Amazon Web Services > Case Studies > Leveraging AWS for Enhanced Data Processing: A GumGum Case Study

Leveraging AWS for Enhanced Data Processing: A GumGum Case Study

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Applicable Industries
  • Oil & Gas
Applicable Functions
  • Sales & Marketing
Use Cases
  • Inventory Management
About The Customer
GumGum is a renowned company known for creating the in-image advertising category. The company offers products based on its patented image recognition technology, delivering programmatic, native, and social analytics software solutions. GumGum reaches 400 million visitors as they view images and content across more than 2,000 premium publishers. The company's customers' ads benefit from 20 percent higher viewability and 10 times the engagement of traditional display options. This results in superior brand lift for marketers and increased revenue for publishers. GumGum works with more than half of AdAge’s top 100 U.S. advertising spenders, including Disney, L’Oreal, Toyota, and Samsung. The company is headquartered in Santa Monica, California, with six additional offices in the United States and the United Kingdom.
The Challenge
GumGum, a leading provider of programmatic, native, and social analytics software solutions, was facing a significant challenge in managing its data. The company generates more than 1 billion events, equivalent to approximately 6 TB of data, every single day. This massive amount of data needed to be processed continuously to eliminate bottlenecks and expedite decision-making for customers. The company was in dire need of a solution that could scale quickly to handle the rapidly growing traffic and data. The challenge was not just about managing the volume of data but also about processing it in real-time to provide valuable insights to customers.
The Solution
GumGum turned to Amazon Web Services (AWS) to address its data management challenges. The company now runs ad servers that generate event data, which is written to logs and uploaded to Amazon Simple Storage Solution (Amazon S3) on an hourly basis. The output is then stored in Amazon Redshift. To manage the workflow for production, testing, and development, GumGum uses Amazon Data Pipeline services. For inventory forecasting and hourly data processing, the company uses Amazon Elastic MapReduce (Amazon EMR) to run Apache Spark and Hadoop respectively. Additionally, GumGum provides its users with a proprietary dashboard that displays data from the reporting server, ensuring that customers have access to real-time insights.
Operational Impact
  • The implementation of AWS solutions has significantly improved GumGum's operational efficiency. The company can now process about 6 TB of data nonstop every day, ensuring that there are no bottlenecks and that decision-making is expedited. The scalability of the AWS solutions means that GumGum can quickly adjust to accommodate peak data periods, ensuring that the company's services are always available when customers need them. Furthermore, the cost-effectiveness of AWS allows GumGum to expand its services as new customers sign on, ensuring that the company can continue to grow without being hindered by data management challenges.
Quantitative Benefit
  • Processes about 6 TB of data nonstop every day with AWS Data Pipeline.
  • Can quickly adjust to accommodate peak data periods.
  • Company can cost-effectively expand its services as new customers sign on.

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