Data Pipelines
Data pipelines are essential to modern applications architecture, with common applications requiring up-to-date data, such as fraud detection, real-time personalization, real-time analytics, ETL (Extract, Transform, Load) pipelines, machine learning, and Internet of Things (IoT). Apache Pulsar and StreamNative provide an ideal enterprise-grade foundation for building these pipelines.
StreamNative includes real-time stream processing capabilities with the enhanced Pulsar functions, but can also be integrated with other stream processing frameworks such as Apache Flink and Apache Spark.
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High-throughput workloads are handled with consistent low latencies, making it suitable for real-time data processing.
Multi-language support: Pulsar provides clients for multiple languages, including Java, Python, and Go.
All messages are stored in a durable log, which means that messages are persisted to disk and are not lost if a machine fails.
StreamNative supports multiple tenants and namespaces, which allows you to use the same cluster for multiple applications or teams.
Dynamic horizontal scaling makes it easy to handle large amounts of data and high levels of concurrency.
Enhanced Pulsar functions provide real-time data streaming processing capabilities.
