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.
- 5 stars65.24%
- 4 stars26.04%
- 3 stars6.22%
- 2 stars1.58%
- 1 star0.88%
來自BUILDING BATCH DATA PIPELINES ON GOOGLE CLOUD的熱門評論
takes time understand , video makes little bore but in practice to enjoy doing but try to mention required time for excuetion or waiting time to task to executeto ece
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.
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.