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.
About this Course
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- 5 stars65.05%
- 4 stars26.19%
- 3 stars6.24%
- 2 stars1.60%
- 1 star0.90%
TOP REVIEWS FROM BUILDING BATCH DATA PIPELINES ON GOOGLE CLOUD
The pipeline building portion assumes in part that the learner has previous experience with programming. Further break down of the Python pipeline builds would be helpful.
Interesting topics, but some of the labs are a waste of time (1 minute of hands-on experience, 30 minutes of provisioning resources and pipeline execution).
A great course to help understand the various wonderful options Google Cloud has to offer to move on-premise Hadoop workload to Google Cloud Platform to leverage scalability of clusters.
Good introduction to pipelines building in GCP, Starting labs need to be in more detail. Other than that very good course.
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