?? Integrations Unlocked: ETL Pipelines (Part 2) ??

Building upon our exploration of Layer 1 in the ETL pipeline , we now venture into Layer 2. This stage is pivotal for data normalization, setting the groundwork for efficient and accurate data processing.

Layer 2: Data Normalization – Staying True to Source

Layer 2 in the ETL pipeline is more than just a bridge between raw data and processed information. It is a carefully crafted stage where data is not only normalized but also primed for unique handling and collaborative problem-solving.

Key features for Layer 2

1. Data Normalization and Storage: One of the primary role of Layer 2 in the data ingestion phase is to process and store raw data received from Layer 1. This process is carefully designed to ensure optimal processing while remaining faithful to the source's format, including entities and nomenclature. In data transmission phase, this layer receives our application specific data and packages it in the data format with required parameters that the third-party accepts.This fidelity is crucial for developing unique processors tailored to each integration.
2. Integration-Specific Processors: In a world of varied integrations, API signatures often differ, even when the data types are similar. For instance, menus provided by different restaurant chains to an F&B aggregator. Layer 2 addresses this by writing lean, integration-specific processors. These processors have the responsibility of transitioning data from Layer 1 to Layer 2, accommodating the unique nuances of each data source during data ingestion phase and converting the standard data received from our application into integration specific format.
3. Selective Validation for Integrity, Independent from Internal Structures: Layer 2 applies minimal validations to ensure data integrity during data ingestion stage. This includes checking for data completeness and mandatory fields, crucial for data correlation. Importantly, we avoid internal database-specific or business logic validations like foreign key constraints, as our focus is on mirroring the source data. Despite this, references to Layer 1 records are maintained for data linkage.
4. Status tracking: As entries from layer 1 are processed successfully, the corresponding entries in this layer are persisted with reference to the source entry in layer 1 and status as "Awaiting Processing", the status of the source entry in layer 1 is marked as "Processed". Should an error occur, the Layer 1 record is flagged as "Error," ensuring immediate visibility. For bulk or chunk processing, we also implement an intermediate "Processing" status in this layer, offering a granular view of the data's journey and facilitating smoother error handling and workflow management. This meticulous approach to status tracking ensures a transparent, traceable, and efficient processing pipeline.
5. Error Tracking and Resolution: Errors identified during Layer 2 processing are logged back in records of Layer 1. By maintaining source entities and nomenclature, we facilitate clear and understandable views for both our integration data management teams and, if necessary, third-party partners. This transparency makes it easier for external parties to comprehend and address issues, as they see data in familiar terms.This layer has its own error logging capabilities, which as you can guess, will be populate in the next layer.

What are the advantages of this approach?

1. Customization and Flexibility: This layer's data normalization is essential for creating unique processors for each integration, significantly enhancing the customizability and flexibility of our data handling. This ensures that each data source is addressed with a tailored approach, respecting its specificities.
2. Scalability through Asynchronous Processing: The division of data processing into smaller, asynchronous steps greatly benefits scalability. It allows us to efficiently scale our background worker instances based on varying demands, maintaining high performance and predictability under different load conditions.
3. Common Ground for Problem Solving: Maintaining the original data format fosters a common understanding, crucial for resolving data issues efficiently. This commonality is beneficial not only internally but also in collaboration with third-party partners via views written over our data, enhancing the problem-solving process and fostering a cooperative environment.
4. Data Analytics, Dashboards, and Reporting: Integrating the capability for data analytics and dashboard generation that this layer offers brings a crucial advantage. It enables us to generate real-time metrics and reports, providing insights into data trends. This is particularly useful for pinpointing high volumes of issues or concerns in data received from third-party sources, allowing for proactive problem-solving.
5. Historical Data for Development and Debugging: The availability of historical data within Layer 2 is invaluable during development phases. It allows for re-processing and tweaking of logic to improve performance or fix bugs. This historical data is also critical when adapting to API changes from third-party sources, as it provides a comprehensive dataset for testing and validation.

Strategic Recommendation

For optimal isolation and long-term maintenance, it is recommended to encapsulate the integration-specific logic, including models, within an 'integration namespace' in Layers 1 and 2. This encapsulation strategy not only organizes the logic coherently but also simplifies future updates and modifications.

Real-World Application

In my experience managing projects across various verticals around the globe, the layered ETL approach has been instrumental in streamlining our data processes and enhancing team efficiency. This methodology has been particularly effective in delineating responsibilities between integration and business logic development teams, fostering an environment where data-centric discussions with third-party partners are not just possible but productive. Adopting the mantra "In God we trust, but we believe in data," this approach has significantly reduced the hours spent on issue hunting and debugging, consequently shortening our delivery cycles.

The impact of this system, once fully operational, is remarkable. It functions like a well-oiled machine, consistently delivering reliable results with minimal intervention. Given these benefits, I strongly recommend revisiting and reassessing existing systems to incorporate a similar layered ETL strategy. The potential gains in terms of time savings, process optimization, and overall project delivery efficiency are substantial and can be a game-changer in managing complex data landscapes.

As we progress to the next layer in our series, we will explore how these foundations set in Layer 2 lead to more sophisticated data transformation processes.

I encourage you to share your insights on this layer's role in your ETL experiences. Let's continue to deepen our understanding and improve our practices in this ever-evolving field of data integration.