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.
Informações sobre o curso
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- 5 stars64,84%
- 4 stars26,31%
- 3 stars6,30%
- 2 stars1,62%
- 1 star0,90%
Principais avaliações do BUILDING BATCH DATA PIPELINES ON GOOGLE CLOUD
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 course covering Dataproc, Dataflow, Dataprep and the labs ofcourse..
great way to get introduced to batch data pipelines in GCP.
This course really teaches me in-depth about data engineering than the cloud or any other products offered by GCP which is the most important part.
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