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Tableau is a Business Intelligence tool for visually analyzing the data. Users can create and distribute an interactive and shareable dashboard, which depict the trends, variations, and density of the data in the form of graphs and charts. Tableau can connect to files, relational and Big Data sources to acquire and process data. The software allows data blending and real-time collaboration, which makes it very unique. It is used by businesses, academic researchers, and many government organizations for visual data analysis. It is also positioned as a leader Business Intelligence and Analytics Platform in Gartner Magic Quadrant.
Introduction to Data Visualization
Nowadays, most BI users use Data Visualization Tools with their products to handle their raw data to create useful and informative data. So, in brief, Data Visualization Tools are the software tools through which the raw and unstructured data can be used as informative data to increase the Business insights and sales.
Need For Smart Data Visualization Due to the popularity of Big Data in the IT Industry, many organizations are looking for a better data management by using the best Data Visualization tools. To handle a huge amount of data and analytics, it is required for the need of smart Data Visualization tools. Tools like Tableau, Qlikview, Dygraphs, ZingChart, and FusionCharts have a better understanding in handling Big Datas. These tools can be very expensive, but because of its handling capacity, connectivity and business trends, they are very popular in the market.
A Tableau data extract is a compressed snapshot of data stored on disk . Extracts are saved subsets of data that you can use to improve performance.. When you create an extract of your data, you can reduce the total amount of data by using filters and configuring other limits. After you create an extract, you can refresh it with data from the original data. When refreshing the data, you have the option to either do a full refresh, which replaces all of the contents in the extract, or you can do an incremental refresh, which only adds rows that are new since the previous refresh.
The benefit of Tableau extract over live connection is that extract can be used anywhere without any connection and you can build your own visualization without connecting to database
Speed. In my experience with Tableau Desktop, an extract is generally much faster, especially with large data sets. An extract will do the "heavy lifting" of large, complex joins and save them in a way they can be accessed in a flash. This is useful if you've made ODBC connections that require a password (you won't have to enter it except when refreshing). If your data is relatively small and simple, a live connection may be just as fast, but live connections to big data can be quite slow.
Speaking of passwords... Utility. Suppose your users don't have permissions to the ODBC sources you do. Using an extract will still allow them to view the snapshot of data without needing passwords.
Static Nature. An extract is a "snapshot" of the data, rather than a live view. You need to refresh the extract to get the newest snapshot. With a live connection, each time you open the viz you should see the latest data.
I'll throw in one more consideration:
Size. Because using an extract creates a second data type (a .tde), a single viz-- when paired with its .tde companion-- will take up more space. I find this a small trade-off for the processing efficiency I get in creating an extract
The tree map displays data in nested rectangles. The dimensions define the structure of the tree map and measures define the size or color of the individual rectangle. The rectangles are easy to visualize as both the size and shade of the color of the rectangle reflect the value of the measure.
A Tree Map is created using one or more dimension with one or two measures.
A dual-axis graph displays two different axes, on opposite sides of the graph. This can be the left and right vertical axes or the top and bottom horizontal axes. To create a dual-axis graph, you create an area, bar, or line graph and add a metric to display on the graph’s second parallel axis.
Tableau offers five main products: Tableau Desktop, Tableau Server, Tableau Online, Tableau reader and Tableau Public.
It is a self service business analytics and data visualization that anyone can use. It translates pictures of data into optimized queries. With tableau desktop, you can directly connect to data from your data warehouse for live upto date data analysis. You can also perform queries without writing a single line of code. Import all your data into Tableau’s data engine from multiple sources & integrate altogether by combining multiple views in a interactive dashboard.(ii)Tableau Server:
It is more of an enterprise level Tableau software. You can publish dashboards with Tableau Desktop and share them throughout the organization with web-based Tableau server. It leverages fast databases through live connections.(iii)Tableau Online:
This is a hosted version of Tableau server which helps makes business intelligence faster and easier than before. You can publish Tableau dashboards with Tableau Desktop and share them with colleagues.(iv)Tableau Reader:
It’s a free desktop application that enables you to open and view visualizations that are built in Tableau Desktop. You can filter, drill down data but you cannot edit or perform any kind of interactions.(v)Tableau Public:
This is a free Tableau software which you can use to make visualizations with but you need to save your workbook or worksheets in the Tableau Server which can be viewed by anyone.
I worked on tableau 9.3
Differences between Tableau 8.3 and Tableau 9.3, 9.2, 9.1 and 9.0Analytics :
Key addition in Tableau 9.x. Analytics tab adjacent to Data helps to directly implement the following features:
Tableau 8.3 calculated fields box allows to access dimensions and measures directly in the same screen and provides access to create a parameter without leaving the formula box.Performance Improvements in Tableau 9.x:
In Tableau 10, we’ve improved both the beauty and brains of Tableau. We’ve added dozens and dozens of new features to make your analysis faster, easier, and even more delightful. Here are ten features that are specifically designed to empower your entire enterprise.1. Revision history
Roll back to an older version of a workbook with just one click in Tableau Server. And restore older versions of your data sources by downloading and republishing them. You can also limit the number of revisions on the Tableau Server site settings page.2. Licensing views
Tableau Server includes new administrative views that give insight into licensing and usage of Tableau Desktop. Once configured, Tableau Desktop sends usage information to Tableau Server in the background every eight hours, server login not required.3. Subscribe others
Easily share vizzes with your team. Subscribe others to your dashboard with a click. The subscription email will include your name, so those users will know who to thank.4. Mobile device management
We’ve added support for VMware Airwatch and MobileIron using a new industry approach called AppConfig. This means you can securely deploy Tableau Mobile across your organization.5. Site SAML
The Tableau REST API has been expanded with more metadata information options, user result filtering, and the ability to return your Tableau Server version.8. Document API
We've added a new Document API which provides a supported path for working with Tableau files such as .twb and .tds. This means you can create a template workbook in Tableau and easily deploy that across multiple servers and/or databases. 9. Web Data Connector 2.0
Build more flexible and powerful connectors with the Web Data Connector 2.0. This version is easier to use and also supports multiple tables and joins.10. ETL refresh
With this feature, you can leverage our Web Data Connectors when working with Alteryx and Lavastorm. If you’re using these products for data prep or ETL, then you can change the data- source parameters right from Tableau Desktop.
Tableau desktop is used for developing visualizations in the form of Sheets, Dashboards and Stories. Other useful functionalities of Tableau desktop are:
Data transformation, creating data sources, creating extracts and publishing visualization on the Tableau Server. Tableau desktop produces files with extensions twb and twbx.
It is a licensed product but comes with 2 weeks of trial.Tableau Public:
It is a free application provided by Tableau to develop visualizations. In functionality, it is similar to Tableau desktop but files are published on Tableau Public and are accessible to everyone.
Join is a query that combines the data form 2 or more tables by making use of Join condition.
Normally in the Tableau we can perform the analysis on the single data server. If we want to perform the analysis from the multiple data sources in a single sheet then we have to make use of a new concept called as data blending.
Data blending mix the data from the different data sources and allow the users to perform the analysis in a single sheet. Blending means mixing. If we are mixing the data sources then it is called as data blending1) The difference between joining and blending data:
Joining your data can only be done when the data comes from the same source, for example from two sheet tabs within a single Excel file. If that same information was stored in separate Excel files you would need to do a data blend in Tableau. A blend is always required if the data is stored in two separate "data sources" within Tableau. So even if your data is very closely related and exists in two separate files or databases, you will have to do a data blend if you are combining the data in Tableau.
When blending data, the first data source used in your view will dictate how your worksheet view in Tableau is built. The secondary (blended) data source will be able to contribute extra information, but will not be able to change the overall structure of the view. The secondary data source's values can be aggregated and applied to the existing view after you have established a "relationship" by assigning a variable that both the primary and secondary data sources have in common.2) When to use data blending:
It is generally preferable to avoid data blending when you can combine the two data sources outside of Tableau. If this is not an option, then you must identify at least one common variable shared by the two data sources you want to blend together. When possible, go for a join rather than a blend. If you need to combine two data sources and for whatever reason cannot manage to join the data outside of Tableau, your only option is a data blend.
A simple example is having (a) a data source with three columns including location names and latitude/longitude values, and (b) a data source with location names and detailed information about each location. You could build a map using (a) and then blend in extra supplemental information using (b), where a relationship is built by connecting the data sources based on the location names.3) When to use joining:
You can only use joining when your data comes form the same underlying source (for example, the same Excel file or Access file).4) When are you unable to blend data from two or more sources?
If there are no variables shared between each data source then you will not be able to do a data blend, because there is no information that can be related from one source to the other. However, this does not mean that the column headers (variable names) need to be an exact match. You can edit the relationships manually to point Tableau to the variables that have matching underlying values.
For example, if I am blending information together based on countries and source (a) calls it "Country" while source (b) calls it "Locations", I can edit a relationship manually to blend on these two variables. If the two column headers are an exact match, Tableau may automatically establish the link for you.4) When are you unable to join data?
If the data comes from different underlying files you will not be able to do a join within Tableau. I recommend preparing your data before importing it into Tableau (there are many great tools available, one being Alteryx, that can help with this). In my opinion blending and joining in Tableau should be a last resort for times when you are unable to shape your data into one coherent file for analysis.
The difference between CSV and XLS file formats is that CSV format is a plain text format in which values are separated by commas (Comma Separated Values), while XLS file format is an Excel Sheets binary file format which holds information about all the worksheets in a file, including both content and formatting
CSV files are file formats that contain plain text values separated by commas.
CSV files can be opened by any spreadsheet program: Microsoft Excel, Open Office, Google Sheets, etc. You can open a CSV file in a simple text editor as well. It is a very widespread and popular file format for storing and reading data because it is simple and it’s compatible with most platforms. But this simplicity has some disadvantages. CSV is only capable of storing a single sheet in a file, without any formatting and formulas.
XLS files are Microsoft Excel’s workbook files in use between 97-2003. Later Excel versions use the XLSX extension. XLS and XLSX file formats contain all the information from the worksheets in a workbook, including formatting, charts, images, formulas, etc.
Filter is nothing but it is restricted to unnecessary, it is showing exact data. Basically filters are 3 types.1. Quick filter 2. Context filter 3. Datasource filter
Tableau filters change the content of the data that may enter a Tableau workbook, dashboard, or view. Tableau has multiple filter types and each type is created with different purposes. It is important to understand who can change them and the order of each type of filter is executed. The following filters are numbered based on the order of execution.
A. Secure Filters: Filters that can be locked down to prevent unauthorized data access in all interfaces (i.e., Tableau Desktop, Web Edit mode, or standard dashboard mode in a web browser).
1. Data source filters: To be “secure” they must be defined on a data source when it is published. If they are defined in the workbook with live connection, Tableau Desktop users can still edit them. Think of these as a “global” filter that applies to all data that comes out of the data source. There is no way to bypass a data source filter.
2. Extract filters: These filters are only effective at the time the extract is generated. They will not automatically change the dashboard contents until the extract is regenerated/refreshed.
B. Accessible Filters: Can be changed by anyone that opens the dashboard in Tableau Desktop or in Web Edit mode, but not in regular dashboard mode in a web browser.
3. Context filters: You can think of a context filter as being an independent filter. Any other filters that you set are defined as dependent filters because they process only the data that passes through the context filter. Context filters are often used to improve performance. However if the context filter won’t reduce the number of records by 10% or more, it may actually slow the dashboard down.
4. Dimension filters: Filters on dimensions, you can think of as SQL WHERE clause.
5. Measure filters: Filters on measures, you can think of as SQL HAVING clause.
C. User Filters: Can be changed by anyone in Tableau Desktop, in Web Edit mode, or in regular dashboard mode in a web browser.
6. Quick filters: Commonly used end user filters.
7. Dependent quick filters: There are quick filters depends on another quick filter. Dependent quick filterscan quickly multiply and slow down dashboard performance.
9. Table calculation filters: Filters on the calculated fields.
By clicking on drop down icon present on quick filter pane we get many options to change the filter show options
Ex. In data source I have column like empID, EmpName, EmpDept,EmpDsignation, EmpSalary
In reports I am using empname on columns and empsalry on rows. I can use empDesignation on Filters
A twbx is a "zipped" archive containing a twb + any external files associated with that workbook, such as extracts and background images.
A .twbx file is a Tableau Packaged Workbook, meaning it is the original .twb file grouped together with the datasource(s) in one package. .twbx files can be considered analogous to specialized zip files, in which these “zip” files contain all the information necessary to work in Tableau. 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.
This is mainly used when more than two mesaures are used in multi lines graphs or charts. Also, when there is a need to show two measures on the same axis, then Blended Axis is the option to explore. We need to drag drop the scond, third and other mesaures in to the first axis of the measure to blend the multiple measures in to one. In this case the single blended axis with multiple measures contains the range of values from the source to satisfy all the measures those are blended in to one axis. The name of the axis changes in to 'Value' which generic in nature.
If I want to use the sheet in dashboard or story then I can hide the sheet. If I will not use the sheet anymore then I can delete the sheet.
By using page shelf you can make animations.
Sometimes it is useful to look at numerical data in an aggregated form such as a summation or an average. The mathematical functions that produce aggregated data are called aggregation functions. Aggregation functions perform a calculation on a set of values and return a single value. For example, a measure that contains the values 1, 2, 3, 3, 4 aggregated as a sum returns a single value: 13. Or if you have 3,000 sales transactions from 50 products in your data source, you might want to view the sum of sales for each product, so that you can decide which products have the highest revenue.
You can use Tableau to set an aggregation only for measures in relational data sources. Multidimensional data sources contain aggregated data only.
Table Calculations (including the Quick Table Calculations) live in our Tableau View. They are created in the view and stay there, locally in our worksheet.
Calculated Fields are created on a data level and appear as a separate column in the data source. Tableau doesn’t change the source, but can create an extract where the calculations will be visible.
Data Blending isn't really a join, but a blend. The closest analogy is a LEFT Join, but the results are aggregated (on the join fields you define) and then Joined on those fields. by way of example if I had the following 2 tables
After connecting with the data, before going to the sheets. If changes are needed in data that time we use the data source filters.
If there are null values in the data to filter the null values or any other values. That time we can use the Data source filters.
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