Quick Data Modeling

Applies to: Kyvos Enterprise Kyvos Cloud (SaaS on AWS) Kyvos AWS Marketplace

Kyvos Azure Marketplace Kyvos GCP Marketplace Kyvos Single Node Installation (Kyvos SNI)

Workflow-based data modeling

As a self-serve tool, Kyvos allows you to define your business case without the need for any coding expertise. To further enhance the user experience, the Quick Data Modeling feature is introduced. It allows you to create data connections quickly, identify your data (available in tables and SQL), and design an semantic model without deep learning of the Kyvos interface. It enables you to process a smart skeleton of your business use case with minimal interaction and bootstrapping work.

Typically, to start your analysis using Kyvos, you need to perform the following five steps.

Connect to data
Create datasets
Define relationships
Design a semantic model
Create visualization/connect to BI tool

With the Quick Data Modeling feature, you can connect to the data source and select the data to register; Kyvos automatically validates your registered data and creates relationships and semantic model design, thereby eliminating the effort and time needed for designing the semantic model.

Let us consider a manufacturing use case for analyzing publicly available SSB data.

On the Kyvos home page, click Quick Data Modeling from the top-right of the home page.
On the displayed wizard, provide an Analysis Name. Kyvos will create all the folders, relationships, and semantic model with this name. In this example, we have named it Kyvos.
Click Start.
On the displayed Create Dataset tab, select your data Connection from the Datasource column.
To modify an existing connection according to your environment, select it from the list and click the Edit link.

Note

For AWS-based deployments, if your data is available in an S3 bucket other than the one in which Kyvos is installed, select the CustomerDataLake option and click the Edit button to configure a connection to your S3 bucket.

Enter the name of your S3 bucket. You can also use a comma-separated list to add multiple buckets here.
Click Save.

You can also modify the default Hadoop connection or click a new connection using the Create New Connection link to define a new Snowflake or Redshift data warehouse connection. Refer to the Working with data connections section to know more.
Select the Input Type from File, Table, or SQL, and proceed as explained below:

Tips

Mark at least one fact table. This will help in the auto-creation of relationships and semantic model. You should mark that file as Fact, which you want to use as a fact table in relationships.
Mark the primary key and foreign key. This will help in the auto-creation of relationships and semantic model in the next stage. Dimensions/measures will not be created for primary/foreign keys.
Hide columns that you do not need in your analysis.
Update format if needed.

Choose the Preview Table icon to see sample data, and click Filter data to refine the data that you want to bring in for your analysis.
Click the Next button. The system validates all your files and tables and displays errors if your data is invalid.
The Define Relationships tab page is displayed with a set of relationships automatically created by the system.
These relationships are created on the basis of data selected in the previous step and information about master data, fact data, primary key, and foreign key.
If the system does not generate any relationships or to define your custom relationships, you can manually define relationships. For this, click the Add Relationship link.

Tips

Create a relationship from fact to dimension by keeping fact on the left side of the relationship.
Each table should participate in at least one relationship. In case the table does not have any relations, it will not be used in auto-semantic model creation in the next stage.
It is recommended to mark the node that you want to use as fact as a fact table using right-click options.

Click Apply.
The system validates relationships for correctness, and if everything is valid, and generates a semantic model design. The validation status is displayed at the bottom of the screen.
The Design semantic model tab displays the dimensions and measures created by the system.
By default, the name of the datasource table from which the first measure is created in the measure group is taken as the measure group name. However, if you have created a measure group with a custom name, then dragging a measure in it would not impact the name of the measure group.

You can further modify the design to add dimensions, measures, define dimensions and measure properties, and so on. Refer to Working with Semantic Models to know more.
Click the Next button.
The Review and Process tab shows the entities designed till now.
Here, you can choose any of the following options:
I want to execute the test process now: Select this option to run the test job and click Process Now.
You can further choose:
1. Input Data: Here, you can select the number of records or partitions to be processed in the test job.
2. Minimal Aggregation: Select the checkbox to minimize aggregations. Aggregates are precalculated summaries that improve query response time. However, semantic model size increases with aggregation.
3. I want to execute the job later: Select this option if you want to review your design and entities before proceeding with the job. In this case, your design is saved, and you can access the semantic model from Toolbox at any time.
Once you have launched the test job, click View Job Progress to see the job status.
The system shows the job status in the semantic model designer, as shown in the following figure.
At the successful completion of your test job, you can start creating visualization and then generate recommendations to further optimize your design.