Can I use this for data quality testing?

Absolutely, the skill includes standardized assertion patterns to automatically check for uniqueness, null values, and specific data integrity constraints within your SQLX workflows.

How does it handle data normalization across different platforms?

It provides mandatory normalization patterns, such as UPPER(TRIM(id)), to ensure seamless joins between platforms with different casing standards, like GA4 and Google Ads.

Does this skill support incremental table logic?

Yes, it includes detailed patterns for basic incremental tables as well as complex rolling window logic to optimize processing costs and handle point-in-time state preservation.

What is the main benefit of using these SQLX patterns?

These patterns ensure consistency, reliability, and performance in BigQuery data pipelines by providing battle-tested configurations for Dataform and standardized SQL logic.

BigQuery & Dataform SQLX Patterns

Name: BigQuery & Dataform SQLX Patterns
Author: Expanly

byExpanly

0•

数据库管理

Standardizes BigQuery SQL and Dataform SQLX development with optimized patterns for data transformations, incremental modeling, and architectural best practices.

This skill provides a comprehensive set of coding conventions and implementation patterns specifically designed for BigQuery SQL and Dataform SQLX environments. It focuses on building robust, production-grade data pipelines through a structured layer architecture—spanning raw sources, intermediate snapshots, and reporting outputs. By enforcing safe data operations, standardized CTE structures, and precise incremental table logic, the skill ensures that data engineering workflows remain performant, maintainable, and free from common pitfalls like join mismatches or calculation errors in scoring models.

主要功能

01Advanced SQL logic for deduplication, conditional ranking, and complex FULL OUTER joins.

020 GitHub stars

03Mandatory safe operation patterns including SAFE_DIVIDE, COALESCE, and SAFE_CAST.

04Standardized SQLX configurations for views, tables, and protected incremental snapshots.

05Multi-layer data architecture guidelines for Sources, Intermediate, and Reporting layers.

06Built-in testing patterns for data quality assertions and uniqueness checks.

使用场景

01Building scalable and cost-effective data warehouses using BigQuery and Dataform.

02Implementing robust incremental data processing for high-volume analytics snapshots.

03Standardizing SQL codebases and naming conventions across enterprise data engineering teams.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add expanly/expanly-claude-code-agents sqlx-patterns

For use in Claude.ai and ChatGPT

Download Skill