High-Load Low-Code Platform For Enterprise Big Data Applications
- Low-Code
- Big Data
- Cloud & Data
- API & Integrations
- Business Application Development Services
- Front-End
Overview of the Client
Our client is a product company building a next-generation low-code platform for enterprise environments with high data volumes and complex business workflows. The company focuses on enabling organizations to design, deploy, and scale business applications faster while maintaining strict control over performance, data integrity, and infrastructure costs.
The goal was to create a modeling-driven platform that combines the speed of low-code development with the capabilities of professional engineering tools. The platform had to handle real-time operations and very large datasets. It also needed to support flexible deployment, both on-premise and in the cloud. The solution was designed for enterprises that need fast application delivery while still ensuring long-term scalability.
Challenge
The client set out to build a platform capable of supporting enterprise-scale workloads, but several technical challenges had to be addressed to make the product viable for real-world use.
- Massive data volumes. The system had to reliably store and process billions of records while maintaining the responsiveness expected from a real-time business platform.
- Heterogeneous data requirements. It was necessary to combine strict transactional consistency for sensitive domains such as financial operations and access control with the flexibility needed to handle large volumes of unstructured data.
- Balancing simplicity and power. The platform needed to remain easy to model and configure while still providing advanced capabilities such as scripting, debugging, and full-fledged development tools.
- Cloud cost and performance optimization. The client also required an approach that would allow moving historical data to cloud storage tiers without losing the ability to run analytical queries efficiently.
Primary Objectives
- Provide developer-grade modeling tools. Deliver a low-code environment equipped with an integrated IDE and debugger, enabling teams to design and refine complex business logic beyond traditional drag-and-drop capabilities.
- Build an elastic big data foundation. Ensure the platform can scale horizontally to billions of records through cluster-aware architecture and automatic sharding.
- Automate the frontend layer. Enable automatic UI generation based on data schemas and workflows to significantly reduce development time and accelerate product delivery.
- Enable extensible business logic. Introduce a proprietary domain-specific language that allows advanced customization without the complexity of general-purpose programming languages.
Project Overview
As part of the project, we developed a high-load, low-code modeling platform designed to support complex enterprise applications and massive datasets. The solution is built on a hybrid architecture that separates transactional and scalable data layers, while a modeling engine enables teams to define schemas, workflows, and business logic that automatically generate user interfaces, combining rapid delivery with enterprise-grade performance and flexibility.
- Region: Global
- Industry: Enterprise Software / Big Data
- Project Type: High-load low-code modeling platform
Solution
As a result, we delivered an enterprise-grade low-code modeling platform built for high-load environments and big data workloads. The solution combines a modeling-driven architecture with automatic UI generation, a functional-style high-level DSL supported by an integrated IDE and debugger, and a hybrid storage approach that uses MS SQL Server for transactional data, MongoDB with automatic sharding for large-scale datasets, and cloud storage for cost-efficient analytics, enabling the platform to scale to billions of records while maintaining performance and flexibility.
Key Features
- Integrated IDE & Debugger. A full development environment for building, testing, and troubleshooting complex business logic.
- Hybrid Cluster-Aware Storage. Automatic sharding in MongoDB combined with robust MS SQL Server management for transactional data, roles, and financial operations.
- Embedded Script (DSL). A high-level, flexible domain-specific language that enables advanced users to define logic in modeling terms without relying on general-purpose programming languages.
- Automatic UI Generation. Dynamic interface creation based on data schemas and workflows, significantly accelerating application development. Despite known solutions, it generates a rich and modern web UI on top of ExtJS and/or React, with enterprise-grade complex controls like tree grids, tables, charts, and interactive graphics.
- Cloud Data Lake Integration. Support for AWS S3 and Google Cloud Storage with SQL-based querying of JSON and Parquet datasets via AWS Athena. The external REST JSON / WebServices API integration is also supported, as well as data import from various formats in clouds like Dropbox, Google Drive, and OneDrive.
- Workflow Engine. Built-in capabilities for defining and executing complex business processes.
Technology Stack
To build the agentic AI platform, we used the following technologies:
Core Modeling
- Low-Code Modeling Engine / DSL
Development Tools
- Integrated IDE & Logic Debugger
Relational Database
- MS SQL Server (Users, Permissions, Financials)
NoSQL / Big Data
- MongoDB (with Automatic Sharding)
Cloud Storage
- AWS S3, Google Cloud Storage (GCS)
Analytics Engine
- AWS Athena (Querying Parquet/JSON)
Data Formats
- JSON
- Apache Parquet
Related Cases
- Java
- ERP
- FinTech
- Node.js
- Java Spring
- MySQL
- Java Spring
- MySQL
- PostgreSQL
- Objective-C
- SQLite
Core Team
- Platform Architects: Designed the distributed system architecture and defined the hybrid storage approach combining relational and NoSQL technologies.
- Language & Compiler Engineers: Developed the Embedded Script DSL along with the supporting IDE and debugging capabilities.
- Database Engineers: Implemented and optimized MongoDB sharding strategies and MS SQL Server performance for transactional workloads.
- UI/UX Framework Developers: Built the engine responsible for automatic UI generation and consistent user experience across applications.
- Cloud & Data Engineers: Managed cloud integrations, data pipelines, and storage layers across AWS/GCP environments and data lake infrastructure.
Business Impact
- Extreme Scalability. The platform can handle billions of records, making it suitable for large-scale enterprise environments and data-intensive applications.
- Reduced Development Cycle. Automatic UI generation and DSL-based scripting enable teams to build and launch complex applications in weeks rather than months.
- Financial Integrity. A dedicated SQL-backed layer ensures consistency and security for critical transactional domains such as financial operations and access control.
- Cloud Efficiency. The use of S3-compatible storage and serverless analytics (Athena) significantly reduces long-term data storage and processing costs.