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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- 5 stars65.26%
- 4 stars25.99%
- 3 stars6.29%
- 2 stars1.57%
- 1 star0.88%
BUILDING BATCH DATA PIPELINES ON GOOGLE CLOUD의 최상위 리뷰
There were too many labs with services that take 30-40 minutes just to spin up. I wouldn't have a problem with all the labs if the services took 2-5 minutes to spin up.
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.
very good as a start, needs more practical on some topics like the last ones, and I had a bug with composer lab, but the over all is fine.
Excellent course with appropriate explanation on cloud data fusion, data composer, data proc and cloud data-flow. Must learn course for all aspiring Big Data Engineers.
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