Buyer’s Guide
The Buyer’s Guide to the Best Semantic Layer Tools for Data and Analytics
As a savvy data and analytics leader, you may be looking to use a semantic layer to help your organization make smarter data-driven decisions at scale. But it can be hard to know what to choose and how to implement it well.
The reality is, companies must define a semantic layer, no matter what. If you don’t have your data and analytics colleagues do it, all your end users will do it for themselves in Tableau, Qlik, Excel or whichever front end tool they are using.
Many data and analytics professionals write and maintain complex ETL pipelines to generate numerous extracts that are fed to BI tools. However, this is not only a maintenance headache, but adds unnecessary work for governance professionals.
The good news is the semantic layer has come a long way.
Business Intelligence Platforms
Traditional BI platforms that bundle data modeling, query management and visualization
Example Vendors
Tableu, Power BI, IBM Cognos, SAP Business Objects, Looker
pros
No extra technology layer needed
Tight integration
Business user friendly
Cons
Semantic layer specific to BI tool only (not reusable)
Vendor lock in
Data Virtualization Platforms
Platforms that abstract away the physical source and location of data in a tabular format
Example Vendors
Denodo, Dremio
pros
Provides flexibility in how/where data is stored
Semantic layer can be used across a variety of tools
Cons
Not friendly for business users (tables, columns)
Data models need to be built before accessing data
Query performance is not guaranteed and/or needs manual tunning
Data Warehouse / Data Marts
A database of information from a variety of data sources
Example Vendors
Snowflake, Amazon Redshift, Google BigQuery, Azure Synapse SQL Analytics
pros
Single source of truth
Widest array of tool/query access
Easy to secure
Cons
Not friendly for business users (tables, columns)
Slow to integrate new data sources
Dependence on IT
Semantic Layer Solution
A platform that presents a business data view that helps users access data autonomously using common business terms
Example Vendors
AtScale, SQL Server Analysis Services
pros
Business user friendly
Single source of truth
Provides flexibility in how/where data is stored
Semantic layer can be used across variety of tools
Easy to secure
Cons
Extra technology layer required
Data models need to be built before accessing data
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“Data and analytics leaders must adopt a semantic layer approach to their enterprise data assets or face losing the battle for competitive advantage.”
Guido De Simoni
Senior Director, Analyst at Gartner
This guide looks at several technical approaches to implementing a semantic layer for your data and analytics stack. Included is an implementation checklist, technology scorecard, and chart of pros and cons with several example scenarios.
As a data and analytics leader, either on the business or tech side, reading this guide will help you adopt a semantic layer approach for your data assets. This guide explains where a semantic layer fits into modernizing your data and analytics infrastructure. It will help you:
- Drive consistency
- Reduce compute costs
- And improve ease of use for a wide variety of consumption types and use cases
Spoiler Alert!
Enterprises that implement a semantic layer powered by data virtualization get instant access to their structured and unstructured data from relational data warehouses, data lakes, and enterprise SaaS applications to create logical data warehouses, accessible with SQL, MDX, DAX, Python and REST. This provides access to data from a broader set of distributed sources and storage formats – without requiring users to know where the data resides or how it’s formatted.
The Top Signs You Need a Semantic Layer
Watch out for multiple analytics tools, complaints about data access, and inconsistent reports – along with other indicators.
- Business units or groups have strong preferences for different analytics tools
- Business analysts and/or data scientists complain about a lack of data access
- The slow pace of data integration drives the business to build their own Solutions
- Reports from different BI tools use similar terms but show different results
- Business executives express doubts about their confidence in the numbers
- And improve ease of use for a wide variety of consumption types and use cases
Key Considerations When Implementing a Semantic Layer
To start, you’ll want to cover all of your use cases, leverage data virtualization, and future-proof your technology choices.
- Business units or groups have strong preferences for different analytics tools
Your semantic layer must work across a variety of BI and ML consumers. It should be decoupled from a single consumption style. - Offers Tabular and Multidimensional Views
Your semantic layer must offer both tabular and multidimensional views to cover the widest range of use cases. - Supports Data Platform Virtualization
Your semantic layer must leverage data virtualization capabilities to abstract away data platform differences and minimize platform lock-in. - Easy Model Development and Sharing
Your semantic layer should provide a multi-user design environment and markup language to promote re-use and enforce standardization. - Ability to Express Business Concepts and Functions
Your semantic layer must support business constructs and core analytics requirements around time intelligence and hierarchical rollups. - Query Performance & Caching
Your semantic layer should include a comprehensive performance management system that goes beyond simple caching techniques. - Support for Business Intelligence and Data Science Workloads
Your semantic layer needs to support a variety of workloads including business intelligence and data science. - Security & Governance
Your semantic layer should integrate with your single sign-on (SSO) standards and support column-level security, row-level security and impersonation.