Global retailers spend months preparing for the peak holiday shopping season. When your tech landscape includes numerous batch and ETL processes supported by an amalgam of legacy messaging infrastructure, point-to-point integrations, and homegrown solutions, preparing for — and making it through — peak season is exponentially more difficult. At Gap Inc., we faced just this scenario. Our data flows depended on a dozen different technologies, including IBM MQ and RabbitMQ as well as ESB, ETL, and in-house integration tools. We recently consolidated and simplified around Kafka with Confluent, enabling us to support real-time data flows for every sale across 2,500 stores while phasing out legacy technologies and batch processes that caused lengthy delays in data availability. In this session, you’ll learn how to simplify your landscape, standardize on the AVRO format and Schema Registry, begin providing topic access to third-party vendors, and pave the way for a move to the cloud.