GigaOm Sonar Report for Semantic Layers and Metrics Storesv1.0

An Exploration of Cutting-Edge Solutions and Technologies

Table of Contents

  1. Summary
  2. Overview
  3. Considerations for Adoption
  4. GigaOm Sonar
  5. Vendor Insights
  6. Near-Term Roadmap
  7. Analyst’s Take
  8. Report Methodology
  9. About Andrew Brust

1. Summary

In today’s world, data proliferates, and the individual teams within organizations responsible for analyzing that data usually possess both autonomy and their own preferred tools. This situation can lead to inconsistent definitions of the metrics used in data analysis from tool to tool and from team to team. Semantic layers and metrics stores offer a solution to these pain points, enabling consistent definitions of metrics to be created and used organization-wide.

A semantic layer creates a consolidated representation of an organization’s data, one that makes data understandable in common business terms. A metrics store is a subcomponent of a semantic layer, and it functions primarily as a repository for the definitions of metrics used by an organization in its analytics and reporting. Semantic layers and metrics stores are beneficial for helping business users understand and access data in terms that are familiar to them and useful for workloads that require a higher-level, consolidated perspective on the data or that require data to be understood through common business terms.

Additionally, because semantic layers are essentially abstraction layers over the physical data, they make organizations more flexible and resilient when it comes to change. Even if the data underneath the semantic layer changes, the higher-level concepts and terms this data is associated with in the semantic layer change less frequently, shielding business users from disruption to the way they query their data.

So, what is a semantic layer and how does it work? As Figure 1 shows, a semantic layer functions as a translation layer between the physical data in analytics repositories (including data warehouses, lakes, lakehouses, and other systems) and client applications, such as business intelligence (BI) tools used to query, analyze, visualize, and present the data. Data is unified from across an organization’s disparate data sources, and the semantic layer creates a consolidated conceptual view of the organization’s data estate.

Figure 1. Semantic Layer Diagram

Measures (such as sales, number of page views, or dwell time at a cell tower), dimensions (for example, product category, geographic location, or business segment), and the relationships among these are all defined in the semantic layer. This model is a logical view of the data over the underlying physical storage, which consolidates the data logically, abstracts away its physical location, and effectively translates it into terms and concepts relevant to the business.

The result is what some vendors in this category refer to as a “single source of truth” across an organization. When different teams across the organization build dashboards and reports with their preferred applications and tools, they all work with the same definition of any given business term or concept, because this logic is all stored within the semantic layer. Metrics are defined once and used across the board, eliminating confusion, duplication of work, and inconsistency.

This GigaOm Sonar provides an overview of semantic layer and metrics store vendors and their available offerings, equipping IT decision-makers with the information they need to select the best solution for their business and use case requirements.

About the GigaOm Sonar Report

This GigaOm report focuses on emerging technologies and market segments. It helps organizations of all sizes to understand a new technology, its strengths and its weaknesses, and how it can fit into the overall IT strategy. The report is organized into five sections:

Overview: An overview of the technology, its major benefits, and possible use cases, as well as an exploration of product implementations already available in the market.

Considerations for Adoption: An analysis of the potential risks and benefits of introducing products based on this technology in an enterprise IT scenario. We look at table stakes and key differentiating features, as well as considerations for how to integrate the new product into the existing environment.

GigaOm Sonar Chart: A graphical representation of the market and its most important players, focused on their value proposition and their roadmap for the future.

Vendor Insights: A breakdown of each vendor’s offering in the sector, scored across key characteristics for enterprise adoption.

Near-Term Roadmap: A 12- to 18-month forecast of the future development of the technology, its ecosystem, and major players of this market segment.

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