Examples of using Stream processing in English and their translations into Portuguese
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Show stream processing modes in stream lists.
Engineers should not"stream all the things" just because stream processing technology is popular.
While stream processing is a branch of SIMD/MIMD processing, they must not be confused.
With our complex event stream processing platform, you can.
The stream processing paradigm simplifies parallel software and hardware by restricting the parallel computation that can be performed.
Stream processing is also conducted by using Apache Kafka to stream data into Apache Flink or Spark Streaming. .
This is not necessarily the case with stream processing, as the original events can be discarded as they are processed.
Stream processing hardware can use scoreboarding, for example, to launch DMAs at runtime, when dependencies become known.
Transcode: A Linux text-console utility for video stream processing. You need this if you want to rip DVD video.
Event stream processing(esp) is a set of techniques to process streams of data in near real time.
The RV770 GPU also supports an Accelerated Video Transcoding(AVT) feature,which has video transcoding functions being assisted by the GPU, through stream processing.
Stream processing hardware can use scoreboarding, for example, to initiate a direct memory access(DMA) when dependencies become known.
Apache Kafka also works with external stream processing systems such as Apache Apex, Apache Flink, Apache Spark, and Apache Storm.
Additionally, Kafka connects to external systems(for data import/export) via Kafka Connect and provides Kafka Streams, a Java stream processing library.
A new class of applications called distributed stream processing systems(dsps) has emerged to facilitate such large scale real time data analytics.
In this work we describe wathershed-ng, our re-engineering of the watershed system, a framework based on the filter-stream paradigm andoriginally focused on continuous stream processing.
Apache Samza is an open-source near-realtime,asynchronous computational framework for stream processing developed by the Apache Software Foundation in Scala and Java.
For stream processing, Kafka offers the Streams API that allows writing Java applications that consume data from Kafka and write results back to Kafka.
When the speed of data increases to millions of events per second,event stream processing can be used to monitor streams of data, process the data streams and help make more timely decisions.
Stream processing is especially suitable for applications that exhibit three application characteristics:* Compute Intensity, the number of arithmetic operations per I/O or global memory reference.
There were also technical wins for implementing stream processing, such as the ability to save on storage costs, and integration with other real-time systems.
StreamSQL on Spark(Video) This presentation will show Intel's implementation of StreamSQL by using Spark-streaming and Catalyst modules,which makes SQL users grasp stream processing with ease.
There were clear business wins for using stream processing, including the opportunity to train machine learning algorithms with the latest data.
At QCon New York 2017, Shriya Arora presented"Personalizing Netflix with Streaming Datasets" anddiscussed the trials and tribulations of a recent migration of a Netflix data-processing job from the traditional approach of batch-style ETL to stream processing using Apache Flink.
Arora concluded the talk by stating that although the business andtechnical wins for migrating from batch ETL to stream processing were numerous, there were also many challenges and learning experiences.
Stream processing is a computer programming paradigm, related to SIMD(single instruction, multiple data), that allows some applications to more easily exploit a limited form of parallel processing. .
Customers can also take advantage of other services in the Google Cloud Platform,including data stream processing using Google Cloud Dataflow, Cloud Pub/Sub for messaging and Google Cloud Machine Learning for predictive maintenance modeling.
Engineers adopting stream processing should be prepared to pay a pioneer tax, as most conventional ETL is batch and training machine-learning models on streaming data is relatively new ground.
This approach to architecture attempts to balance latency, throughput, and fault-tolerance by creating a batch layer that provides a comprehensive and accurate"correct" view of batch data,while simultaneously implementing a speed layer for real-time stream processing to provide potentially incomplete, but timely, views of online data.