InfoQ Homepage Streaming Content on InfoQ
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Streaming from Apache Iceberg - Building Low-Latency and Cost-Effective Data Pipelines
Steven Wu discusses the design of the Flink Iceberg, comparing the Kafka and Iceberg sources for streaming and how the Iceberg streaming source can power many common stream processing use cases.
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From Zero to a Hundred Billion: Building Scalable Real-Time Event Processing at DoorDash
Allen Wang discusses the design of the event system including major components of event producing, event processing with Flink and streaming SQL, event format and schema validation.
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Resilient Real-Time Data Streaming across the Edge and Hybrid Cloud
Kai Waehner explores different architectures and their trade-offs for transactional and analytical workloads. Real-world examples include financial services, retail, and the automotive industry.
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Building & Operating High-Fidelity Data Streams
Sid Anand discusses building high-fidelity nearline data streams as a service within a lean team.
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From Batch to Streams: Building Value from Data In-Motion
Ricardo Ferreira discusses the risks of designing siloed-based systems and how streaming data architectures can become a solution to address competitiveness.
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Everything You Wanted to Know about Apache Kafka But Were Too Afraid to Ask!
Ricardo Ferreira explains what a streaming platform such as Apache Kafka is and some of the use cases and design patterns around its use.
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Reactive Event Processing with Apache Geode
Bill Burcham discusses how to integrate Geode with your Reactive System efficiently, and at scale.
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Databases and Stream Processing: a Future of Consolidation
Ben Stopford digs into why both stream processors and databases are necessary from a technical standpoint but also by exploring industry trends that make consolidation in the future far more likely.
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Taming Large State: Lessons from Building Stream Processing
Sonali Sharma and Shriya Arora describe how Netflix solved a complex join of two high-volume event streams using Flink.
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Building a Data Exchange with Spring Cloud Data Flow
Channing Jackson presents a case study in the distillation of the finite patterns on each side of the data exchange and a discussion of the patterns used.
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Machine Learning through Streaming at Lyft
Sherin Thomas talks about the challenges of building and scaling a fully managed, self-service platform for stream processing using Flink, best practices, and common pitfalls.
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Real-Time Data Streaming with Azure Stream Analytics
Alexander Slotte introduces Azure Stream Analytics, its ecosystem, and real world examples streaming Twitter feeds as well as sensor data from Raspberry Pi.
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