Tableau Interview Questions
Tableau can be used with a variety of data sources. This includes Excel, CSV, Multi-dimensional cubes, MS –access, and also relational databases, cloud-based data sources.
Typical Tableau implementation follows these principles,
- Requirement gathering. Discussion with users to understand their data and visualization requirements.
- Analyze data sources. Gather information on different data sources and relationships among the data elements.
- Create mockups of the dashboards. User review of mockups.
- Create a data source.
- Structure the data by renaming data fields to user-friendly columns. As required, create calculated fields, hierarchies, parameters, and other elements.
- Depending on the number of data elements in the data source, organize data into different folders.
- worksheets and dashboards as per the requirement.
- Unit Testing. Comparing the data in the visualizations with the source data. Review of a dashboard by the users.
- If the server option is available and desired deploy dashboards over the Server.
- Create data load schedules.
- Requirement gathering. Discussion with users to understand their data and visualization requirements.
- Analyze data sources. Gather information on different data sources and relationships among the data elements.
- Create mockups of the dashboards. User review of mockups.
- Create a data source.
- Structure the data by renaming data fields to user-friendly columns. As required, create calculated fields, hierarchies, parameters, and other elements.
- Depending on the number of data elements in the data source, organize data into different folders.
- worksheets and dashboards as per the requirement.
- Unit Testing. Comparing the data in the visualizations with the source data. Review of a dashboard by the users.
- If the server option is available and desired deploy dashboards over the Server.
- Create data load schedules.
Dimension tables also referred to as Dimensions contain the descriptive attribute of the data element. Example of a Dimension table will be a Customer Dimension, containing fields that describe a customer, i.e. Customer Name, Address, and so on. Dimensions are used to slice the data. The primary key in a Dimension table also referred to as Dim ID uniquely identifies each row.
The fact table contains a measurable attribute of the data. For example, the Fact table can contain Sales Amount or the Sales made to a Customer. The fact table contains the foreign key of the Dimension Table.
Dimension provides context to a Fact. Without Dimension Fact will not be meaning full.
In the above example, Customer Info will be stored in the Customer Dimension, and Sales made to different customers will be stored in the Fact table. These two tables will be joined to get the Sales Amount of a Customer.
The fact table contains a measurable attribute of the data. For example, the Fact table can contain Sales Amount or the Sales made to a Customer. The fact table contains the foreign key of the Dimension Table.
Dimension provides context to a Fact. Without Dimension Fact will not be meaning full.
In the above example, Customer Info will be stored in the Customer Dimension, and Sales made to different customers will be stored in the Fact table. These two tables will be joined to get the Sales Amount of a Customer.
1) Cardinality between Dimension and Fact is one too many, with many on the Fact side. Look for One to Many relationships in the ER diagram.
2) The dimension contains the textual/ descriptive attribute and Fact contains the measurable data. Look for such data elements in the tables.
3) The fact table contains the foreign key of the dimension table.
2) The dimension contains the textual/ descriptive attribute and Fact contains the measurable data. Look for such data elements in the tables.
3) The fact table contains the foreign key of the dimension table.
Star schema is a data structure in which a Fact table is at the center surrounded by dimension tables. The structure looks like a Star and hence the name. Using below Star schema, you can slice Sales Amount and Quantity by Customer, Time period, Product, and Regions.
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