In this lab, you a) build a batch ETL pipeline in Apache Beam, which takes raw data from Google Cloud Storage and writes it to Google BigQuery b) run the Apache Beam pipeline on Cloud Dataflow and c) parameterize the execution of the pipeline.
Serverless Data Processing with Dataflow - Writing an ETL pipeline using Apache Beam and Cloud Dataflow (Java)
Google via Google Cloud Skills Boost
Overview
Syllabus
- Overview
- Setup and requirements
- Apache Beam and Cloud Dataflow
- Lab part 1. Writing an ETL pipeline from scratch
- Task 1. Generate synthetic data
- Task 2. Read data from your source
- Task 3. Run your pipeline to verify that it works
- Task 4. Adding in a transformation
- Task 5. Writing to a sink
- Task 6. Run your pipeline
- Lab part 2. Parameterizing basic ETL
- Task 1. Creating a JSON schema file
- Task 2. Writing a JavaScript user-defined function
- Task 3. Running a Dataflow Template
- Task 4. Inspect the Dataflow Template code
- End your lab
Tags
Related Courses
-
Serverless Data Processing with Dataflow - Writing an ETL Pipeline using Apache Beam and Cloud Dataflow (Python)
-
Serverless Data Processing with Dataflow - Testing with Apache Beam (Java)
-
ETL Processing on Google Cloud Using Dataflow and BigQuery
-
Getting Started with Apache Beam
-
Architecting Serverless Big Data Solutions Using Google Dataflow
-
Serverless Data Processing with Dataflow: Develop Pipelines
