PV01 – Tokenized Bond Infrastructure
Scaling and reproducible infrastructure for a fintech market.
Challenge
PV01 needed to scale their tokenized bond platform with reproducible infrastructure that could handle high-frequency trading and complex financial regulations.
Our Approach
We migrated their entire infrastructure to AWS EKS, built comprehensive IaC pipelines using Terraform and Ansible, implemented CI/CD via GitHub Actions, and established monitoring with Prometheus & Grafana.
Results
Background
PV01 is an innovative startup focused on developing software solutions for tokenizing bonds on the blockchain. As the company sought to revolutionize traditional bond markets with blockchain technology, it recognized the need for a robust and scalable infrastructure to support its ambitious goals. PV01's decision to hire a DevOps team as their first move was crucial for laying a strong foundation for their operations.
Challenges
Infrastructure Setup
The startup required a comprehensive and scalable infrastructure to handle the complexities of blockchain technology and tokenization.
Rapid Development and Deployment
With a new and innovative product, PV01 needed to rapidly develop, test, and deploy its software to gain a competitive edge.
Operational Efficiency
Ensuring smooth operations and high availability was crucial, given the financial and blockchain domains' sensitivity to downtime and performance issues.
Best Practices Implementation
As the first DevOps team, it was essential to establish and implement industry best practices from the outset.
Objectives
Establish a Scalable and Reliable Infrastructure
Utilize cloud-based solutions to create a scalable and reliable environment for development and deployment.
Implement a Modern DevOps Pipeline
Develop and deploy a CI/CD pipeline to streamline development, testing, and deployment processes.
Optimize Monitoring and Alerting
Ensure robust monitoring, alerting, and logging mechanisms to maintain operational efficiency and quickly address issues.
Deliver Exceptional Value
Provide exceptional value to both the development team and the business through effective DevOps practices and tools.
Solution
Infrastructure Setup
AWS Cloud: Leveraged AWS as the cloud provider to build a scalable and flexible infrastructure. AWS RDS (PostgreSQL): Used AWS RDS for PostgreSQL to manage relational databases with high availability and automated backups. AWS ElastiCache (Redis): Implemented AWS ElastiCache for Redis to handle caching and improve application performance.
DevOps Pipeline
EKS (Elastic Kubernetes Service): Deployed applications using AWS EKS, enabling efficient container orchestration and management. Terraform: Utilized Terraform for Infrastructure as Code (IaC), allowing for the automated provisioning and management of cloud resources. Ansible: Employed Ansible for configuration management and automation.
Monitoring and Alerting
Prometheus: Implemented Prometheus for comprehensive metrics collection and monitoring. Grafana: Used Grafana to create detailed dashboards for visualizing metrics and KPIs. Loki: Integrated Loki for centralized logging. Alertmanager: Configured Alertmanager to handle alerts generated by Prometheus.
CI/CD Pipeline
GitHub Actions: Set up GitHub Actions for CI/CD, automating the build, test, and deployment processes. This integration streamlined development workflows and reduced manual errors.
Results
By leveraging AWS services alongside tools like Terraform and Ansible, PV01 was able to rapidly deploy infrastructure and meet tight deadlines with ease. The introduction of a modern CI/CD pipeline and robust automation significantly accelerated development and deployment workflows, enhancing overall developer efficiency. Operational visibility was elevated through integrated monitoring with Prometheus, Grafana, Loki, and Alertmanager, ensuring proactive performance management. Altogether, this comprehensive DevOps setup delivered exceptional value by providing PV01 with a scalable, reliable, and future-ready infrastructure.
Conclusion
The DevOps team at PV01 played a pivotal role in the startup's success by implementing industry best practices and leveraging modern tools and technologies from the get-go. By establishing a solid DevOps foundation, PV01 achieved exceptional results in terms of infrastructure scalability, operational efficiency, and developer productivity. This case study demonstrates the significant impact that a well-executed DevOps strategy can have on a startup's ability to innovate and compete in a rapidly evolving industry.
Credora – Credit Risk Platform
Credora offers real-time credit assessments and analytics, providing technology-driven credit ratings to improve efficiency in private credit markets.
Overview
Credora faced scalability, cost inefficiencies, and process limitations in their legacy infrastructure
Our Approach
We migrated the platform to Azure AKS, introduced auto-scaling and Intel SGX for confidential computing, integrated Vault for secrets management, and optimized resource usage to reduce costs. The DevOps pipeline was fully automated with GitHub Actions, while observability was established through Prometheus and Grafana, resulting in a secure, scalable, and SOC2 compliant infrastructure.
Results
Background
Credora offers real-time credit assessments and analytics, providing technology-driven credit ratings to improve efficiency in private credit markets. The platform utilizes AI-enabled automation for accurate credit assessments and facilitates access to capital through its network of distribution partners and lenders. Credora aims to enhance transparency and efficiency in credit markets with comprehensive credit reports and privacy-preserving technology. As Credora expanded, it needed to upgrade its DevOps pipeline to solve for scalability, cost and institutional grade processes.
Challenges
Scalability
As Credora’s user base and transactions increased, the DevOps infrastructure needed to keep up pace with that growth.
Cost
Credora’s sophisticated architecture and technical needs meant that without careful resource management, costs would become prohibitive.
DevOps Pipeline
Their institutional grade client base required processes that would be solved with a comprehensive DevOps pipeline.
Monitoring and Logging
Credora required a robust monitoring, alerting, and logging mechanisms to ensure timely resolution.
Security
Credora’s focus on data privacy meant leveraging Intel SGX ensuring the DevOps infrastructure had to be of the highest standard to maintain system integrity.
Objectives
Implementation of Azure AKS
Leverage Azure Kubernetes Service (AKS) for optimal scalability and resource management.
Cost Reduction
Implement an optimized infrastructure for predictable and reduced operational costs.
DevOps Pipeline Implementation
Ensure a complete DevOps pipeline to streamline CI/CD processes.
Enhanced Monitoring and Logging
Implement advanced monitoring, alerting, and logging solutions to ensure real-time visibility and issue resolution.
Security
Implement custom secure Kubernetes operators and CI/CD pipelines for managing privacy preserving architecture.
Solution
Migration to Azure AKS
Planning and Assessment: Conducted a thorough assessment of the existing infrastructure and identified a sustainable path forward. Infrastructure: Setup scalable infrastructure using Infrastructure as Code (Terraform). Containerization: Ensured all applications were containerized to guarantee flexibility and maintainability. AKS Setup: Leveraged AKS clusters to guarantee institutional grade security with configurability. Data Storage: Ensured sensitive data was warehoused in secure and stable environments with failover and risk mitigation built in. Testing: Built rigorous testing infrastructure to ensure any new applications can be seamlessly released to production.
Cost Reduction
Resource Optimization: Utilized Azure’s cost management tools to optimize resource usage, including right sizing VMs and using reserved instances. Auto-scaling: Implemented auto-scaling in AKS to dynamically adjust resources based on demand, thus avoiding over-provisioning. Cost Monitoring: Set up cost monitoring and alerting to keep track of expenditures and identify opportunities for further savings.
DevOps Pipeline Implementation
CI/CD Tools: Integrated tools such as Jenkins, and GitHub Actions to create a robust CI/CD pipeline. Automation: Automated build, test, and deployment processes to ensure quick and reliable releases. Version Control: Implemented version control best practices, ensuring all code changes. Security: Integrated security checks into the pipeline to ensure compliance and reduce vulnerabilities.
Enhanced Monitoring and Logging
Monitoring Tools: Implemented comprehensive infrastructure for application monitoring. Logging: Implemented detailed logging for quick issue diagnosis. Alerting: Set up proactive alerting mechanisms to spot anomalies or issues in real-time. Dashboards: Created detailed dashboards to visualize key metrics and KPIs, providing the team with actionable insights.
Security
Implemented robust institutional grade infrastructure guaranteeing secure storage of client data and privacy preserving computation of real-time data.
Results
Credora benefited from a scalable and flexible infrastructure that seamlessly accommodated growing user demand without compromising performance. Through resource optimization and Azure’s cost management tools, operational expenses were significantly reduced. The streamlined DevOps pipeline accelerated development, testing, and deployment, enabling faster time-to-market for new features. Enhanced monitoring, alerting, and logging capabilities ensured quick issue detection and resolution, resulting in greater system reliability and uptime. Additionally, the platform was secured with an institutional-grade, privacy-preserving architecture that maintained client data confidentiality—even in the event of a breach at the infrastructure level.
Conclusion
The DevOps implementation at Credora, resulted in a scalable, cost-effective, and efficient infrastructure. The creation of a complete DevOps pipeline and advanced monitoring solutions significantly enhanced the company’s operational capabilities, enabling it to continue its growth trajectory with confidence.
Kontxt @ RealNetworks
Kontxt, developed at RealNetworks, is a messaging and content categorization tool that enriches user communication experiences with smarter content organization.
Overview
RealNetworks needed an AI-driven platform to streamline content and message categorization for improved UX.
Our Approach
For Kontxt, we designed and built a scalable messaging platform tailored to handle large volumes of communication efficiently. We implemented intelligent content tagging and AI-driven categorization logic to enhance message organization and user experience. To ensure flexibility and future growth, we integrated a modular architecture that supports extensibility and enables real-time data analysis across the platform.
Results
Background
Kontxt@RealNetworks is a cutting-edge messaging and content categorization solution designed to improve communication experiences. Midway through its development phase, the product was not yet in production but was intended to be deployable on-premises, in a hybrid setup, or fully in the cloud (AWS). Kontxt faced several challenges in streamlining its deployment process, managing infrastructure costs, and ensuring compatibility with diverse customer requirements.
Challenges
Complex Deployment Process
The existing deployment process was cumbersome and error-prone, leading to delays and inefficiencies.
Infrastructure Management
Lack of infrastructure as code (IaC) practices made it difficult to manage and replicate environments consistently.
High QA Environment Costs
The cost of maintaining AWS environments for quality assurance (QA) was escalating.
Monitoring and Logging
The ELK stack used for logging and monitoring was resource-intensive and costly
Diverse Customer Needs
On-premise customers required support for running their own services like Apache Spark, Kafka, and Cassandra DB, necessitating a flexible and scalable solution.
Objectives
Simplify Deployment
Streamline and automate the deployment process to enhance efficiency and reduce errors.
Implement Infrastructure as Code
Use Terraform to manage infrastructure consistently across different environments.
Reduce QA Environment Cost
Optimize AWS resource usage to lower the costs associated with QA environments.
Modernize Monitoring and Logging
Replace the ELK stack with a more cost-effective and efficient monitoring solution.
Support On-Premise Deployments
Introduce Kubernetes to accommodate on-premise customer requirements for running additional services.
Solution
Simplified Deployment Process
Automation: Automated the deployment process using scripts and tools to reduce manual intervention and potential for errors. CI/CD Pipeline: Developed a continuous integration/continuous deployment (CI/CD) pipeline to facilitate seamless code integration, testing, and deployment.
Infrastructure as Code (IaC)
Terraform: Implemented Terraform to manage and provision infrastructure across all environments. This ensured consistency, repeatability, and easier scalability. Version Control: Used version control systems to manage Terraform scripts, enabling trackable and auditable changes to infrastructure.
Cost Reduction for QA Environments
Resource Optimization: Conducted a thorough analysis of AWS resource usage in QA environments and optimized instances, storage, and networking to reduce costs. Auto-scaling: Implemented auto-scaling for QA environments to ensure resources were only used when necessary, further reducing expenses.
Modernized Monitoring and Logging
Prometheus and Grafana: Replaced the ELK stack with Prometheus for metrics collection and Grafana for visualization, providing a more lightweight and cost-effective monitoring solution. Loki: Integrated Loki for centralized logging, offering efficient log aggregation and querying. Alertmanager: Implemented Alertmanager for handling alerts from Prometheus, ensuring timely notifications and incident response.
Kubernetes for On-Premise Deployments
Kubernetes Implementation: Introduced Kubernetes for managing containerized applications, enabling flexible and scalable deployments. Custom Services Support: Configured Kubernetes clusters to support on-premise customer requirements for running services like Apache Spark, Kafka, and Cassandra DB.
Results
The deployment process was streamlined through automation, significantly reducing the time and effort needed to roll out updates and new features. Infrastructure consistency was achieved using Terraform for Infrastructure as Code, enabling reliable environments that simplified scaling and ongoing maintenance. By optimizing AWS resources and introducing auto-scaling, QA environment costs were notably reduced. Monitoring and logging became more efficient and cost-effective with the adoption of Prometheus, Grafana, Loki, and Alertmanager. Additionally, the introduction of Kubernetes empowered on-premise customers to run required services independently, enhancing overall product flexibility and appeal.
Conclusion
The DevOps transformation at Kontxt@RealNetworks delivered significant improvements in deployment efficiency, cost management, infrastructure consistency, and customer satisfaction. By leveraging modern DevOps practices and tools such as Terraform, Kubernetes, Prometheus, Grafana, Loki, and Alertmanager, the team was able to provide exceptional value to both developers and business stakeholders. This case study illustrates the importance of adopting best practices in DevOps to streamline operations, reduce costs, and meet diverse customer needs, ultimately positioning Kontxt@RealNetworks for success in a competitive market.