When should I choose ELT over ETL?

Choose ELT for modern cloud data warehouses where you need flexibility and faster loading. ELT allows you to store raw data first and transform it in-place using the target system's compute power.

Does this skill help with Change Data Capture (CDC)?

Yes, it provides patterns for log-based, trigger-based, and timestamp-based CDC to help you capture source changes without full table scans.

How does this skill assist with data quality?

It includes patterns for validation gates, data contracts, and quality checks (like null and range checks) to ensure only clean data reaches your production tables.

Can I use this for real-time data processing?

Yes, the skill covers streaming pipeline architectures and Kappa/Lambda patterns suitable for real-time analytics and event-driven systems.

ETL & ELT Design Patterns

Name: ETL & ELT Design Patterns
Author: melodic-software

bymelodic-software

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データベース管理

Provides architectural guidance and implementation patterns for designing modern data pipelines and transformation workflows.

概要

This skill equips developers and data engineers with standardized patterns for designing robust data pipelines, helping navigate the choice between ETL and ELT architectures. It covers modern data stack integration, Change Data Capture (CDC) strategies, data quality validation gates, and advanced architectures such as Lambda and Kappa. Whether you are building real-time streaming services or batch processing systems, this skill ensures your data infrastructure is scalable, idempotent, and maintains high integrity through proven engineering best practices.

主な機能

Batch and Streaming architecture (Lambda/Kappa) guides
ETL vs. ELT architectural decision frameworks
Modern Data Stack blueprints including dbt and Snowflake
12 GitHub stars
Implementation patterns for Incremental Loads and CDC
Data quality validation gates and contract patterns

ユースケース

Implementing robust error handling and dead letter queues
Designing a new data warehouse ingestion pipeline
Migrating legacy ETL processes to modern ELT workflows

概要

主な機能

Batch and Streaming architecture (Lambda/Kappa) guides
ETL vs. ELT architectural decision frameworks
Modern Data Stack blueprints including dbt and Snowflake
12 GitHub stars
Implementation patterns for Incremental Loads and CDC
Data quality validation gates and contract patterns

ユースケース

Implementing robust error handling and dead letter queues
Designing a new data warehouse ingestion pipeline
Migrating legacy ETL processes to modern ELT workflows