logentries.com Case Study Yummly Yummly uses Logentries to empower the data team to resolve issues and identify insights that would normally require a developer. Use Case: Production Data Flow Monitoring and Diagnostics Industry: Recipe Search and Recommendation About Yummly Often referred to as the Pandora of recipes, Yummly is a free smartphone app and website that provides recipe recommendations personalized to the individual’s tastes, semantic recipe search, a digital recipe box, and connects to Instacart for one-hour grocery delivery. Today, all versions of the Yummly app are ranked first among recipe apps and have reinforced Yummly’s leadership position in food-related technology. The Yummly app for the iPhone was selected for inclusion in the App Store Best of 2014, which celebrates the year’s most innovative and entertaining apps and games. As of December 2014, Yummly’s mobile apps have been installed over eight million times worldwide. I used to settle for knowing there were certain insights I simply wouldn’t uncover because I couldn’t interrupt our dev team. Logentries now enables me to identify problems and patterns I couldn’t see before. - Sean McGlinchey, Chief Data Officer, Yummly ? Challenges With more than 15 million monthly unique visitors exploring and adding recipes to Yummly each month, Yummly’s head of data, Sean McGlinchey, is responsible for ensuring recipe and ingredient data is captured consistently and accurately. “We apply proprietary technology to understand food and make recommendations built upon mapping the food genome,” explains Sean. Doing so requires Sean and his team to understand everything about a recipe and the properties of everything in a recipe: nutrition, diet, allergy, flavor and taste, techniques, prep and cooking time and more. logentries.com With thousands of recipes being yummed by users every day across the web, it’s critical that the Yummly team is able to identify new ingredients and patterns in recipes to continue to grow and improve the food genome. With Logentries, Yummly found a solution that enabled non-development staff to monitor and configure alerts for the recipe datastream to ensure continued recipe digestion, pipeline up-time, and new ingredient identification. Before Logentries, I was performing a lot of ad hoc database queries or spending development time to query logs. We needed to find a better way. Solutions After evaluating several options, the Yummly team chose Logentries to monitor their recipe ingestion pipeline. At last, the Yummly data team could identify things like ingredient validation errors and pipeline downtime without tying up precious development resources. Custom tagging through Logentries enabled Sean to easily spot errors as they occurred. Identifying data sources using incompatible recipe and ingredient formats became automatic, enabling Yummly to scale with less friction as developers spent more of their time developing new features. The most valuable aspect of Logentries for me is the ability to see what’s going on and quickly identify problems without a developer.