AtScale, the leading provider of semantic layer solutions for modern business intelligence and data science teams, today announced new capabilities within its semantic layer platform to support code-first data modelers including developers, analytics engineers, and data scientists.
These new capabilities tightly integrate with AtScale’s existing no-code visual modeling framework and provide flexibility to build and manage data models and metric definitions within the semantic layer using code-based modeling frameworks.
“Analytics engineers and other code-first data modelers need the flexibility of a markup language and automation scripts to build and maintain the sophisticated data models underlying a robust semantic layer,” said Dave Mariani, founder and CTO for AtScale. “AtScale’s modeling language is built on best practices of dimensional analytics and seamlessly integrates with our metrics serving engine, ensuring optimal performance and cost efficiency of analytics queries, while maintaining tight integration with analytics layer tools.”
AtScale is announcing three new capabilities to support code-first data modeling:
AtScale Modeling Language (AML) Delivers Flexibility to Analytics Engineers: AML allows analytics engineers to design dimensional models that logically represent views of raw data, optimized for business intelligence (BI) and data science. AtScale models include table joins, dimensional hierarchies, and metrics definitions, as well as rich metadata to support user interaction from analytics tools. Models built in AML can be also accessed and modified from AtScale’s visual modeling canvas. Likewise, models built in the canvas can be accessed and edited within code. This new option also brings CI/CD support with Git integration for all AtScale models. AML is in private preview for AtScale customers. This blog explores code-first data modeling in more detail.
dbt Metrics Serving Brings Open Source Modeling Alternative: AtScale can now “read” dbt Metric definitions directly from Git project files, establish the connections to dbt Models implemented on cloud data platforms, and serve dbt Metrics to AtScale-supported analytics tools including Excel, Power BI, Tableau, Qlik and Looker, as well as through Python and REST APIs. This approach lets analytics engineers work within a familiar, open source modeling environment while leveraging AtScale’s proven, enterprise-class analytics layer integration, push-down query execution, and automated aggregate orchestration. dbt Metrics serving is in private preview for AtScale customers.
Also Read: 5 Steps to Developing a No-Code Chatbot for Free
Python-based Metrics Engineering for Flexible and Efficient Management of Metrics Stores: AtScale’s AI-Link now includes Python utilities, enabling programmatic interaction with metric definitions built in AtScale. This allows organizations to create, read, update, and delete a range of AtScale objects using a Python API. This includes the capability to define and update definitions for large sets of metrics, including calculated, time-relative, and categorical metrics using automation. This provides a simple and efficient approach for analytics engineers and data scientists to manage large metrics stores, avoiding time-intensive, manual updates. Python-based metrics engineering is generally available within AtScale AI-Link. This blog explores the topic of metrics engineering in more detail.
The AtScale semantic layer platform delivers a comprehensive data modeling solution that empowers organizations to achieve greater efficiency and productivity for their resource-constrained data teams. With support for both visual and code-based modeling, AtScale enables collaboration among analytics engineers, BI teams, and data scientists in the data modeling process. This flexibility allows organizations to choose the best approach for their teams, bringing the broadest set of personas into the data modeling process, maximizing resource efficiency and accelerating analytics innovation.
AtScale is a universal semantic layer platform built to support enterprise data teams in optimizing analytics experience and delivering self-service analytics to their users. Data models defined within AtScale are served on demand, to common business intelligence platforms, including Excel, Power BI, Tableau, Qlik, and Looker, with no client-side add-ins or intermediate caching of data. End-user queries are dynamically optimized and orchestrated on modern cloud data platforms, such as Snowflake, Databricks, Google BigQuery, Amazon Redshift, and Microsoft Azure Synapse.
