GERD847
发表于 2025-3-21 17:26:12
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以烟熏消毒
发表于 2025-3-21 20:48:59
Zubair NabiHighlights the differences between traditional stream processing and the Spark Streaming micro-batch model.Targets real-world applications from multiple industry verticals.Provides an introduction to
情感
发表于 2025-3-22 02:21:47
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Herpetologist
发表于 2025-3-22 05:08:44
DStreams: Real-Time RDDs,ng will define the future of real-time analytics. There is also a growing need to analyze both data at rest and data in motion to drive applications, which makes systems like Spark—which can do both—all the more attractive and powerful. It’s a system for all Big Data seasons.
Neuropeptides
发表于 2025-3-22 12:19:05
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AIL
发表于 2025-3-22 16:11:01
Real-Time ETL and Analytics Magic,solution such as Hive, but this is a marriage of convenience rather than a natural fit: data must be copied back and forth, not to mention the burden of maintaining two different APIs. A better solution is Spark SQL.
别名
发表于 2025-3-22 19:15:10
Of Clouds, Lambdas, and Pythons,c), Databricks, and IBM (Bluemix). No book about Spark would be complete without a discussion of running it in the cloud. Other topics covered in this chapter include the Spark Python API, the lambda architecture, and graph processing.
思考
发表于 2025-3-22 22:14:29
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beta-carotene
发表于 2025-3-23 04:24:10
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limber
发表于 2025-3-23 07:51:09
Real-Time Route 66: Linking External Data Sources,cation—can substantially affect performance. With this in mind, this chapter is dedicated to ingesting data from solutions such as Kafka, Flume, and MQTT. In the process, you also write your own connector for HTTP to learn the ropes of connecting to external data sources.