Schema

Schema Creation

A Schema serves as a fundamental blueprint or model for organizing and structuring data within a database.

This architectural framework delineates the data structure, establishes relationships between metadata elements, and outlines constraints or rules for storage and retrieval.

The subsequent step after creating a dataset is constructing a Schema. This process involves combining data from different sources through joining tables. Once a join schema is established, users can seamlessly integrate data from various tables into a unified data model, facilitating comprehensive analysis and insight discovery.

The schema design is a foundation for database design and is crucial for ensuring data consistency, integrity, and organization.

Following the dataset upload, the subsequent step involves creating a schema to facilitate the creation of a dashboard.

Steps for creating a Schema:

After uploading the dataset, the next crucial step is establishing a data schema, facilitating the seamless creation of a dashboard. The schema creation process is initiated after connecting the data through connectors.

Step 1: Log in to your account, then select the “Data” tab from the left menu.
Step 2: Click on “Create New Schema.”
Step 3: Select the desired dataset from the list of available datasets.
Step 4: If your data includes multiple sheets, select the “Join Tables” option and click “Proceed.” If there’s only one sheet, the schema will be created automatically without any joins. We’ll continue with the “Join Tables” option for now.

Creating Joins Between Different Datasets:

Creating joints between different datasets involves combining or merging data from multiple sources or datasets based on common attributes or fields. In databases or data analysis, a join is a way to bring together related information from different tables or datasets, allowing for a more comprehensive and interconnected view of the data.

For example, suppose you have one dataset containing information about customers and another dataset containing information about their purchases. You might join these datasets using a common identifier, such as a customer ID. This allows you to combine the information and more integratedly analyze customer behavior, preferences, and purchase history.

Step 5: In the Join Schema, the user must select the table and column of both sheets (the chosen column in both sheets should be identical).

Step 6: Various types of joins are available; choose the appropriate one based on your requirements. For the demonstration, we are choosing an inner join.

Types of Joins

• Inner Join: Select common records from Table A and Table B where the specified join condition is satisfied.
• Left Join: Retrieves all records from Table A and only those from Table B for which the join condition is met (if any).
• Right Join: Retrieves all records from Table B and only those from Table A for which the join condition is met (if any).
• Full Join: Retrieves common records from Table A and Table B, along with the remaining non-join records from Table A and Table B.

Step 7: Upon successfully creating joins, a “Joins are valid” message will appear. Proceed by clicking “Save.”

Step 8: Provide the schema name and click “Save.”

Step 9: After saving the schema, the Schema Manager window will open.

Now you can create a dashboard by clicking on Create dashboard.