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
- 5 stars65.22%
- 4 stars26.06%
- 3 stars6.23%
- 2 stars1.58%
- 1 star0.89%
TOP REVIEWS FROM BUILDING BATCH DATA PIPELINES ON GOOGLE CLOUD
Good course covering Dataproc, Dataflow, Dataprep and the labs ofcourse..
great way to get introduced to batch data pipelines in GCP.
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.
There were some minor problem and mistake in the lab file. The python/java scripts were not explained at all. There are questions about the code itself, but then the questions were not answered.
Good contents. Some lab questions do not have answers. Hope provide them to know how I understand the knowledge.
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