#60 How to NOT fail in Platform Engineering

Here’s what we covered:Defining Platform Engineering* Platform engineering: Building compelling internal products to help teams reuse capabilities with less coordination.* Cloud computing connection: Enterprises can now compose platforms from cloud services, creating mature, internal products for all engineering personas.Ankit’s career journey* Didn't choose platform engineering; it found him.* Early start in programming (since age 11).* Transitioned from a product engineer mindset to building internal tools and platforms.* Key experience across startups, the public sector, unicorn companies, and private cloud projects.Singapore Public Sector Experience* Public sector: Highly advanced digital services (e.g., identity services for tax, housing).* Exciting environment: Software development in Singapore’s public sector is fast-paced and digitally progressive.Platform Engineering Turf Wars* Turf wars: Debate among DevOps, SRE, and platform engineering.* DevOps: Collaboration between dev and ops to think systemically.* SRE: Operations done the software engineering way.* Platform engineering: Delivering operational services as internal, self-service products.Dysfunctional Team Interactions* Issue: Requiring tickets to get work done creates bottlenecks.* Ideal state: Teams should be able to work autonomously without raising tickets.* Spectrum of dysfunction: From one ticket for one service to multiple tickets across teams leading to delays and misconfigurations.Quadrant Model (Autonomy vs. Cognitive Load)* Challenge: Balancing user autonomy with managing cognitive load.* Goal: Enable product teams with autonomy while managing cognitive load.* Solution: Platforms should abstract unnecessary complexity while still giving teams the autonomy to operate independently.How it pans out* Low autonomy, low cognitive load: Dependent on platform teams but a simple process.* Low autonomy, high cognitive load: Requires interacting with multiple teams and understanding technical details (worst case).* High autonomy, high cognitive load: Teams have full access (e.g., AWS accounts) but face infrastructure burden and fragmentation.* High autonomy, low cognitive load: Ideal situation—teams get what they need quickly without detailed knowledge.Shift from Product Thinking to Cognitive Load* Cognitive load focus: More important than just product thinking—consider the human experience when using the system.* Team Topologies: Mentioned as a key reference on this concept of cognitive load management.Platform as a Product Mindset* Collaboration: Building the platform in close collaboration with initial users (pilot teams) is crucial for success.* Product Management: Essential to have a product manager or team dedicated to communication, user journeys, and internal marketing.Self-Service as a Platform Requirement* Definition: Users should easily discover, understand, and use platform capabilities without human intervention.* User Testing: Watch how users interact with the platform to understand stumbling points and improve the self-service experience.Platform Team Cognitive Load* Burnout Prevention: Platform engineers need low cognitive load as well. Moving from a reactive (ticket-based) model to a proactive, self-service approach can reduce the strain.* Proactive Approach: Self-service models allow platform teams to prioritize development and avoid being overwhelmed by constant requests. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit read.srepath.com