Success Stories

Making data-driven decisions based on its sales transaction data is crucial for Walmart's successful business operations. Unfortunately, due to its scale of operations, the volume of data Walmart collects is tremendous and ever-growing. Even with today's commercial distributed stores and compute engines, the latencies of analytical queries easily exceeded 10-20 mins, significantly hampering the productivity of its data analysts. To reduce these long query latencies and improve their productivity, the data analysts use VerdictDB on top of their existing data analytics engines without making any modifications. VerdictDB has successfully reduced the latencies of their large-scale analytical queries down to a few seconds. Learn more here.

LOCALLY is a leading company in location data intelligence and real-time consumer engagement. LOCALLY is interested in building/offering a web-based platform where its customers can interact (or play) with its massive amount of location data capturing people's mobile activities. In building such a web platform, the essential part is achieving real-time responses to the customers' interactions. For example, if a customer wants to learn how many people are within a certain (arbitrary) area, the web platform must be able to obtain the number within a few seconds. In this way, the customer can be more engaged with LOCALLY's platform and use more of its premium services. In offering these near real-time responses, today's distributed compute engines (e.g., Amazon Redshift, Facebook Presto) were expensive and still not fast enough. To solve this problem, LOCALLY uses VerdictDB, which can compute the answers to big queries only within a few seconds using a small cluster (thus, affordable).