Orchestrate BigQuery Workloads with Dataform
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Learning Track
Data Engineering and Analytics
Delivery methods
On-Site, Virtual
Duration
1 day
Dataform is a service for data analysts to develop, test, version control, and schedule complex SQL workflows for data transformation in BigQuery. In this course, you will explore the components of Dataform core, learn how to define tables and dependencies in SQLX, document BigQuery tables and views, understand BigQuery security settings and how to manage these with Dataform, write assertions, execute SQL workflows, and explore additional advanced use cases.
Course objectives
- Understand the components of Dataform core.
- Create tables and views in BigQuery using Dataform.
- Document BigQuery tables and views.
- Understand BigQuery security settings using Dataform.
- Use assertions to validate data in Dataform workflows.
- Execute Dataform SQL workflows in an automated fashion.
Audience
- Any data analyst, data engineer, or other data professional who wishes to use Dataform to orchestrate data workloads in BigQuery.
Prerequisites
Knowledge of SQL data analysis and BigQuery as discussed in BigQuery for Data Analysis.
Course outline
- SQL workflow
- Repositories and workspaces
- Default files and folders
- Compiled graphs
- Declare a data source
- Create a table
- Create an incremental table
- Set partitioning and clustering options
- Create an empty table
- Create an external BigLake table
- Create views and materialized views
- Define dependencies.
- Use column descriptions
- Use globally defined JavaScript constants
- Add labels
- Lab: Build SQL Workflows with Dependencies in Dataform
- IAM dataset and table/view access
- Column-level security
- Row-level security
- Use built-in assertions
- Create manual assertions
- Lab: Work with Assertions and BigQuery Security Settings in Dataform
- Dataform code lifecycle
- What happens during compilation
- Customize and schedule compilation results
- Execute workflows (UI, Cloud Scheduler, Cloud Composer)
- Logging and monitoring
- Lab: Automate and Monitor SQL Workflow Executions in Dataform
- Create a BigLake table after file upload using Cloud Run functions
- Build a Machine Learning pipeline with BigQuery ML
- Work with Slowly Changing Dimensions Type 2
- Lab: Create a BigLake Table with Dataform Using Cloud Run Functions
