In the realm of Business Intelligence, the focus has shifted from guesswork to data-driven decision-making. This entails identifying challenges, detecting market trends, and optimizing business operations. At the forefront of BI platforms is Google Looker — a robust tool that empowers organizations to explore, aggregate, model, visualize, and present data in a way that simplifies comprehension.

As projects grow in complexity, Looker offers an intuitive interface composed of multiple views, explores, and models. Some views are shared across explores, while others remain independent. To manage projects efficiently and maximize the potential of embedded analytics, adopting a structured approach is essential. Here, we outline a series of steps to achieve a well-organized model set:

Step 1: Establishing the Base Folder

Begin by creating a foundational folder housing common views applicable to multiple explores. Within this base folder, include shared dimensions and measures.

Step 2: Defining Common Elements

Define common dimensions, measures, and calculations within the base folder views. These elements can be accessed across various explores where the views are employed.

Step 3: Crafting the Extended Folder

Expand your organizational structure with an extended folder, containing subfolders dedicated to specific explores such as Orders and Events. For instance, the Orders subfolder comprises essential base views for the orders explore. This approach accommodates the addition of new views, both interconnected and independent of the orders explore.

Step 4: Customizing Extended Views

Within the extended view, define dimensions, measures, and calculations tailored exclusively for the orders explore. If modifications are needed for certain dimensions or measures, articulate these changes specifically for the orders explore. The newly defined elements will be visible exclusively within the context of the orders explore.

Step 5: Model File Implementation

In the model file, integrate the extended views such as Orders_ext, users_ext, inventory_ext, product_ext, new_users, and existing_users to construct the Orders explore.

Conclusion:

Ultimately, the objective of BI platforms is to present customized data that aligns with the expectations of the intended audience. This approach ensures accessibility for developers who comprehend the model structure and its construction, enabling them to leverage Looker’s power effectively.

Author Name: Ratna Deep Burman

Curious and would like to hear more about this article ?

Contact us at [email protected] or Book time with me to organize a 100%-free, no-obligation call

Follow us on LinkedIn for more interesting updates!!

Data platr Inc. specializes in data engineering & analytics with pre-built data models for Enterprise Applications like SAP, Oracle EBS, Workday, Salesforce to empower businesses to unlock the full potential of their data. Our pre-built enterprise data engineering models are designed to expedite the development of data pipelines, data transformation, and integration, saving you time and resources.

Our team of experienced data engineers, scientists and analysts utilize cutting-edge data infrastructure into valuable insights and helps enterprise clients optimize their Sales, Marketing, Operations, Financials, Supply chain, Human capital and Customer experiences.