Data pipelines typically fall under one of the Extract and Load (EL), Extract, Load and Transform (ELT) or Extract, Transform and Load (ETL) 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 get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.
Overview
Syllabus
- Introduction
- 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.
- Course Summary
- Course Summary
Taught by
Google Cloud Training
