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Netflix shares how Amazon EC2 Auto Scaling allows its infrastructure to automatically adapt to changing traffic patterns in order to keep its audience entertained and its costs on target. Netflix runs dozens of stateful services on AWS under strict sub-millisecond tail-latency requirements, which brings unique challenges. Wednesday?—?December
Instead, in the style of nanoarrow , our Fast Data library only relies on the stable Arrow C data interface , producing a hermetically sealed library with no external dependencies. In other cases, it is more convenient to share the results via a low-latency API. A key challenge in creating a knowledge graph is entity resolution.
Netflix shares how Amazon EC2 Auto Scaling allows its infrastructure to automatically adapt to changing traffic patterns in order to keep its audience entertained and its costs on target. Netflix runs dozens of stateful services on AWS under strict sub-millisecond tail-latency requirements, which brings unique challenges. Wednesday?—?December
Netflix shares how Amazon EC2 Auto Scaling allows its infrastructure to automatically adapt to changing traffic patterns in order to keep its audience entertained and its costs on target. Netflix runs dozens of stateful services on AWS under strict sub-millisecond tail-latency requirements, which brings unique challenges. Wednesday?—?December
Batch processing data may provide a similar impact and take significantly less time. Its easier to develop and maintain, and tends to be more familiar for analytics engineers, data scientists, and dataengineers. Align on Performance Expectations A major challenge during development was managing API latency.
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