Comprehensive and Detailed In-Depth

The solution must support SQL-based ELT, be serverless and cost-effective, and include advanced

features like version control and quality checks. Let’s dive in:

Option A: Cloud Data Fusion is a visual ETL tool, not SQL-centric (uses plugins), and isn’t fully

serverless (requires instance management). It lacks native source code control and parameterization.

Option B: Dataform is a serverless, SQL-based ELT platform for BigQuery. It uses SQLX scripts,

integrates with Git for version control, supports environment variables (parameterization), and offers

assertions for data quality—all meeting the requirements cost-effectively.

Option C: Dataproc is for Spark/MapReduce, not SQL ELT, and requires cluster management,

contradicting serverless and cost goals.

Option D: Cloud Composer orchestrates workflows (Python DAGs), not SQL pipelines natively. It’s

managed but not optimized for ELT within BigQuery alone.

Why B is Best: Dataform leverages your team’s SQL skills, runs in BigQuery (no extra infrastructure),

and provides Git integration (e.g., GitHub), parameterization (e.g., DECLARE env STRING DEFAULT

'prod';), and quality checks (e.g., assert 'no_nulls' AS SELECT COUNT(*) FROM table WHERE col IS

NULL). It’s the perfect fit.

Extract from Google Documentation: From "Dataform Overview"

(https://cloud.google.com/dataform/docs): "Dataform is a fully managed, serverless solution for

building SQL-based ELT pipelines in BigQuery, with built-in Git version control, environment

parameterization, and data quality assertions for robust data warehouse management."

Reference: Google Cloud Documentation - "Dataform" (https://cloud.google.com/dataform).