05
Building Batch Data Pipelines on Google Cloud
05
Building Batch Data Pipelines on Google Cloud
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Data pipelines typically fall under one of the Extract and Load (EL), Extract, Load and Transform (ELT) or Extract, Transform and Load (ETL) paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud for data transformation including BigQuery, executing Spark on Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Dataflow. Learners get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.
课程信息
目标
- Review different methods of data loading: EL, ELT and ETL and when to use what
- Run Hadoop on Dataproc, leverage Cloud Storage, and optimize Dataproc jobs
- Build your data processing pipelines using Dataflow
- Manage data pipelines with Data Fusion and Cloud Composer
前提条件
Experience with data modeling and ETL (extract, transform, load) activities.
Experience with developing applications by using a common programming language such as Python or Java.
受众
Developers responsible for designing pipelines and architectures for data processing.
支持的语言
English, español (Latinoamérica), 日本語, français, português (Brasil), italiano, and 한국어
学完本课程后，我可以做些什么？
学完本课程后，您可以探索学习路线 中的其他内容或浏览学习目录
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学完一门课程后，您将获得结业徽章。徽章可在个人资料中供查看，还可在社交网络上分享。
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