Examples of using Data pipelines in English and their translations into Chinese
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Building real-time streaming data pipelines that reliably get data between systems or applications.
建立实时的流式数据通道，这个通道能可靠的获取到在系统或应用间的数据.Can a machine learning system be distorted by compromising the data pipelines that feed it?
机器学习系统是否可以通过破坏供给它的数据的管道来误导？是可以的。?To collect its growing amount of data, LinkedIn developed many custom data pipelines for streaming and queueing data. .
为了收集日益增长的数据量，LinkedIn开发了许多自定义的数据管道，用于对数据进行流式处理。Under the hood, the framework uses many of the APIs from existing machine learning libraries to maintain high quality data pipelines and learning models.
在底层，该框架使用现有机器学习库中的许多API来维护高质量的数据通道和学习模型。Txt files, it might be hard to understand why there would exist people whose full-time jobs it is to build and maintain data pipelines.
Txt文件中的相对较小的（<5GB）数据集，可能很难理解为什么会有FullTime来构建和维护DataPipeline。This isn't just a different model; these two businesses require completely different data pipelines.
这就不仅仅是一个不同的模型了，这两项业务需要完全不同的数据管道。Apache Kafka: Kafka, named after that famous czech writer, is used for building real-time data pipelines and streaming apps.
ApacheKafka：以着名的捷克作家命名的ApacheKafka用于构建实时数据流管道和流媒体应用程序。Apache Kafka: Kafka, named after that famous czech writer, is used for building real-time data pipelines and streaming apps.
Samza 1.0 comes with a high-level API that enables developers to express complex data pipelines easily by combining multiple operators.
Samza1.0提供了一组高级API，开发人员可以通过组合多个运算符来构建复杂的数据管道。Kafka: Apache Kafka, an open-source platform for building real-time streaming data pipelines, is also experiencing explosive growth, up over 1,200% in five years.
Kafka:ApacheKafka，一个建立实时流数据管道的开源平台，也经历了爆炸性的增长，在五年内增长了超过1200%。This ultimately results in creating and developing tables and data pipelines to support analytical dashboards and other customers(like data scientists, analysts, and other engineers).
这代表着数据工程师的目标是创建并开发表和数据管道，以支持用于分析的仪表盘和其他的数据用户（如数据科学家、分析师和其他工程师）。The general issue with data scientists is that they're not engineers who put things into production, create data pipelines, and expose those AI/ML results.
数据科学家面临的一般问题是，他们不是将工作投入生产、创建数据管线以及公开这些AI/ML结果的工程师。In particular, we will describe how to control data distribution, avoid data skew, and implement application-specific optimizations to build performant and reliable data pipelines.
在此之中，我们会介绍如何控制数据分布，避免数据偏移，并实现对特定应用程序的优化，以构建高性能及可靠的数据管道。Help simplify the data pipeline for greater availability.
帮助简化数据管道以获得更高的可用性。Kafka was created to address the data pipeline problem at LinkedIn.
Kafka是为了解决LinkedIn数据管道问题应运而生的。A data pipeline needs love and attention.
数据管线需要爱和关注。This cluster is kept up to date via the AWS Data Pipeline service.
该群集通过AWSDataPipeline服务保持最新状态。Make the data pipeline serve the partnership and reporting teams.
要使数据管道服务于这种合作伙伴关系和报告团队。The DataOps team will need to be monitoring the real-time data pipeline usage.
数据运维团队需要监控实时数据管线的使用情况。What is Data Pipeline?
什么是DataPipeline?
