What Is a Universal Semantic Layer? Implentation Steps, Popular Tools & Use Cases Explained

Big data is now crucial for driving business decisions. Companies are tapping into it to gain valuable insights and make smarter moves. To unlock this power, they’re using tools like data warehouses, BI tools, and cloud storage. One key innovation? The semantic layer: Its role is simple—standardize data definitions, making them more accessible and easier for business users to understand.

Let’s dive into everything about the universal semantic layer—what it is, the different types, and how to implement it.

What Is a Universal Semantic Layer?

A semantic layer bridges the gap between the technical structure of source data and end-user platforms. Instead of raw data columns like ac_tcin_id_23, the team renames it to something more understandable, like ac_item_id_23, making it easier for business users to understand. 

Databases often store data in complex structures that aren’t easy for end users to grasp. That’s where the semantic layer steps in. It creates a unified view of data using business terms, making sure everyone in the organization is on the same page.

It also ensures consistent terminology across the organization. For instance, “customers” for the sales team might be “clients” for the finance team. The semantic layer standardizes the vocabulary, ensuring everyone is aligned.

There are different types of semantic layers, and a universal semantic layer is one common type. It’s an independent abstraction that offers a consistent view of data, acting as a single source of truth for business logic and data definitions.

You can explore how you can work and unlock insights using the Snowflake semantic layer or the Tableau semantic layer.

Evolution of a Semantic Layer

The concept of the semantic layer isn’t new—it’s been around for over three decades, and here’s how it has evolved.

In the ’80s and ’90s, organizations started adopting data warehouses like Oracle and IBM DB2 to centralize data from various sources. Therefore, complex SQL querying was required to extract insights, which caused business users to rely completely on IT teams for reports. 

In the early 2000s, tools like BusinessObjects and Microstrategy emerged with semantic layers that mapped complex data definitions to business terms. This reduced the need for deep knowledge of SQL. These tools were later replaced by modern platforms like Tableau, Power BI, and Looker, which have built-in semantic layers, making self-service analytics accessible to everyone.

However, these semantic layers were tool-specific, leading to inconsistent data definitions across multiple BI platforms. Moreover, as data grew so rapidly, the need for separate systems for storage and computation also increased. Therefore, cloud data warehouses were adopted for storage purposes, and to efficiently query and compute cloud data, independent semantic layers like dbt’s semantic layers were introduced. These provide an abstraction layer on top of cloud storage, offering business users a consistent view of data.

Universal Semantic Layer vs Traditional Semantic Layer 

	Universal Semantic Layer	Traditional Semantic Layer
Definition	An independent abstraction layer that provides a consistent view of data across multiple platforms	A tool-specific layer that provides a consistent view of data but specific to a single BI tool.
Flexibility	Seamlessly integrates with new data platforms	Requires complex customizations to integrate into a new tool
Querying	You can directly query cloud data warehouses.	Relies on OLAP cubes to run queries.
Scalability	Efficiently scales with cloud data.	Cannot handle big data.

Why Is a Universal Semantic Layer Important?

Flexibility

Different teams across an organization use different tools, but with a tool-specific semantic layer, it becomes tough for end users to pull data from one tool and use it in another. Teams end up dealing with the complexity of structure themselves while integrating data across various tools. It standardizes the data view, ensuring that every tool uses the same structure. This means you don’t need to learn a new tool to make sense of your data, making the entire process smoother and more flexible.

Consistency

Different data domains call an entity with different names. Moreover, the definition of a metric might vary across domains. That’s why a universal semantic layer ensures consistency in data definitions across the organization. It ensures everyone in the company follows the same data definition, removing inconsistencies and miscommunications between teams.

Cost-Efficiency

Without a standardized semantic layer, the same data can be present in duplicate places in multiple formats. This increases the storage and computation costs for companies. It removes data duplicacy, reducing the storage costs. 

Types of Semantic Layers

1. BI Semantic layer: These are tool-specific semantic layers that come built-in with the business intelligence tools. Or your team can build a semantic layer for a specific BI tool. These layers work best for the data products built on these tools. However, adapting different tools and data products with this approach is challenging. 

2. Universal Semantic Layer: This is an independent layer of abstraction that transforms raw data into business terms. By “independent,” it means the abstraction layer is designed to be integrated with any business tool, standardizing the format across various tools used within the organization.

3. Data Warehouse Semantic Layer: The data warehouse semantic layer resides within the warehouse itself. It transforms the raw data stored in the warehouse into business-friendly terminology, allowing data engineers to organize and manage the data effectively.

4. Data Pipeline Semantic Layer: Like the data warehouse semantic layer, the data pipeline semantic layer is used within a pipeline to organize its data processes. It helps standardize the meaning and relationship between data elements within the pipeline. 

How to Implement a Universal Semantic Layer? 

Dbt is one of the most popular modern frameworks for creating semantic layers. Let’s walk through how you can implement one using dbt.

• Step 1: Install and set up dbt in your environment, then connect it to your data warehouse so you can start interacting with your data.

• Step 2: Create semantic models. These models act as the abstraction layer, defining business logic, relationships, and hierarchies. For example, you can build an SQL query to calculate revenue and profits, store it as a standard model, and easily reuse it across your analysis.

SELECT 
    cust_id, 
    SUM(sales_units) AS tota_sales, 
    COUNT(DISTINCT item_id) AS total_items 
FROM {{ ref('sales_table') }} 
GROUP BY cust_id;

• Step 3: Define metrics in the YML file. For example, assign the model from the previous step to a standard term. Whenever a user calls that term, the allocated model is executed.

version: 2

metrics:
  - name: total_sales
    label: "sum of sales units"
    model: ref('sales_table')
    calculation_method: sum
    expression: sales_units
    timestamp: sales_date
    time_grains: [day, week, month]

  - name: total_items
    label: "count of unique items"
    model: ref('sales_table')
    calculation_method: count_distinct
    expression: item_id
    timestamp: sales_date
    time_grains: [day, week, month]

• Step 4: Use the dbt server API to integrate the semantic layer with BI and analytics tools. These tools interact with the standard semantic layer, ensuring consistent terminology and structure across the organization.

Challenges and Considerations in Implementing a Universal Semantic Layer

• Initial Setup: Integrating an independent semantic layer into your existing tools and processes can be complex. While it’s a one-time effort, it requires subject matter experts and a solid adoption strategy.
• Scalability: If the semantic layer is designed only with current data sources and structure in mind, it may struggle to handle growing data volumes. To avoid this, the semantic layer should be built with performance optimization techniques such as caching and indexing.
• Resources: Developing an independent universal semantic layer requires technical experts. So, companies should invest in a dedicated analytics engineer to build and maintain the semantic layer.
• Security: Since the semantic layer provides a centralized view of your datasets, it’s mandatory to implement security measures like role-based access controls, audit trails, and logging, especially if you have sensitive information. This will help you track potential vulnerabilities.

Popular Tools and Technologies for Universal Semantic Layers 

1. Data Integration Platforms: ETL tools like Hevo Data pull data from multiple systems and transform it into a unified and standardized format, which can then be accessed through a semantic layer.
2. Data Warehouses: The data pulled by ELT from various sources is stored in a single data warehouse to provide a consistent view through semantic layers. Popular examples include Snowflake, BigQuery, and Redshift.
3. BI Tools: Business intelligence tools, such as Tableau, Power BI, and Looker, are the end-user platforms that rely on semantic layers for a unified and consistent data view. They consume this data, making it easier to generate reports for business users.
4. Data Virtualization Tools: Rather than moving data from multiple sources into a central storage location, data virtualization tools provide a virtual layer that provides a combined view of the semantic layer.

Use Cases of a Universal Semantic Layer

Many industries like manufacturing, telecommunications, eCommerce, and healthcare are benefiting from analyzing data to optimize their operations.

1. eCommerce

Over the past 20 years, eCommerce has been the most evolved and adopted industry. With Amazon leading the charge by providing the fastest delivery through Prime services, other startups are pushing the bar from same-day to hourly delivery. And what’s driving all of this? Data.

eCommerce businesses are constantly gathering, storing, and analyzing data from various verticals—delivery times, distances, customer satisfaction—and using that info to optimize their logistics, resulting in happier customers and smoother operations. Throughout this data collection to the analysis phase, the semantic layer is involved.

2. Healthcare

Healthcare is the next big industry, utilizing data to predict diseases and viruses. A semantic layer in business tools has made it easier for non-technical users to leverage data in operations such as patient care, research and development, and hospital management.

Conclusion 

Understanding the universal semantic layer is crucial to standardize data definitions across teams. It eliminates the dependency on any specific tool or service that your organization relies on for data.

By learning these fundamentals, you are on your way to choosing the right data tools and storage architectures for your data analytics and reporting use cases.

Try a 14-day free trial and experience the feature-rich Hevo suite firsthand. Also, check out our unbeatable pricing to choose the best plan for your organization.

FAQs