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This is the second in a series of three blog posts about Rollup, AdRoll’s UI component library. This post covers how we build individual components and the developer tools supporting them.
A few months ago we deployed two new and nearly identical dashboards. For the launch of those dashboards, we built Rollup, a UI component library. In this post we talk about how the component library allows us to easily share code between teams and maintaing a consistent look and feel across products.
From math teacher to software engineer, Sadie tells the story of her journey to AdRoll’s engineering team.
This is the second article in a series that describes how we built AdRoll Prospecting. This time, we’ll talk about managing batch job dependencies.
This is the first article in a series that describes how we built a new data-intensive product, AdRoll Prospecting, using an architecture based on Docker containers.
The data science team at AdRoll is constantly working to improve our programmatic bidding algorithm, BidIQ. One recent improvement to BidIQ has been the introduction of a novel modeling technique called Factorization Machines, which is allowing AdRoll to more accurately value every potential impression that comes our way.
In this post, Tim writes about a bash script to run bash scripts.
It has been a long time outstanding to do item to start collecting feedback and net promoter scores from the tens of thousands of customers that AdRoll has. Once at scale it becomes surprisingly challenging to ensure that feedback from users is sent to Product, Engineering and SMB teams.
The AdRoll Growth team was created to ensure that as AdRoll continues to build out more great products for marketers in more regions, we stay true to our founding philosophies. As we ourselves grow, we want the SMB advertisers that make up the core of our business to grow along with us.
By migrating AdRoll’s real-time data pipeline to Kinesis we were able to reduce our end to end latency more than one hundredfold while simultaneously cutting costs and improving system stability. Here we’ll follow architectural decisions, implementation details, and overall learnings from this process.