Course Overview
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.
Who should attend
This course is intended for developers who are responsible for designing pipelines and architectures for data processing.
Prerequisites
- Experience with data modeling and ETL (extract, transform, load) activities.
- Experience with developing applications by using a common programming language such as Python or Java.
Course Objectives
- Review different methods of data loading: EL, ELT and ETL and when to use what.
- Run Hadoop on Dataproc, use Cloud Storage, and optimize Dataproc jobs.
- Build your data processing pipelines by using Dataflow.
- Manage data pipelines with Data Fusion and Cloud Composer
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