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
What you will learn
Review different methods of data loading: EL, ELT and ETL and when to use what
Run Hadoop on Dataproc, leverage Cloud Storage, and optimize Dataproc jobs
Build your data processing pipelines using Dataflow
Manage data pipelines with Data Fusion and Cloud Composer
Syllabus - What you will learn from this course
Introduction to Building Batch Data Pipelines
Executing Spark on Dataproc
Serverless Data Processing with Dataflow
- 5 stars65.26%
- 4 stars25.96%
- 3 stars6.32%
- 2 stars1.56%
- 1 star0.87%
TOP REVIEWS FROM BUILDING BATCH DATA PIPELINES ON GOOGLE CLOUD
Thank you very much the team. Course content and materials are at the higher appreciation level. really enjoyed and satisfied.
Good introduction to pipelines building in GCP, Starting labs need to be in more detail. Other than that very good course.
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, I think pipelines need to have more labs related to some necessities in the industry, such as connect them to other external sources outside GCP
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