Platform Engineering Essentials
Platform engineering aims to empower developers by providing them with a self-service infrastructure that abstracts away complexity and accelerates software delivery. This involves creating and managing a set of reusable tools, services, and infrastructure that developers can easily leverage to build, deploy, and run applications. Ultimately, platform engineering strives to increase developer productivity, improve software quality, and enhance overall organizational agility. Platform engineering is much more building infrastructure and deploying applications though. You must also account for security, observability, data management, and life cycle management.
Security By Design
Built-in security is a crucial pillar of platform engineering, ensuring security is integrated throughout the entire platform lifecycle rather than being an afterthought. This proactive approach focuses on embedding security practices and controls directly into the platform's design, development, and operation. By prioritizing security from the outset, organizations can significantly reduce vulnerabilities, minimize the attack surface, and enhance the overall resilience of their systems. This approach also shifts security left, enabling developers to identify and address security issues earlier in the development process, reducing remediation costs and time.
Implementing built-in security involves various practices and technologies. These include secure coding practices, automated security testing (static and dynamic analysis), infrastructure as code (IaC) security scanning, vulnerability management, and robust access control mechanisms. By automating security checks and integrating them into the continuous integration/continuous delivery (CI/CD) pipeline, organizations can ensure consistent enforcement of security policies and rapid identification of potential threats. This automation is vital for maintaining a secure posture in a fast-paced development environment.
Built-in security fosters a culture of shared responsibility for security across development, operations, and security teams. This collaborative approach promotes better communication and enables faster responses to security incidents. By making security an integral part of the platform engineering process, organizations can build more secure and resilient systems, reducing the risk of costly breaches and protecting sensitive data. For more information on security in platform engineering, consult resources like the Cloud Native Computing Foundation (CNCF) TAG Security and the National Institute of Standards and Technology (NIST) Cybersecurity Framework. People at US-based complanies should be familiar with both of these.
Built-In Observability
Observability and monitoring are essential pillars of platform engineering, providing crucial insights into the health, performance, and behavior of platform components and applications. Monitoring focuses on tracking predefined metrics and logs to detect known issues and anomalies. Observability goes further, enabling teams to understand the internal state of a system by exploring its outputs, even in the face of unforeseen problems. This combination empowers both developers and operations teams to proactively identify and address issues before they impact users.
For developers, observability and monitoring provide valuable feedback on the performance and behavior of their code in a production environment. Detailed metrics, logs, and traces allow developers to pinpoint performance bottlenecks, identify bugs, and understand how users interact with their applications. This insight is crucial for optimizing code, improving user experience, and accelerating the development lifecycle. For operations teams, these tools offer real-time visibility into the platform’s health, enabling them to detect and respond to incidents quickly, ensure service availability, and maintain system stability.
By implementing robust observability and monitoring practices, platform engineering teams empower both developers and operations with a shared understanding of the system's state. This shared context facilitates better collaboration, faster troubleshooting, and more effective incident response. Tools and technologies like distributed tracing, metrics dashboards, and log aggregation platforms are fundamental to achieving comprehensive observability and monitoring in modern platform architectures. Resources such as the OpenTelemetry project and books like "Observability Engineering" provide further guidance.
Data Management
Data management in platform engineering encompasses the strategies and practices for handling data within the platform ecosystem. This includes data storage, processing, access, and governance. Effective data management is crucial for ensuring data availability, integrity, and security, while also optimizing performance and controlling costs. Key considerations include data locality, data movement, and the organic growth of data volumes. Data locality, placing data closer to where it is processed or consumed, can significantly improve performance by reducing latency. However, it can also increase storage costs if data needs to be replicated across multiple locations. Data movement, transferring data between different storage systems or locations, incurs network bandwidth costs and can introduce delays. Optimizing data movement strategies, such as using efficient data transfer protocols and minimizing unnecessary data replication, is essential for cost and performance efficiency.
The organic growth of data volumes, a natural consequence of increasing usage and data generation, presents ongoing challenges for data management. As data volumes grow, storage costs increase, and data processing becomes more complex. Platform engineering teams need to plan for this growth by implementing scalable storage solutions, optimizing data indexing and querying strategies, and implementing data lifecycle management policies (e.g., archiving or deleting older data). This proactive approach benefits both developers, by ensuring consistent data access and performance as data scales, and operations, by simplifying capacity planning and reducing the risk of storage bottlenecks.
Effective data management also involves implementing robust data governance policies to ensure data quality, compliance with regulatory requirements, and data security. This includes defining data access controls, implementing data encryption, and establishing data backup and recovery procedures. By addressing data locality, data movement, and organic growth proactively and implementing strong data governance, platform engineering teams can build high-performing, cost-effective, and secure data platforms that empower both developers and operations.
Life Cycle Management
Lifecycle management in platform engineering governs the entire lifespan of the platform and its components, ensuring stability, security, and maintainability. This includes managing infrastructure, tooling, APIs, and services. Crucially, version control extends beyond application code to encompass all platform elements, enabling change tracking, rollbacks, and team collaboration. This benefits both developers, by providing a clear history of changes and facilitating experimentation, and operations, by simplifying troubleshooting and disaster recovery. Effective dependency management between platform components is also a core aspect.
Addressing technical debt is vital for healthy lifecycle management. Accumulating technical debt slows development, increases maintenance costs, and creates security vulnerabilities. Regularly addressing it through refactoring and upgrades benefits developers by simplifying codebases and improving development velocity, and benefits operations by reducing system complexity and improving stability. This proactive approach ensures the platform remains adaptable, benefiting both development and operations in the long run.
Key Takeaways
To build great internal developer platforms, platform engineering depends on a few key things. Security has to be baked in from the start—that's security by design—to keep things safe. Built-in observability gives everyone a clear view of how the platform's doing, so problems can be caught early. Smart data management keeps data organized and accessible without breaking the bank. And solid lifecycle management handles everything from building to retiring platform components, with a focus on version control and keeping technical debt in check. When these principles work together, the result is a platform that is reliable, secure, and easy to maintain, enabling development teams to move faster and deliver greater value. How is your organization handling platform engineering?
