Yelp Dataset Challenge Round 6 Winners The sixth round of the Yelp Dataset Challenge ran throughout the second half of 2015 and we were really impressed with the projects and ideas that came out of the challenge. Today, we are proud to announce the grand prize winner of the $5,000 award: “Topic Regularized Matrix Factorization for Review Based Rating Prediction” by Jiachen Li, Yan Wang, Xiangyu Sun, Chengliang Lian, and Ming Yao (from the Language Technologies Institute, School of Computer Science, at Carnegie Mellon University). The authors created a recommender system to inform Yelpers about which business they might be...

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This post is part of a series covering Yelp's real-time streaming data infrastructure. Our series explores in-depth how we stream MySQL and Cassandra data at real-time, how we automatically track & migrate schemas, how we process and transform streams, and finally how we connect all of this into data stores like Redshift, Salesforce, and Elasticsearch. Read the posts in the series: Billions of Messages a Day - Yelp's Real-time Data Pipeline Streaming MySQL tables in real-time to Kafka More Than Just a Schema Store PaaStorm: A Streaming Processor Data Pipeline: Salesforce Connector Streaming Messages from Kafka into Redshift in near...

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For the past few months we’ve been working on revamping our API based off your feedback of wanting more Yelp data and functionality. Today, we’re excited to announce that the newest version of our API is entering developer preview. What’s new? We’re exposing two new features as part of the developer preview: autocomplete and transaction search. As a user performs a search, autocomplete will help them find what they want (some might even say we have the ability to read their minds). With autocomplete, a user’s search experience will feel much more intuitive. The API now exposes a search endpoint...

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This post is part of a series covering Yelp's real-time streaming data infrastructure. Our series explores in-depth how we stream MySQL and Cassandra data at real-time, how we automatically track & migrate schemas, how we process and transform streams, and finally how we connect all of this into data stores like Redshift, Salesforce, and Elasticsearch. Read the posts in the series: Billions of Messages a Day - Yelp's Real-time Data Pipeline Streaming MySQL tables in real-time to Kafka More Than Just a Schema Store PaaStorm: A Streaming Processor Data Pipeline: Salesforce Connector Streaming Messages from Kafka into Redshift in near...

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One of the values that we cherish at Yelp is to “Be Unboring”. It’s the quality of never accepting “standard” as okay and the guiding principle for creating new and remarkable things. One of the ways we foster that creative spirit at Yelp Engineering is through our internal hackathons. Many remarkable projects have come out of them - some of them open sourced, some of them revealing interesting demographic insights and some of them pushing the boundaries of science & technology. The 19th edition of our internal hackathon wasn’t any different. Close to 80 fantastic projects across our engineering offices...

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At Yelp we leverage Cassandra to fulfill a diverse workload that seems to combine every consistency and availability tradeoff imaginable. It is a fantastically versatile datastore, and a great complement for our developers to our MySQL and Elasticsearch offerings. However, our infrastructure is not done until it ships and is monitored. When we started deploying Cassandra we immediately started looking for ways to properly monitor the datastore so that we could alert developers and operators of issues with their clusters before cluster issues became site issues. Distributed datastores like Cassandra are built to deal with failure, but our monitoring solution...

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If you haven’t heard about PaaSTA before, feel free to check out the blog post introducing it. One step in creating a service is to decide how many compute resources it needs. From the inception of PaaSTA, changing a service’s resource allocation has required manually editing and pushing new configs, and service authors had to pore over graphs and alerts to determine the proper resource allocation for a service whenever load requirements changed. This changed earlier this month when autoscaling was introduced into PaaSTA. Why did we do this? Autoscaling was introduced into PaaSTA to make sure services are allocated...

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It’s 10:00 AM and a designer just created a new icon for Yelp. At 10:05 AM iOS has received a newly versioned CocoaPod containing 1x, 2x, and 3x PNGs, Android has received an AAR Library containing mdpi, hdpi, xhdpi, and xxhdpi WebPs, and the Web CDN has a new SVG and PNG spritesheet, code to manage the PNG fallback for our IE8 users, and a new Python package to access the icon in a template. The workflow wasn’t always this streamlined and was only made possible through a cross-team collaboration with Android, iOS, Web, and Design at Yelp. With a...

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