Feb 12, 2026 · Big Data

Spark & Flink in Lakehouse: Latency, Accuracy & Cost Trade-offs for Batch-Stream Design

This article analyzes how Spark and Flink should divide responsibilities in lakehouse architectures by evaluating three core dimensions—data latency, computation accuracy, and resource cost—arguing that mature batch-stream integration balances business needs against technical overhead rather than pursuing universal real-time processing.

Batch ProcessingComputation AccuracyData Latency

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Spark & Flink in Lakehouse: Latency, Accuracy & Cost Trade-offs for Batch-Stream Design

JD Cloud Developers

Nov 2, 2020 · Cloud Computing

Why Edge Storage Beats Central Cloud Storage for Real‑Time IoT Data

As IoT and 5G drive massive data generation, edge storage emerges as a flexible solution that reduces latency, lowers bandwidth costs, and enhances data security compared to traditional central cloud storage, especially for video surveillance and other high‑throughput scenarios.

Data LatencyEdge StorageIoT

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Why Edge Storage Beats Central Cloud Storage for Real‑Time IoT Data