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Ubix
A Domain-Specific Language That Cut Ubix's Analysis Time by 90%
Analysis time
−90%
Who can explore data
data scientists
domain experts
Ubix builds Ubix Engine, a big data science platform for data science automation and advanced analytics, serving historical and real-time streaming data under a unified lambda architecture.
What changed
Before
Request
Analyst asks
Bottleneck
Data scientist
writes the code
After
Interface
Custom DSL
no programming needed
Engine
Spark
Cassandra
Elastic Stack
Delivery
Self-service
−90% time to insight
A domain-specific language put exploration in the hands of analysts, so the data-science team stopped being the queue.
What we built
Ubix, an analytics company, wanted to enable their domain experts (non-technical users) to efficiently explore data and build predictive models without writing complex code. Data scientists also required a more advanced framework for creating sophisticated models quickly. We built a custom Domain-Specific Language (DSL) that simplified data exploration, feature engineering, and model building — intuitive for non-programmers but flexible enough for technical users.
Key Features
Built a DSL system that enables non-technical users to explore data, perform feature engineering, and build predictive models with simple, human-readable commands.
Enabled data scientists to use advanced modeling techniques and integrate with existing tools for deep analysis.
Provided a web-based interface for running DSL commands, generating visualizations, and creating reports.
Ensured scalability and support for large datasets in a distributed environment.
Results
Analysts who previously relied on data scientists could now explore datasets and build models independently using simple DSL commands, reducing time spent on data analysis by 90%.
Domain experts were able to perform advanced data exploration and modeling tasks, leading to better insights and decision-making without requiring programming skills.
Implemented a fully automated CI/CD pipeline to support continuous deployment.
Configured monitoring, alerting, and automated scaling to enhance operational efficiency.
Designed product infrastructure architecture on AWS and implemented automatic deployments using IaC.
Stack
Engagement: 2 years · Real-time & Industrial Data Platforms, Platform & Cloud Engineering
Next step
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