Tag: Streaming
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Empirical: batch vs streaming stores
This empirical post explores the modern trade-offs between batch and streaming data stores. Using benchmarks from real-world systems like Spark, Flink, and Pinot, it examines performance, cost, and operational complexity in 2025. Learn how unified architectures and hybrid designs are shaping the next generation of data processing systems.
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Introduction to streaming data architecture
Streaming data architecture is the backbone of modern real-time systems, powering everything from recommendation engines to IoT telemetry and financial analytics. This post introduces the core concepts, patterns, and tools behind streaming architectures, with practical insights on how to design scalable, fault-tolerant pipelines for real-world applications.
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Empirical: throughput comparison of streaming architectures
This empirical analysis benchmarks the throughput of modern streaming architectures, comparing Apache Kafka, Apache Pulsar, Redpanda, and Flink-based pipelines. Using standardized workloads and realistic latency constraints, we dissect their design trade-offs, operational costs, and observed performance under varied load conditions.