Server to publish
or embed your views across your organization.
What
is Tableau Desktop?
- Tableau Desktop is a data visualization application that lets you analyze virtually any type of structured data and produce highly interactive, beautiful graphs, dashboards, and reports in just minutes. After a quick installation, you can connect to virtually any data source from spreadsheets to data warehouses and display information in multiple graphic perspectives. Designed to be easy to use, you’ll be working faster than ever before.
