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.
Optimizing Dataproc Templates and Autoscaling
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Reseñas
4.5 (1,597 calificaciones)
- 5 stars65,30Â %
- 4 stars25,92Â %
- 3 stars6,32Â %
- 2 stars1,56Â %
- 1 star0,87Â %
AS
19 de may. de 2020
Informative on various features. But cloud fusion and dataflow are not very clearly explained in detail.. expecting more on this. Want to learn more on the pipeline topic please.
SV
18 de jun. de 2020
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.
De la lección
Executing Spark on Dataproc
This module shows how to run Hadoop on Dataproc, how to leverage Cloud Storage, and how to optimize your Dataproc jobs.
Impartido por:
Google Cloud Training
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