Core Tenets to Build a Scalable, Heterogeneous and Interoperable Cloud Platform

The need for a heterogenous, scalable, and interoperable cloud platform to manage business applications efficiently has never been more pressing. Studies predict that by 2025, over 95% of enterprise data workloads will reside in clouds. Agile development practices empower application engineers to develop and introduce product features faster than ever before. This further emphasizes the need for platforms that can adapt swiftly to changing requirements and enable businesses to innovate at an accelerated rate.?

Business applications are becoming increasingly heterogeneous, comprising a mix of custom applications, open architecture, and off-the-shelf software. This diversity poses a significant challenge for IT teams, as they have to manage a variety of workloads with distinct requirements. Traditional IT infrastructure, with its rigid structure and limited flexibility, is not well positioned to address this challenge.??

Integrated platforms empower enterprises to effectively streamline heterogeneous workloads such as: transactional, real-time, batch, big data and advanced analytics. Platforms should be scalable, secure, and universally available, providing the tools and frameworks necessary to manage complex workloads efficiently. Additionally, inculcating a mindset beyond a monolithic centralized data lake to a modern intentionally distributed architecture of Data Mesh will benefit in the long-run. Data Mesh treats Data As A Product, with domain-oriented decentralized data ownership, self-service data infrastructure as a platform and federated data governance. The priority should be maturing a data science platform that enhances current MLOps foundation to build Large Language Models (LLM)?

Microservices Architecture: Unveiling the Challenges?

Microservices architecture has gained immense popularity in recent years, as it offers faster delivery and deployment, enhanced automation, and improved scalability. However, microservices also introduce a host of complexities.

Design: The very nature of microservices, characterized by their independent existence, poses a significant challenge during the design phase. Developers might struggle to determine the optimal size and scope of each microservice to ensure cohesion and avoid duplication. The task of establishing a robust framework that seamlessly integrates these independent entities further compounds the design complexity.?

Security: The widespread adoption of multi-cloud environments for microservices deployments introduces several security concerns. The loss of centralized control and the diminished visibility into the system’s inner workings leave applications vulnerable to exploitation. Furthermore, microservices communicate through different infrastructure layers causing a challenge for developers in identifying and mitigating potential vulnerabilities.?

Model Lifecycle Development: While microservices revolutionize application development, they introduce a new layer of complexity to the model lifecycle development process. As every microservice group is independent, teams face different challenges: which technology stack to adopt, how to deploy and manage each microservice, and where to host them. As industry is ripe for Gen AI disruption, leveraging strong analytics capabilities to build Large Language Models (LLM) utilizing the rich large data assets is critical for the management of model lifecycle.??

Lack of Resilience: Traditional applications rely heavily on the underlying infrastructure for their stability and performance. If the infrastructure experiences downtime and performance issues, applications break and affect the microservices approach. As multiple services are deployed, developers will spend time repairing various parts of a microservice.?

Complex Coordination: As the number of microservices grows, the complexity of coordinating their interactions increases significantly. Developers must establish clear communication protocols and orchestration mechanisms to ensure seamless interactions between services.?

When assisting in platform development, teams not only focus on addressing the cycle time challenges of microservices by having DecSecFinOps, but also providing automated pipelines for MLOps with feature engineering and model risk monitoring (MRM) capabilities to build ML and LLM models.?

My philosophy when building platforms is to create a reliable, secure, resilient, available, and performant platform that prioritizes scalability and efficiency. The control plane dictates how data and applications are managed, routed, and processed, while the data plane is responsible for the actual movement of data. A truly resilient cloud platform constantly strives to become the most trusted and ubiquitous, unleashing technologies that foster innovation and power the next generation of business ecosystems.?

Data Mesh architecture avoids multiple copies of data, minimizes maintenance overhead, positioning us to respond to client changes faster while meeting regulatory compliance effectively. Robust data catalog and high degree of self-service platform automation will drive efficiencies across multiple lines of business.?

To achieve the goal of building reliable, secure, and high-performance platforms, we need to adhere to five core tenets:?

Security: A secure platform is the foundation of a successful organization. We prioritize security by implementing a comprehensive security strategy to address any potential security threats and protect critical organizational data. Continually monitoring the platform ensures a secure dedicated environment for our client’s products and services. Security strategy should cover:?

• Identity and Access Management (IAM): Determine who has access to what data and for how long. It includes authentication techniques, role-based access control and access auditing.?
• Data Security and Privacy: Ensure data security and encryption when in use, inflight, and at rest.?
• Vulnerability Management: Identify, classify, prioritize, and remediate vulnerabilities in the platform infrastructure and software components.?
• Threat Protection: Safeguard the platform from all potential cyber threats, including malware, ransomware, phishing attacks, and distributed denial-of-service (DDoS) attacks.?

Standardization: It promotes consistency and efficiency across the platform by establishing a pre-defined set of software and capabilities for network, storage, database, load balancers, software assets, virtualization, and containerization. This ensures that development teams have a common foundation to work on. We foster standardization through:?

• Automation: Mitigate the challenges associated with manual management of assets and help implement modern programs with better agility and efficiency. It helps build a centralized platform that can manage the development and deployment of applications in a seamless, integrated manner.??
• Single Source of Truth (SOT): Along with automation, it maintains SOT for all assets to drive homogeneity on our platform. Every code change goes through rigorous reviews and is deployed through our CI/CD pipeline.?
• GenAI and LLM Data Science Platform: Maturing Data science platform to embed Data scientists into Product and Engineering DNA drives standardization. Platform will provide the standardized capability to build, promote, deploy and train LLM models for experimentation and eventually productizing for our clients.?

Observability:? Observability practices monitor, log, trace, profile, and debug platform performance, enabling us to identify and address flaws quickly. It facilitates a proactive mode of operations, monitoring and measuring all events to have a holistic view of all activities. However, you should always ensure proper instrumentation and telemetry to capture activity data and gain accurate measurement. Proactive health checks enable us to identify & mitigate issues before clients encounter them. Additionally, giving platform teams the opportunity to implement preventive actions? drives continuous improvement and service excellence culture.??

Elasticity: Leverage auto-scaling to dynamically adjust infrastructure resources based on changing workload requirements, ensuring optimal performance and resource utilization. Auto-scaling based on demand eliminates outdated capacity management and related errors. PaaS Infrastructure Manager provides control plane and data plane automation, monitors, and collects metrics, remediates, and upgrades live components, and manages capacity by scaling up or down.??

?Resilience: High availability with four nines is table stakes. Building resilience ensures platforms? can withstand disruptions and continue to operate even in the face of failures. Customers demand uninterrupted services, especially during peak times, and if done right can be a major competitive advantage. Nevertheless, developers should incorporate resilience not just in the architecture but also throughout the deployment lifecycle and continuous monitoring. Additionally, we need to increase our focus on operational discipline to test and certify changes during software upgrades to ensure zero downtime.?

We incorporate these platform tenets from the start–during MVP product development. This enables us to align with product requirements and scale seamlessly. Our goal is to provide scalable, secure, and high-performance cloud platforms that empower our clients to accelerate innovation and derive deeper analytics insights. This platform philosophy, centered on security, standardization,?observability,?elasticity,?and resilience,?guides us in building robust cloud platforms. Ultimately,? it helps businesses thrive in the ever-evolving technology landscape and grow efficiently.?