Navigating Major Architecture Migrations

Architectural migrations are some of the most complex, high-stakes moves engineering teams undertake—often necessary, rarely easy, and always full of trade-offs. 
    
This track explores real-world stories of organizations making bold shifts in their systems—changing foundational technologies, rethinking architectural paradigms, and evolving long-standing infrastructure.
    
You'll hear hard-won lessons, unexpected hurdles, and practical guidance from those who've made (or are still making) these transitions. Whether you're planning your own migration or simply curious about how others are doing it, this track will help you make sense of the chaos and chart a clearer path forward.

From this track

Session

Monolith Down: Cleaning Up After the Great Identity Migration Disaster

Tuesday Nov 18 / 10:35AM PST

One does not simply migrate a monolith. Imagine a team working on a monolith-to-microservices migration of a healthcare portal. A foundational first step - migrating to a commercial identity provider - takes 9 months, only to bring the entire portal crashing down on release day.

Speaker image - Sonya Natanzon

Sonya Natanzon

VP of Engineering @Heartflow, Decomplexifier, Software Architect, Healthcare and Life Sciences Specialist, and International speaker

Session

Migrating from Monolith to Modern SaaS: Untangling 40 Years of Legacy

Tuesday Nov 18 / 11:45AM PST

What does it take to successfully modernize and migrate a mission-critical line of business running on a 40-year-old monolith to a more modern SaaS?

Speaker image - Indu Alagarsamy

Indu Alagarsamy

Principal Engineer @CircleCI, 25 Years of Software Development Experience

Session

Modernizing Relevance at Scale: LinkedIn’s Migration Journey to Serve Billions of Users

Tuesday Nov 18 / 01:35PM PST

How do you deliver relevant and personalized recommendations to nearly a billion professionals—instantly, reliably, and at scale? At LinkedIn, the answer has been a multi-year journey of architectural reinvention.

Speaker image - Nishant Lakshmikanth

Nishant Lakshmikanth

Engineering Manager @LinkedIn, Leading Infrastructure for "People You May Know" and "People Follows", Previously @AWS and @Cisco

Session

Migrating Uber Eats Feeds to Webview

Tuesday Nov 18 / 02:45PM PST

Uber Eats has many surfaces developed using native-first design. Historically these were built on the Android and iOS stacks. To accelerate development and enable rapid iteration and experimentation, while preserving the native-first design, a webview-powered stack was developed.

Session

Accelerating Data: A Cross-Team Journey from Offline to Online

Tuesday Nov 18 / 03:55PM PST

Details coming soon.

Session

Unconference: Navigating Major Architecture Migrations

Tuesday Nov 18 / 05:05PM PST

Date

Tuesday Nov 18 / 10:35AM PST

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Track Host

Janani Narayanan

Applied ML Engineer @Uber, Previously Tech Lead on DynamoDB Control Plane (Early Stage), 10+ Years Tech Industry Experience

Janani is a Senior Staff Engineer and Technical Lead Manager at Uber Technologies, where she has been at the forefront of transformative zero-to-one (0-1) initiatives for the past nine years. Prior to her current role, she was a pivotal contributor to SimpleDB and DynamoDB, playing a key role in their development during the initial four years post-public launch. Her work has led to several patents across both DynamoDB and Uber.

At Uber Eats, Janani leads a specialized "tiger team" focused on addressing the cold start problem by understanding selection. Her team's mission is to solve challenges related to eater and merchant retention by improving the precision of machine learning models that drive eater targeting, merchant acquisition, growth, personalization, ranking, and recommendations. Previously, she has successfully scaled UberEats’s Ads, Feed, and Search backends by 3x while maintaining low latency, optimized global rider pricing through advanced ML pipelines, and developed precise ETA predictions for accurate upfront pricing.

Janani has also built foundational infrastructure for anomaly detection in fares, created targeted rider promotions powered by machine learning, and developed distributed event processing systems capable of handling trip-scale data. These systems are crucial for real-time and batch processing, underpinning Uber’s real-time machine learning predictions across its marketplace, finance, and accounting functions.

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