AtScale on Wednesday announced the latest version of its Semantic Layer platform. The platform features a new architecture aimed at enabling decision agility and collaboration, and includes support for both traditional AI and large-scale language models.
In addition, AtScale announced a free version of its platform designed to introduce new users to the vendor's semantic layer capabilities.
The vendor announced the Developer Community Edition and overall platform updates during Semantic Layer Summit, AtScale's annual user conference in Boston. Both are currently in public preview.
Boston-based AtScale is a semantic layer specialist whose platform aims to help facilitate self-service analytics, data sharing, and collaborative decision-making.
A semantic layer is a tool that allows data managers to define key metrics and standardize terminology across the organization. As a result, data consistency is ensured no matter which department is handling the data and data duplication is avoided.
The semantic layer allows self-service users to search and share data without having to know how to code, query, or join data tables or sources. These are essentially conduits to business users, said Steven Catanzano, an analyst in TechTarget's enterprise strategy group.
“The semantic layer acts as a bridge between raw data sources and end-user applications, providing a simplified, consistent, and efficient way to access and analyze data,” he said. Ta.
Meanwhile, BARC US analyst Kevin Petrie says they are especially useful now as the amount and complexity of data continues to increase.
“This layer is more important than ever as enterprises need to integrate and analyze increasingly diverse data sets across increasingly distributed and heterogeneous environments,” he said. . “BI and data science teams need to reconcile different formats and schemas so they can analyze data in a unified way.”
In addition to AtScale, other vendors offering semantic layer functionality include DBT Labs and Google-owned Looker.
New features
According to the vendor, AtScale's platform updates are centered around three pillars.
One is the flexibility of a universal semantic hub that supports all AI and BI platforms and allows organizations to work with data and AI products developed with tools from any vendor. Another is improved discoverability features and collaboration with both code-first and no-code modeling tools that facilitate sharing between different personas within an organization. Finally, there's the community, which allows anyone to build and share semantic models, including sharing between partner organizations.
To support flexibility, collaboration, and community, AtScale updates include the following features within the Semantic Layer platform:
- A metadata hub that enables users to discover the data they need to enrich both traditional and generative AI models with their own data. Enable your models to understand your specific business and provide domain-specific insights to inform decision-making.
- Container deployment. Tailored for cloud-based customers, the ability to add deployment options such as Kubernetes and Docker that add scalability and automation capabilities.
- Integration with DBT Labs enables DBT semantic models to work with AtScale's support for analytics platforms such as Tableau, Microsoft Power BI, and Microsoft Excel.
- A new integrated development environment (IDE) enables both code-first and no-code semantic data modeling and includes support for continuous integration/continuous delivery integration with Git.
- Enhanced support for enterprise integration and authentication through open source platforms Keycloak, OpenTelemetry, OpenAPI, and KeyGen.
The emphasis on sharing is particularly noteworthy, Petrie said.
“AtScale truly recognizes the need to share data views across and across enterprises,” he said. “By providing data teams with modular, portable, and reusable semantic objects, we can standardize how stakeholders across partner organizations share data. This allows for more efficient collaboration. analysis is possible.”
Additionally, the new IDE is an important addition, Petrie continued.
“The new UX for semantic modeling should help accelerate the adoption of the semantic layer across teams and enterprises,” he said. “If we can simplify the modeling experience for basic users while still catering to code-first users, we can encourage broader usage.”
Meanwhile, Catanzano noted that improved integration support is an important addition to expanding AtScale's ecosystem. However, he added that such integration support is becoming the norm, so while it is attractive, it is not a differentiator.
“The most important thing is [addition] We're consolidating with some of the major vendors, but this is quickly becoming a table-take and a do-it-all for me,” he said.
While data sharing and a new IDE are among the more important new features, AtScale founder and chief technology officer Dave Mariani said the new features were driven in part by customer feedback. did.
In particular, users want improved sharing of semantic objects and versioning of semantic models, which is now possible through AtScale's integration with Git and by storing semantic models as code, he said.
Beyond customer feedback, Mariani continued, the update targets a new user persona. Currently, in addition to business analysts, data engineers are also targeted.
“With AtScale, data engineers and business analysts alike can now collaborate on semantic modeling on one platform,” he said.
In addition to AtScale's platform updates, the vendor's new Developer Community Edition introduces its semantic layer capabilities to potential new customers.
The free version includes use of AtScale's semantic modeling language, which facilitates the sharing and reuse of semantic objects by organizations adopting data meshes and similar distributed data strategies. An easy-to-use interface and a GitHub repository for prebuilt semantic models that you can reuse across your organization.
AtScale typically charges on a pay-as-you-go basis, but does not publish pricing details.
Future Plans
Our analysis shows that with AtScale's platform updates and Developer Community Edition currently in public preview, vendors should continue to add integration capabilities.
Catanzano recommended that AtScale add new integrations with generative AI vendors.
Meanwhile, Petrie suggested that AtScale would be wise to add integration with vector databases.
Vendor metadata hubs provide powerful tools to help users give business context to both traditional and generative AI models. According to Petrie, one of his potential use cases is to enhance vectorization of unstructured data.
“I'm interested in how companies actually use it.” [the metadata hub] to enhance labeling and vectorization of unstructured data objects,” he said. [therefore] You may want to focus more on integration with vector databases and other elements of the GenAI ecosystem. ”
Eric Avidon is a senior news writer for TechTarget Editorial and a journalist with over 25 years of experience. He is responsible for analysis and data management.
