How can you design a data pipeline that is easily interpretable and explainable?

From my own journey, I've learned just how crucial it is to be super clear about what our end game is, when building a data pipeline. I remember working on a challenging project where we kicked things off by really drilling down into what we wanted to achieve with our pipeline. It involved engaging in meaningful conversations with stakeholders to get to the heart of the business queries we were aiming to solve. We took a hard look at our data sources, scrutinizing their variety and dependability to make sure they were in line with what we were trying to accomplish. This project was a bit of a jigsaw puzzle – dealing with data in all sorts of shapes and sizes, and nailing down our quality standards from the get-go was the key.

- Use a simple and straightforward pipeline design. - Avoid overly complex algorithms and models. - Use clear naming conventions for data fields and tables. - Document all steps in the pipeline, including the purpose of each step and the data it produces. - Utilize Explainable AI (XAI) techniques: Implement XAI techniques like LIME or SHAP to explain model predictions and understand feature importance.

Data pipelines form the crux of any ETL architecture. Defining clear objectives with respect to the types of data sources, expected output format and a predefined quality requirements can help in designing robust and scalable data pipelines. I believe an efficient data model is also mandatory while designing pipelines because as we move data through various layers from raw to secured(tokenized) to consumption layer, it becomes easier for stakeholders to independently use the data. Also, keeping the expectations of stakeholders is a top priority and working towards achieving them should be the primary goal.

In my experience, one easy tool that can be leveraged to make your pipelines much more explainable is dbt (data build tool). Dbt provides a modular approach to building out models and transforming your data as it captures transformation logic in SQL modular files using CTE statements. When defining these CTE statements within a model, you are also able to refer to sources and other models, creating dependencies and therefore building out a lineage graph to track the logical flow of the data downstream. Dbt also provides documentation within the tool itself, meaning that devs do not need to switch between a documentation and develop environment, as it is all in one and they can do it as they go.

I recall a project where we integrated Apache Airflow for workflow orchestration, which was a revelation. It streamlined our processes, making them more efficient and transparent. Using Apache Spark for data processing significantly boosted our pipeline's speed and performance, handling large datasets with ease.

In my experience it is a good practice to standardise the documentation and implementation of the pipelines and code. Ensuring that it adheres to a predefined standard convention and style guide. In Python, utilising the PEP-8 style guide along with PEP-257 docstring conventions is a great idea. Furthermore, adhering to the Zen of Python (import this) and its core principles. These principles emphasize the importance of code readability, simplicity, and clarity

?? Side of docstring and functions documentation; you need to document packages, tests and architecture templates( by Infrastructure as code )! ?? This knowledge must need available to data members, so you need to choose tools that automatic this part from code and infrastructure as code they build.

Adding data quality checks in a data pipeline is essential, but ensuring that a failure of your checks blocks downstream propagation is even more important. In theory, subsequent tasks in your pipeline DAG (Directed Acyclic Graph) should be dependent on successful quality checks. When a data quality check fails, it is important that there are pointers either within your pipeline code or alerting platform that direct the recipient of the notification to how the failure should be handled; this includes run books, troubleshooting and debugging techniques, and information about historic failures and the steps that were previously taken to achieve resolution.

When we set up live dashboards to keep an eye on how our data pipeline was doing, it really made a huge difference for everyone. There was this one time when we noticed things were slowing down more than usual. Thanks to constantly watching our dashboards, we caught this issue very quickly. We dug into it and found out that a part of our process for changing data was not as efficient as it should be. Because we spotted this early on, we were able to fix it right away. This didn't just solve the immediate problem, but it actually made the whole system work better and faster in the long run. But it wasn't just about identifying the issue. Our system had built-in pointers that directed us on how to handle this.

?? Show sample of generated results for each step inside data pipeline can help to evaluate and understand how data is transforming! This point help both data team and business team to validate data pipeline. Make sure that the data have a relationship with each other for tracking purposes.

Clear Documentation and Visualisation: Maintaining comprehensive documentation and visualization tools for data flow can make a pipeline more interpretable. Visual representations of data pipelines help stakeholders understand the process flow easily. Modular Design and Consistent Naming Conventions: Designing the pipeline in modular segments with consistent naming conventions enhances its readability and maintenance. This approach simplifies understanding each component's function and interaction with others. Implement Logging and Monitoring: Incorporating thorough logging and monitoring within the pipeline aids in tracking data flow and operations, making it easier to explain how data is processed and transformed at each stage.