Cloud Dataflow和Dataprep有什么区别 [英] What are the differences between Cloud Dataflow and Dataprep

问题描述

Dataprep和Dataflow均可用于ETL任务.实际上，Dataprep似乎使用了Dataflow作业.这是Dataprep提供的使用用户界面编写数据流作业的工具的唯一区别吗?

Both Dataprep and Dataflow can be used for ETL tasks. In fact Dataprep seems to use Dataflow jobs. Is it that the only difference that Dataprep provides tools to write dataflow jobs with a user interface ?

推荐答案

数据流和dataprep都可以确定地转换数据.主要区别在于谁在使用该技术.您的项目是否需要数据用户(例如数据工程师)或业务用户(例如分析师和数据科学家)进行自助数据转换?然后选择dataprep.这不是编码.最终，它将生成数据流作业. Cloud dataprep提供了高级转换，例如，透视，透视，聚合，时间序列，联接，联合，标准化以及数百种通过直观的可视界面公开的其他数据功能.数据需要存储在CDS或BigQuery中.

Both dataflow and dataprep can transform data for sure. The main difference is who is using the technology. Does your project need self-service data transformation by data users such as data engineers or business users such as analysts and data scientists? Then dataprep is the choice. This is no coding. Ultimately it generates dataflow jobs. Cloud dataprep offers advanced transformations such as pivoting, unpivoting, aggregations, time series, joins, unions, standardization, and hundreds of other data functions exposed with an intuitive visual interface. Data needs to be in CDS or BigQuery though.

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