Data pipelines typically fall under one of the Extra-Load, Extract-Load-Transform or Extract-Transform-Load 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 will get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.
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이 강좌에 대하여
배울 내용
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
Use Dataflow to build your data processing pipelines
Manage data pipelines with Data Fusion and Cloud Composer
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Google 클라우드
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강의 계획표 - 이 강좌에서 배울 내용
Introduction
In this module, we introduce the course and agenda
Introduction to Building Batch Data Pipelines
This module reviews different methods of data loading: EL, ELT and ETL and when to use what
Executing Spark on Dataproc
This module shows how to run Hadoop on Dataproc, how to leverage Cloud Storage, and how to optimize your Dataproc jobs.
Serverless Data Processing with Dataflow
This module covers using Dataflow to build your data processing pipelines
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- 5 stars64.67%
- 4 stars26.57%
- 3 stars6.29%
- 2 stars1.59%
- 1 star0.86%
BUILDING BATCH DATA PIPELINES ON GCP의 최상위 리뷰
It would be great if there can be a walkthrough for the lab session to check if the answers for interpreting the code are correct.
Some parts of the course where not explained in full detail, especially some qwuick labs where questions were not tested or even provided with answers
Thank you very much the team. Course content and materials are at the higher appreciation level. really enjoyed and satisfied.
Good course covering Dataproc, Dataflow, Dataprep and the labs ofcourse.. great way to get introduced to batch data pipelines in GCP.
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