Flink, Spark, and Storm are three popular streaming platforms compared on performance. A benchmark was created to simulate an advertising analytics pipeline with events streamed to Kafka. Flink and Storm had similar linear latency increases with throughput. Spark had higher latency due to micro-batching, but could handle higher throughput. At very high throughput, Storm performed best with acknowl
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The Evolution of Massive-Scale Data Processing Tyler Akidau Staff Software Engineer @takidau With slides by Frances Perry (@francesjperry) and Kenn Knowles (@kennknowles). Strata + Hadoop World London 2016 1 https://goo.gl/VlVAEp A high-level tour of MapReduce, Apache Hadoop, Google Flume, Apache...
Streaming is necessary to handle IoT data rates and latency but SQL is unquestionably the lingua franca of data. Apache Samza and Apache Storm have new high-level query interfaces based on standard SQL with streaming extensions, both powered by Apache Calcite. Calcite's relational algebra allows query optimization and federation with data-at-rest in databases, memory, or HDFS. A talk given by Juli
Realtime Data Processing at Facebook Guoqiang Jerry Chen, Janet L. Wiener, Shridhar Iyer, Anshul Jaiswal, Ran Lei Nikhil Simha, Wei Wang, Kevin Wilfong, Tim Williamson, and Serhat Yilmaz Facebook, Inc. ABSTRACT Realtime data processing powers many use cases at Face- book, including realtime reporting of the aggregated, anon- ymized voice of Facebook users, analytics for mobile applica- tions, and
With the rise of the Internet of Things (IoT) and low-latency analytics, streaming data becomes ever more important. Surprisingly, one of the most promising approaches for processing streaming data is SQL. In this presentation, Julian Hyde shows how to build streaming SQL analytics that deliver results with low latency, adapt to network changes, and play nicely with BI tools and stored data. He al
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