Overview

What is semantic layer?

While much of data work focuses on building robust pipelines and transformation models, the true value of data is only recognized when it influences business decisions. As most stakeholders who want to derive insights don't often work with SQL or complex data tools, there's a crucial demand for an interface to enable end users to flexibly query data without tinkering with the technicalities.

Semantic layer is created to maps real-world entities and their metrics to a logical data structure. It translates database concepts into familiar business terms like product, customer, and revenue, creating a unified view of data across your organization.

Recurve's Semantic Layers provide an abstraction layer that simplifies data access and enhances query performance. By organizing data into meaningful structures such as Cubes, Views, and Relationships, Recurve enables users to build efficient, reusable, and scalable data models.

Let's look at where this fits in your workflow. After ingesting and transforming your data, you need to make it meaningful for business use. This meaning comes from metrics that help end users make important decisions. A store owner may ask:

• "What are the monthly revenue this year?"

• "Which menu items have the highest profit margins?"

Recurve's Semantic Layers makes these answers accessible without having to write custom SQL. By expressing requests through measures and dimensions, users can retrieve consistent, accurate insights.

Recurve Semantic Layers are available exclusively in the Production environment. This means that when creating cubes or views, they will reference the production database. Please ensure the data connection for the Production environment is properly configured.

Concept

Semantic Layers in Recurve are designed to bridge the gap between raw data storage and business intelligence by structuring data in an intuitive manner. Key components include:

Cube

A Cube is a structured data model that enables multidimensional analysis. It consists of:

• Measures: Aggregable numerical values (e.g., revenue, sales count).

• Dimensions: Categorical values used for grouping and filtering (e.g., product category, region, date).

• Primary and Foreign Keys: Used to establish relationships with other cubes or data models.

• Reusability: Each cube can be used as an input to create views.

• Integration with BI Tools: Cubes provide structured data that feeds into business intelligence tools for reporting and analysis.

View

A View is a logical representation of data derived from one or multiple cubes. It serves as a simplified and optimized data source, enabling users to:

• Pre-define complex aggregations and calculations.

• Improve query performance by materializing specific data structures.

• Customize datasets for different analytical use cases.

Relationships

Recurve allows users to define Relationships between different cubes and views to establish data connectivity. These relationships enable:

• Seamless data integration across multiple datasets.

• Enhanced query efficiency by leveraging predefined joins.

• A structured approach to data governance and security.

Benefit of using Semantic Layers

• Performance Optimization: Reduces query complexity and speeds up data retrieval.

• Reusability & Maintainability: Encourages consistent data modeling and reuse.

• Scalability: Supports growing datasets and evolving analytical requirements.

• Improved Data Governance: Ensures data integrity and security by structuring access to datasets.