AtScale has taken a major step by announcing the open source release of its Semantic Modeling Language (SML)This initiative aims to provide an industry-standard semantic modeling language that can be adopted across multiple platforms, fostering greater collaboration and interoperability in the analytics community. The introduction of SML marks an important step in the company’s decade-long journey to democratize data analytics and advance semantic layer technology.
AtScale’s journey began with a vision to create an enterprise interface that would allow users to interact with data. This led to the creation of an independent semantic layer that sits on top of technical data platforms, allowing business users to query data in terms they understand. Since its inception, AtScale has been committed to promoting the concept of a universal semantic layer that can work across different analytics tools and data platforms, making it easier for business users to gain insights without deep technical knowledge.
The need for an open standard
Semantic layers are fundamental to modern analytics platforms, bridging the gap between raw data and business insights. When AtScale was founded in 2013, no other vendor offered semantic layer platforms. However, the industry has seen a proliferation of semantic layer platforms from various vendors over the past decade. With the increasing diversity of tools, the need for a unified standard language for semantic modeling arose.
AtScale has launched open-source SML. The company aims to promote model portability, which will allow users to create semantic models that can be shared across platforms. A key motivation behind this move is to foster a community where model builders can create and share a library of reusable semantic models that can be plugged into any platform. This will save users time, allowing them to consume enterprise data with minimal technical setup.
What SML offers
SML is the result of more than a decade of hands-on development. It is designed to handle complex, multidimensional data from a variety of industries, including finance, healthcare, retail, manufacturing, and more. The language supports metrics, dimensions, hierarchies, and semi-additive measures, which are critical for building sophisticated analytical models.
SML offers several benefits to developers and organizations:
- Object-oriented structure: SML is designed to be object-oriented, so its semantic objects can be reused across different models, promoting consistency and efficiency in model building.
- Comprehensive scope: It is a superset of existing semantic modeling languages that incorporates over a decade of experience and use cases across different industries. This makes SML versatile enough to fit a wide range of applications.
- Familiar syntax: SML is based on YAML, a widely adopted, human-readable syntax, making it easy for developers to adopt the language without steep learning curves.
- CI/CD Supported: Being code-based, SML integrates well with modern software development practices, including Git for version control, and supports continuous integration and continuous deployment (CI/CD) workflows.
- Extensibility and open access: SML is Apache open source, meaning it is free to use and can be extended by the community. This open nature allows for innovation and collaboration, ensuring the language evolves to meet new demands.
What is open source?
AtScale makes several components available as part of its open source initiative:
- SML Language Specification: This includes tabular and multidimensional constructs, providing a comprehensive framework for model building.
- Pre-designed semantic models: These models, available on GitHub, cover standard data schemas such as TPC-DS and other common training models. AtScale plans to release models for popular SaaS applications such as Salesforce and Jira.
- Help classes and translators (coming soon): These will include programmatic tools to facilitate reading and writing SML syntax and translators to migrate models from other semantic languages, such as those used by dbt Labs and Power BI.
AtScale’s decision to open source SML represents a significant step toward fostering greater collaboration in the analytics industry. By creating a standard semantic modeling language, the company hopes to accelerate the adoption of semantic layers and promote the development of reusable and interoperable models. With the introduction of SML, AtScale is positioning itself at the forefront of the movement to standardize the expression of business logic and facilitate seamless interoperability of data and analytics across platforms.
In conclusion, the open source code for SML underlines AtScale’s commitment to democratizing analytics and creating a vibrant community around semantic modeling. As more organizations adopt the standard, it is expected to spur innovation and make analytics more accessible and efficient for all industry stakeholders.
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