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.
- 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
- In this module, we introduce the course and agenda
2. Introduction to Building Batch Data Pipelines
- This module reviews different methods of data loading: EL, ELT and ETL and when to use what
3. 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.
4. Serverless Data Processing with Dataflow
- This module covers using Dataflow to build your data processing pipelines.
5. 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.
6. Course Summary
- Course Summary
7. Course Resources
- PDF links to all modules
- Lectures 0
- Quizzes 0
- Duration 1 week
- Skill level All levels
- Language English
- Students 0
- Assessments Yes