Deepbloo: Serverless Migration for EU Energy Data SaaS Platform
An EU energy data SaaS modernized from a monolithic backend into scalable serverless microservices with secure GraphQL APIs.
Client
Deepbloo
Industry
Services
About Deepbloo
Deepbloo is an EU-focused energy data SaaS platform serving enterprise users across the energy sector. As the product grew, the existing monolithic backend became harder to scale, maintain, and optimize for performance.
The project focused on modernizing the backend architecture by migrating core services into serverless microservices, improving API reliability, optimizing PostgreSQL query performance, and reducing manual DevOps overhead through infrastructure automation.
Project Goals
Modernize Backend Architecture
Migrate from a highly coupled monolith to scalable serverless microservices.
Improve API Reliability
Build faster and more reliable APIs for enterprise platform users.
Optimize Database Performance
Reduce PostgreSQL query latency through optimized RDS query handling.
Secure Enterprise Access
Use Cognito and secure API patterns for internal and external consumers.
Automate Infrastructure Setup
Use AWS CDK to reduce manual DevOps and deployment overhead.
Key Challenges
Modernizing Deepbloo required carefully moving from a monolithic backend to serverless microservices while improving performance, security, and operational efficiency.
Reducing monolithic coupling
The existing backend had tightly coupled services, making updates harder and increasing the risk of changes affecting unrelated features.
Improving slow database queries
PostgreSQL query latency had to be reduced so enterprise users could access energy data faster and more reliably.
Creating secure API access
The platform needed secure GraphQL APIs for internal teams and external consumers without adding unnecessary backend complexity.
Reducing DevOps workload
Manual infrastructure work was slowing delivery, so the setup needed automation for repeatable infrastructure-as-code.
Driving Deepbloo's Serverless Modernization
GeeksVisor supported Deepbloo by designing and implementing a serverless migration strategy, improving API performance, securing access, and automating AWS infrastructure.
Serverless Backend Migration
Moved monolith into scalable microservices.
GraphQL API Development
Built secure unified API layer.
PostgreSQL Query Optimization
Reduced latency for energy data.
Infrastructure Automation
Automated provisioning with AWS CDK.
Serverless Microservices
Used AWS Lambda and API Gateway to create database backend services that scale independently.
Unified GraphQL Layer
Implemented AppSync with GraphQL to provide a cleaner and more reliable API experience.
Secure User Access
Integrated Cognito to manage secure access for enterprise users and API consumers.
Optimized RDS Queries
Improved PostgreSQL query performance on RDS to reduce latency for data-heavy workflows.
Infrastructure as Code
Used AWS CDK to automate provisioning, reduce manual setup, and improve deployment consistency.
A Serverless Microservices Architecture Built for Enterprise Energy Data
We rebuilt Deepbloo's backend using AWS Lambda, API Gateway, AppSync, Cognito, RDS PostgreSQL, and AWS CDK to improve scalability, API reliability, database performance, and deployment efficiency.
40% Lower Coupling
Reduced system coupling by migrating monolithic backend logic into independent serverless microservices.
35% Faster Queries
Optimized PostgreSQL queries on RDS to reduce latency for enterprise data workflows.
60% Less DevOps Work
Automated infrastructure setup with AWS CDK, reducing manual operational workload.
Secure GraphQL APIs
Delivered secure APIs for internal teams and external enterprise consumers using AppSync and Cognito.
More Scalable Platform
Built a backend foundation ready to support enterprise growth across European energy data workflows.
From Monolithic Bottlenecks to Scalable Serverless APIs
Deepbloo became a more scalable, secure, and maintainable energy data platform with faster queries, lower coupling, and significantly reduced operational overhead.
Key Technologies & Platforms
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