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High-Performance Data Processing with Spring Cloud Data Flow and Geode

High-performance data pipelines and in-flight data processing have become the cornerstone of most enterprise data platforms. Pipeline development uniquely combines experienced software development with architecture assessment, identifying potential risks and bottlenecks. More often than not, enterprises are trying to manipulate traditional ETL processes into a streaming architecture. How can we effectively accommodate the processing of millions and millions of events, without having to worry about something like Kafka retention time? How do we squeeze a batch application into a streaming data pipeline? What if your business requirements require you to poll from a legacy API with extremely high latency? Apache Geode (also known as Pivotal GemFire) and Pivotal Cloud Cache presents an extremely unique opportunity to solve these problems. Low latency, event driven, and extremely scalable are but a few reasons Enfuse.io developers choose Geode over common competing products.