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.
In this module, we introduce the course and agenda
Introduction to Building Batch Data Pipelines
This module reviews different methods of data loading: EL, ELT and ETL and when to use what
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.
Serverless Data Processing with Dataflow
This module covers using Dataflow to build your data processing pipelines
Manage Data Pipelines with Cloud Data Fusion and Cloud Composer
This module shows how to manage data pipelines with Cloud Data Fusion and Cloud Composer.