What are the most common data pipeline architecture patterns?

Data pipeline architecture patterns have evolved significantly, transitioning from the traditional ETL (Extract-Transform-Load) to ELT (Extract-Load-Transform), reflecting changes in data workflows and computing resources. ELT allows raw data to be stored directly in cloud data warehouses like Snowflake, BigQuery, and Redshift, leveraging their elastic compute for transformations, which simplifies version control and testing through tools like dbt. The batch hub-and-spoke architecture remains relevant for organizations with strict compliance needs, although it faces challenges such as latency and scalability issues. Modern cloud platforms often serve as the central hub, supporting diverse use cases and reducing data movement, yet require careful cost management and access control. The emergence of the semantic layer addresses metric inconsistency by offering a centralized definition for business logic, enhancing data governance and decision-making. Streaming architectures cater to low-latency needs through Change Data Capture (CDC) and are essential for real-time applications, albeit with increased complexity. Hybrid and federated patterns combine multiple approaches, offering flexibility but introducing performance and governance challenges. The selection of architecture patterns depends on organizational needs, balancing factors like latency, data volume, and governance, with dbt playing a crucial role in maintaining consistency and scalability across various patterns.

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