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Clinch: Serves Advertisers Better with Upsolver
CUSTOMER STORY
INDUSTRY:
Advertising Technology
USE CASE:
Processing streaming data from hundreds of millions of web users
DATA:
1 billion events each day
Serves
more relevant personalized ads
Doubles
the number of features available to clients every month
Avoids
the cost of hiring a team of developers to manage its data platform
Delivering a hyper-personalized product and optimizing campaigns in real time require Clinch to process a high volume of data fast. According to Yaron Cohen, Vice President of Research and Development at Clinch, the company tracks hundreds of millions of anonymous web users, who generate 1 billion events each day. Based on a user’s activity and profile, Clinch models intent and then builds and serves ads that are most appropriate to the user at that moment. Its entire infrastructure runs on Amazon Web Services (AWS) and has done so since the firm was founded in 2012.
At the beginning, Clinch streamed all events into a single NoSQL data store, where its teams could query the raw data or aggregate it into various states to be used by Clinch’s products. As the company grew to its current size, this data store was becoming a bottleneck.
The Goal
Cohen and his colleagues looked at various data-streaming technologies, but no solution was exactly right. Cohen says, “Every technology we tested involved stitching together a lot of code, so each change we made required significant manual work. We figured we’d need a team of developers just to make these changes, perhaps to build an automation layer that supported the entire data pipeline for our product teams.”
Serves
more relevant personalized ads
Doubles
the number of features available to clients every month
Avoids
the cost of hiring a team of developers to manage its data platform
We must constantly make changes to the types of data we track and aggregate to build new products or cater to a new customer’s requirements. This was far from easy. The Solution
Upsolver goes well beyond just moving Clinch’s data from source to target; thanks to its declarative pipelines Upsolver also eliminates a lot of the manual work involved in data transformation. This includes partitioning, merging small files, and converting raw data into useable columnar file formats such as Apache Parquet. In addition to saving time via automation, Clinch avoids the cost and hassle of testing its own data transformations and fixing the inevitable bugs that would result.
Says Cohen, “If we had used a product like Apache Spark, we’d have developers writing Scala code to create transformations, manage clusters, and orchestrate workflows to make sure jobs run efficiently and on time. Upsolver automates the majority of that for us, which saves us money, but also allows us to provide a better service to customers.”
Upsolver is completely self-serve. My team quickly became proficient with the platform, and our first stream was up in less than a day. The Results
Clinch has taken advantage of how much easier it is now to make changes to its data pipeline. In fact, since it began working with Upsolver Clinch has roughly doubled the number of features available to clients every month. “Ad tech is a competitive industry. We compete on speed, but Upsolver also helps us deliver new features that bring better ROI for customers,” says Cohen.
Upsolver’s automations move incoming data into Clinch’s data lake within around 2 minutes; as a result, end users see more relevant ads because the data is fresher. Cohen says, “Now we can perform more optimizations, measure the performance of each algorithm in real time, and adjust it as needed. It has made a real difference in our ability to innovate for our clients.”