A Data Mesh approach works with real-time streaming data by treating streams as domain-owned data products. Each domain is responsible for producing, processing, and serving its own streams with defined schemas, quality standards, and SLAs. Domains can consume each other’s streams without central coordination, enabling decentralized, real-time analytics and insights.
Real-time processing is supported by modern streaming platforms like Kafka, Kinesis, or Pulsar, and stream processing frameworks such as Flink or Spark Streaming. Data engineering services are essential in implementing a Data Mesh for real-time data, as they help design and maintain streaming pipelines, manage schemas, enforce data quality, and provide the infrastructure and support domain teams need. By leveraging data engineering services, organizations can ensure governance, observability, and interoperability while scaling streaming data operations efficiently.
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spiralmantra07
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