The world's largest producer of casual games has selected MySQL Cluster to power its real-time recommendations platform.
High velocity data ingestion, low latency reads, on-line scaling and the operational simplicity delivered by MySQL Cluster has enabled Big Fish to increase customer engagement and deliver targeted marketing, providing a more personalized experience to its users.
The global video gaming market is experiencing explosive growth. Competition is intense, and so to differentiate services and engage users, progressive gaming companies such as Big Fish are seeking solutions to more fully personalize the customer experience.
Using Business Intelligence (BI) and predictive analytics Big Fish can segment customers based on a range of demographic and behavioural indicators. This enables Big Fish to serve highly targeted recommendations and marketing, precisely personalized to a user's individual preferences.
Big Fish's Marketing Management Service platform, powered by MySQL Cluster, is used across all of the company's customer management systems, including customer support and the company's "Game Manager", to provide a unique customer experience to each of its millions of monthly users whenever they come in contact with Big Fish.
Big Fish already has an extensive deployment of MySQL databases powering web applications, including the storefront. They knew MySQL could power the recommendations database, but would require additional engineering efforts to implement database sharding to support data ingest and future scaling needs, coupled with a Memcached layer for low-latency reads.
As a result, they began evaluations of MySQL Cluster, in addition to other database technologies. Using MySQL Cluster, the Engineering teams were able to leverage their existing MySQL skills, enabling them to reduce operational complexity when compared to introducing a new database to the Big Fish environment.
At the same time, they knew MySQL Cluster, backed by Oracle, provided the long-term investment protection they needed for the MMS recommendations platform.
Through their evaluation, the Big Fish engineering team identified MySQL Cluster was best able to meet their technical requirements, based on:
Write performance to support high velocity data ingest
Low latency access with in-memory tables
On-line scalability, adding nodes to a running cluster
Continuous availability with its shared-nothing architecture
SQL and NoSQL APIs to the cluster supporting both fast data loading and complex queries
As illustrated in the figure below: