The Right ETL Architecture for Multi-Source Data Integration

When building ETL (Extract, Transform, Load) pipelines for marketing analytics, customer insights, or similar data-driven use cases, there are two primary architectural approaches: dedicated pipelines per source and common pipeline with integration, core, and sink layers. 

Each has its distinct non-functional trade-offs in terms of maintainability, performance, cost efficiency, and operational visibility.

Let’s explore the best practices and pros and cons of both approaches.

A Common Use Case: Multi-Source Marketing Data Aggregation

Consider a scenario where an organization needs to aggregate marketing data from sources like Google Ads, TikTok Ads, Facebook Ads, and internal customer data sources. The collected data needs to be transformed, analyzed, and stored in different tables or databases for further insights.

Two approaches exist:

1. Dedicated ETL pipelines per source. Each data source has a separate ETL pipeline deployment that independently extracts, transforms, and loads data into the target(s).
2. A common ETL pipeline with an integration layer. A unified pipeline that includes an integration layer (handling ingestion from different sources and filtering), a core processing layer (handling common transformations, deduplication, and business logic), and a sink layer (handling writes to a single or different destination(s) as needed). 

Within this approach, there are two main variants:

• Common core only. The integration layer processes data per source, but the core layer handles transformations centrally before distributing data to destination(s).
• Common core + destination. A fully unified model where transformations and formatting are done in the core layer before data is directed to the appropriate destination(s).

Dedicated Pipelines: Pros and Cons

Pros

• Simplicity. Each pipeline is tailored for a single source, making it easier to understand and troubleshoot.
• Granular optimization. Since each ETL is independent, performance optimizations can be source-specific.
• Less complexity in initial setup. Teams can get started quickly with isolated pipelines, without worrying about commonality between pipelines.

Cons

• High maintenance overhead. More pipelines mean more configurations, monitoring, and operational overhead.
• Scalability challenges. Independent pipelines may require redundant processing logic and infrastructure, leading to inefficient resource utilization and resource duplication.
• Limited cross-source insights. Since each source has its own pipeline, cross-source event correlation (e.g., deduplication, attribution modeling, complex event processing scenarios) becomes challenging.

Common Pipeline With Integration, Core, and Sink Layers: Pros and Cons

Pros

• Cross-pipeline visibility. A common processing layer allows event correlation across different sources, enabling advanced insights such as complex event processing (CEP).
• Better resource utilization. With a shared compute layer, economies of scale are achieved in terms of hardware and license costs.
• Consistency across data sources. Business logic, transformations, and quality checks are centralized, reducing inconsistencies.
• Scalable and maintainable. Instead of maintaining multiple ETL jobs, a single pipeline can be optimized and scaled efficiently.

Cons

• Increased complexity. A shared pipeline requires robust orchestration and error-handling mechanisms with more moving parts.
• Single point of failure risk. If the core layer fails, multiple data sources are affected.
• Higher initial investment. Designing a robust common pipeline with abstraction layers takes more design and development effort upfront.

Common Data Model Considerations

One major challenge of a common ETL pipeline is the need for a common (or canonical) data model (CDM). Since data sources often have different schemas and formats, a CDM must be established to standardize the data before it reaches the core processing layer. This model ensures that transformations are uniform across sources and enables complex event-processing scenarios across sources.

There are different implementations of normalizing to a common data model. These can be schema-based, where the schema is stored and version-controlled in schema registries while being enforced at the entry points, or staging-table-based, where data is written into an intermediary staging table from the ingestion layer, enforcing schema at the database level. The schema-based approach is generally considered best practice as it allows easy versioning, flexibility, and validation at multiple stages of the pipeline. 

However, in real-world implementations, the staging-table-based approach is also used, especially when the ETL pipeline exists within a single data source, such as reverse ELT scenarios where data is transformed after being loaded into the final destination.

Conclusion

In this blog, I talked about the pros and cons of building dedicated versus common pipelines for a single use case (marketing analytics). For organizations dealing with scattered external or internal data sources, the choice depends on scalability needs, maintenance costs, and operational complexity. A common ETL pipeline with an integration layer offers better visibility, scalability, and efficiency but requires upfront investment in orchestration and fault tolerance. On the other hand, dedicated pipelines are quick to deploy but may lead to inefficiencies in the long run.

Additionally, adopting a common data model is crucial when implementing a shared ETL approach, as it ensures data consistency across sources and simplifies processing logic.

Choosing the right ETL approach is not just about tech — it’s about balancing business needs, operational efficiency, and long-term maintainability.