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

Business Semantic Layers

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.

  1. Business units or groups have strong preferences for different analytics tools
  2. Business analysts and/or data scientists complain about a lack of data access
  3. The slow pace of data integration drives the business to build their own Solutions
  4. Reports from different BI tools use similar terms but show different results
  5. Business executives express doubts about their confidence in the numbers
  6. 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.

  1. 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.
  2. Offers Tabular and Multidimensional Views
    Your semantic layer must offer both tabular and multidimensional views to cover the widest range of use cases.
  3. Supports Data Platform Virtualization
    Your semantic layer must leverage data virtualization capabilities to abstract away data platform differences and minimize platform lock-in.
  4. 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.
  5. Ability to Express Business Concepts and Functions
    Your semantic layer must support business constructs and core analytics requirements around time intelligence and hierarchical rollups.
  6. Query Performance & Caching
    Your semantic layer should include a comprehensive performance management system that goes beyond simple caching techniques.
  7. Support for Business Intelligence and Data Science Workloads
    Your semantic layer needs to support a variety of workloads including business intelligence and data science.
  8. Security & Governance
    Your semantic layer should integrate with your single sign-on (SSO) standards and support column-level security, row-level security and impersonation.