How i helped rue la la become a one stop ecommerce boutique

1. 1) How I improved a Flash Sale site to become a unique one stop ecommerce boutique
Some of Nick’s initiatives with different enterprises (start ups, midsize and large) for digital transformation
are listed below. Few programs / initiatives or companies cannot be disclosed due to NDA. I may be unable
to discuss certain initiatives at greater length.
Innovation with AI, Cloud and IoT solutions: 1) Automower, 2) parkguide (live at Verizon) working with graduate
students at Columbia University.
Rue La La: How I improved a Flash Sale site to become a unique one stop ecommerce boutique
To the consumer, Rue La La is an online shopping destination that curates the best-selling brands from
boutiques filled with women’s, men’s, and kids’ fashion, home decor, travel experiences, and more. On the
back end, Rue La La is a technology business with the mobile platform, the data science, real-time analytics.
Hence it has a rapidly growing loyal customer base to lead the e-commerce revolution.
Rue La La, an innovative e-commerce destination known for connecting world-class brands with next-
generation shoppers, entered into an agreement to acquire Gilt(in June 2018), a leading member-based
digital shopping business.
Rue La La and Gilt, operating under the newly formed Rue Gilt Groupe, leverage an advanced technology
platform that combines leading capabilities in mobile and personalization.
What architecture and platform was built for Rue La La:
The Big Picture and what we learned /achieved:
 DynamoDB worked well for storing 100,000,000+ items of data that can change frequently. Docker
container to avoid Lambda.
 AWS API Gateway worked well with a direct integration to DynamoDB, albeit with a couple of
idiosyncrasies.
 Custom importers are required to load data into DynamoDB at scale and AWS Batch proves an
excellent way to run this code.
 AWS Cloud Formation served significantly better than the alternatives for bringing up repeatable stacks
and CI/CD pipelines.
 Our web and mobile applications look up and render personalized boutiques, brands, and styles for each
member when they log in. This is effectively a blocking call since although we can smooth out
rendering the page while we’re retrieving the data, the recommendation section of the page is only
actionable after they’re displayed.
 The landing page (on various devices-mobile or tablet or laptop) and products displayed change based
on the web history and past browsing.

2.  The format of the data returned by the Data Science APIs may be different then the data written by
Apache Spark. And in turn the data required to render pages or mobile app screens may be different
again.
 The API must be RESTful, secure enough, fast enough (average API call < 50ms, 95% < 150ms), and
highly available (> 99.99%).