Tableau is a visual analytics platform transforming the way we use data to solve problems empowering people and organizations to make the most of their data.
Tableau helps us Explore and analyze data in seconds. It allows click, drag, and drop data elements to integrate your data. It facilitates the creation of beautiful and interactive dashboards.
More than 60000 companies choose Tableau as web platform to analyze the data in real-time building solutions to organizational challenges. Such companies are Netflix, Lenovo, and Linkedin.
Brief history about Tableau.
Tableau was founded in 2003 as a result of a computer science project at Stanford that aimed to improve the flow of analysis and make data more accessible to people through visualizations.
Co-founders of Tableau are Chris Stolte, Pat Hanrahan, and Christian Chabot who developed and patented Tableau's foundational technology.
VizQL—which visually expresses data by translating drag-and-drop actions into data queries through an intuitive interface.
Why Use Tableau.
It is flexible because you can easily work with a lot of different data sources.
It is quite intuitive: visual cues and icons make the interface easier to navigate.
The drag-and-drop functionality makes prototyping very fast: you can build dashboards in hours or days instead of weeks.
Presents insights all in one tool.
Tableau was acquired by Salesforce in 2019 and the mission remains the same;
- to help people see and understand their data thus developing solutions to help anyone working with data get answers faster, and uncover unanticipated insights.
Tableau is your partner in Data culture,Data culture -A Data Culture is the collective behaviors and beliefs of people who value, practice, and encourage the use of data to improve decision-making.
**Tableau story **is a connected series of worksheets and dashboards that allow you to capture insights and share them in a sequential presentation. A story consists of one or more story points, these story points are worksheets or dashboards that highlight specific insights.
Tableau Desktop Delivers everything you need to access, visualize, and analyze your data, with an intuitive drag-and-drop interface, you can uncover the hidden insights you need to make impactful business decisions faster, even when you are offline.
Tableau Desktop is a kind of laboratory in which you can discover the meaning that lies hidden in your data.
How to make your data connection
Tableau can work with file-based data including Excel spreadsheets; CSV files; PDFs; spatial files such as Shapefiles and GeoJSON Files; and statistical files such as SAS, SPSS, and R data files.
Tableau can connect to database servers-Server-based data sources, including relational databases, cube database sources and cloud data.
Tableau can connect to saved data sources automatically installed as well as those that are user-created.
The canvas is where your visualizations will appear. The page shelf lets you break a visualization into several pages for example one page for each neighborhood. The filter shelf lets you filter your data.
The Tableau Platform - The Tableau platform is made up of three foundational products that work together to create a cohesive data flow from raw information to clear insights:
- Tableau Prep - clean up dirty data with ease and speed. Automate data cleansing processes with Tableau prep, part of Tableau Data Management.
- Tableau Desktop- Interactive dashboards help you uncover hidden insights on the fly.
- Tableau Cloud/Server - Like Tableau Online, it allows users to host and store data visualizations created with Tableau. The difference from Tableau Online is that it’s not fully hosted by Tableau, but rather on public cloud platforms like AWS, Google Cloud Platform, and Microsoft Azure.
Let's Discuss about Joins and Relationships.
When it comes to joining data, Tableau offers two distinct methods: Relationships and Joins. Both approaches serve the same purpose; however, they differ in handling data connections.
Joins.
The same concept of joins is also used in SQL.
Joins aim to combine different tables from the same source based on some logical column relationships between those tables. For instance, trying to combine an Excel file and a SQL table will break because they do not come from the same source.
Creating a join.
To create a join, connect to the relevant data source or sources. These can be in the same data source(such as tables in a database or sheets in an Excel, spreadsheet) or different data sources(this is known as a cross-database join)
Drag the first table to the canvas.
Select Open from the menu or double-click the first table to open the join canvas (physical layer)
Double-click or drag another table to join canvas.-If your next table is from another data source entirely, in the left pane, under Connections, click the Add button ( in web authoring) to add a new connection to the Tableau data source. With that connection selected, drag the desired table to the join canvas.
Click the join icon to configure the join.Add one or more join clauses by selecting a field from one of the available tables used in the data source, Choosing a join operator and a field from the added table.
When finished, close the join dialog and join canvas.
Anatomy of a join.
Joins Types.
There are four types of joins that you can use in Tableau: Inner, Left, right, and full outer. If you aren't sure what join type you want to use to combine data from multiple tables, you should use relationships.
A Join is performed by setting up one or more join clauses. The join clause tells Tableau which fields
Joins are a more static way to combine data. Joins must be defined between physical tables up front, before analysis, and can't be changed without impacting all sheets using that data source. Joined tables are always merged into a single table.
Relationships.
Relationships are a dynamic, flexible way to combine data from multiple tables for analysis. It narrates how tables relate to each other based on common fields but don't merge the tables.
Think of relationships as a contact between two tables.
Using relationships is more easier and intuitive.
In Tableau a dataset is used to build visualizations and when it connects to data fields are assigned to two roles: Dimensions and Measures.
About data field roles and types
Data fields are made from the columns in your data source. Each field is automatically assigned a data type such as integer, string, or date, and a role: a discrete dimension or continuous measure (or less commonly, a continuous dimension or discrete measure).
In Tableau quantitative fields are referred to as Measures, and qualitative fields are referred to as Dimensions.
Qualitative fields(Dimensions)-Describes or categorizes data, tells you what, when or who, and slices the quantitative data for instance names,dates or georgraphical data. You can use dimensions to categorize,segment and reveal the details in your data.
Dimensions are fields that are qualitative,categorical data types,such as Colors,Names and States.
*fields above the gray lines
Quantitative fiels(measures) - Provides the measurement for qualitative category,can be used in calculations and represents numerical data.
These are values you can measure,like Height,Weight and sales.Measures can have infinite value and can be aggregated(summed or averaged)
Dimensions and measures are the building blocks of tableau charts.
Blue versus green fields.
Tableau represents data differently in the view depending on whether the field is discrete or continuous). Continuous and discrete are mathematical terms.
Continuous means "forming an unbroken whole without interruption" These fields are colored green treated as an infinite range. When a continuous field is put on the rows or columns shelf, an axis is created in the view.
Discrete means "individually separate and distinct." These fields are colored blue. When a discrete field is put on the Rows or Columns shelf, a header is created in the view.
Blue Text Field- A blue icon indicates that the field is discrete, which means it is data that contains separate parts.
Invalid Field red exclamation point (at the edge of a textbox) indicates a problem - it can indicate a field is missing,or in this case that the calculated field is broken.
If you are building charts that use date fields, it is important to understand how Tableau processes dates , dates can be discrete or continuous.
Fields that are italics are Tableau-generated fields.
The views refer to the space where you can drag fields and display data
Data pane - lists all the fields from the source field.
Data source- The name of the data source connection for example the orders sheet in sample-superstore is listed near the data pane tab.
Dimensions- usually positioned at the top. They are usually coded blue and are fields that contain qualitative or categorical data for example City and Product Name, dimensions slice our data into categories to show different details.
Measures -They are color-coded green and contain quantifiable data that consists of numerical values for example Discount and Quantity. They are quantitative values that you can well, measure and aggregate. They are positioned under the dimensions.
Segmenting with dimensions.
Segmenting means grouping similar data. Dimensions and measures affect visualizations differently. Dimensions are used to segment data while measures can be aggregated and add quantitative values to dimensions.
Below is an image to show segmenting.
There are various ways to segment data in Tableau:
Basic Segmentation - Use filters: Apply filters to your data to include or exclude specific values based on conditions.
Create sets: Define sets to group related data points based on a condition, and then use them in your analysis.Grouping and Binning:
Group data: Combine similar values into custom groups to simplify analysis. For example, grouping states into regions.
Top N / Bottom N:
Use Top N or Bottom N filters to focus on the top or bottom data points based on a chosen measure. This is useful for identifying the highest or lowest values.Dashboard Actions:
Implement dashboard actions to enable interactivity between different visualizations. For example, clicking on a specific data point in one chart could filter related data in another.
Filters allow you to include and exclude specific data.
Mark card is used to set different properties for fields in visualization. As you drag fields to different properties on the Marks card you add more information to the view.
U use a marks card to set the mark type and to encode your data in the view with color, size, shape, text, and other properties that add context, detail, and meaning to the marks in the view.
Top comments (0)