Hard work pays off

When you’re at a growing company like Cube, everything is moving so fast that you don’t often take stock, slow down, and appreciate how far you’ve come. But this week has been a moment to do just that as we were honored to be recognized as a Leader in GigaOm’s Sonar for Semantic Layers and Metrics Stores. And - as to be expected - a Fast-Mover too. We’ve been launching new features left and right - in the last year support for YAML, the SQL API, the Data Graph, the Orchestration API, Semantic Layer Sync, and Integrations with Langchain and other AI tools (Delphi!) - so are excited to be recognized for all the hard work.

But why does it matter? Understanding the Significance of Semantic Layers

Semantic layers and metrics stores play a pivotal role in the world of data analysis. They act as bridges between raw data and actionable insights, enabling organizations to harness the full potential of their data. A semantic layer, in particular, consolidates complex data into a format that is understandable across different teams and tools, effectively translating raw data into common business terms.

Why Cube Stands Out

GigaOm noted several compelling reasons that made Cube standout:

Code-First Orientation: Cube's strong code-first orientation is a distinguishing feature. We firmly believe that the future of data engineering lies in applying proven software engineering practices - such as version control, code reviews, infrastructure testing - to data management. This will lead not only to data engineers having very granular control over the semantics of their data, but also structure these data models for reusability and maintainability. However, we also know that being able to work with data models with a GUI (graphical user interface) is also sometimes preferred. That’s why we combine the best of both worlds to allow data engineers to be code-first but the ability to jump into a GUI-based experience - like our recently launched Data Graph. We are happy that GigaOm agrees.

All the APIs: Cube's native API support ensures seamless integration with various data sources and analytics tools, making it a versatile choice for organizations with diverse tech stacks. We offer both data APIs - - GraphQL, REST API, and SQL API - as well as the Orchestration APIs and monitoring integrations to not only send modeled data to whatever data experience your users want, but also integrate Cube monitoring and observability into all the usual DevOps tools.

Analytics Pre-Processing: Cube's ability to perform analytics pre-processing through caching and pre-aggregations is a significant advantage that many competitors miss. This is especially critical in embedded analytics use cases where a data engineer is creating analytics for their customers. While it might be OK for an employee to wait for a minute or two when generating insights, it is never OK for a customer to wait even a nanosecond longer than necessary. And with Cube, customers can speed up queries from minutes to seconds - and seconds to sub-seconds - even with large datasets.

Check out our position on their sonar report.

Yes. We’re a Platform, Not Just a Feature We love that Gigaom recognized us as a platform play vs. just a feature, as we believe that having a semantic layer will become a necessity for all data stacks in the very near future. Only a company that has built a platform and is able to integrate with any data source and any data experience - customer dashboards, BI tools, or even AI agents - will be able to serve as that missing piece of the data stack.

Beating the Big Brands Part of the advantage of having an open source version of Cube is that we have been able to grow faster with thousands of developers reviewing, adding, and using our code. How else were we able to leap ahead of SAP, Oracle, and even Google!?

What Else Might you Get from this Report

But if you want more than a glowing evaluation of Cube, you can find it in this report. GigaOm has provided plenty of information about semantic layers and how they fit into various organizations both big and small as well as real-world use cases to help drive home the need for one. Learn what to consider when you decide to adopt a semantic layer, how to make that decision, and how to implement it into your data stack with minimal interruptions.

Get the Full Report and See for Yourself

For a comprehensive look at Cube's performance and a deeper dive into the world of semantic layers and metrics stores, we encourage you to read the complete GigaOm Sonar report. We offer the report here for free.