What technologies are used to develop SSPs?
Since SSPs must provide intensive data processing and real-time connection to programmatic pipelines, they rely on advanced technology stacks. The choice of technologies must depend on your project specifics, but it will generally come down to the following elements:
- Programming languages. Go and Node.js provide lightweight ways to facilitate concurrent bid processing. Python can also plug in to incorporate ML models, and Java supports enterprise back-end services as it can handle large volumes of simultaneous transactions.
- Data processing tools. Apache Kafka and RabbitMQ can be applied for message queuing and streaming tasks. Redis is also one of the default technologies to include in your project, as its in-memory data storage helps maintain a cache of data generated from thousands of events.
- Databases. High-performing databases like PostgreSQL, MongoDB, or Cassandra are necessary for storing raw data and fetching real-time analytics.
SSPs’ back-end must also incorporate optimization models to select winning bids in RTB auctions automatically. Publishers can use the following formula to calculate the probability of winning a bid:
Pwin = Bid/(Bid+Floor_Price),
where “Bid” stands for an advertiser’s offer and “Floor_Price” is the starting price set by the media seller.