The idea of an ideal data pipeline is nice but it is different from the real world. The variance between theory and execution is huge. People generally make suggestions without considering the reality. It is one of the main reasons for the data pipeline mess. Let us start with understanding what a data pipeline is.
What exactly is a data pipeline?
A data processing pipeline is series of connected processed that help in moving data from one point to another. In such an automated data pipeline, data can be transformed during transmission. The mode of transformation can be sequentially linear or parallel execution.
The word pipeline is enough to give us an idea of the difficulty in building and maintaining. Consider these processing pipelines analogous to the real-world pipeline. It may take a lot of time to find out the actual cause of the disruption. The challenge will be to build an opaque data analysis pipeline that makes problem detection easy.
A database pipeline should be built to maintain data quality. Other vital aspects are fitness, lineage, governance, and stability. It must have a contingency mechanism if in case the pipeline breaks down. Next, we look into ten strategies for building data pipelines.
10 Strategies for building and managing a data pipeline –
1. Understand the existing solutions
Before building anything, understand what solutions are existing. Know their data models, their loopholes. Try to anticipate vulnerable aspects of the systems from which data is importing and to which the data is exporting. Take a note of all such observations. Also, make a question list that needs to be answered beforehand.
Implementing a new solution is a good idea. It will be one of the best solutions when it integrates well with existing legacy architecture. Compatibility analysis of the tools may end up saving a lot of time and effort.
2. Build block by block
Build your big data analysis pipeline incrementally. The main reason is to save you from building something which doesn’t serve the purpose. Sometimes, requirements are also unclear until your customer asks for something at the last moment. Imagine the rework you have to do if it’s unsupportable.
3. Document your goals
Goals tend to evolve you start building the solution. It is advisable to create a live document mentioning all the targets. Keep revisiting and updating whenever required. Also, ask others to document their goals as well. We tend to presume that others are thinking the same as we are. So, listing down the objectives will help in bringing your team on the same page.
4. Build to optimize costs
The actual cost will be higher than the estimated costs. If something grows exponentially, remove it from the paid platform and include it in the data ingestion pipeline. In this way, it will minimize the cost of operation.
Another good tip will be to overestimate your costs by 20% while planning the budget.
5. Identify stake and tolerance
Stakes are generally high for a low tolerance system. It requires careful planning. The benefit of dealing with data is decisions taken are easily reversible. It means at any point decision can be changed. Understanding what is at stake and how tolerant is system helps us in understanding and guessing the breaking point of the system
6. Create functional working groups
Rather than working in the waterfall model of completing a task, working in agile like methodology will help you manifold efficiency. Create a small team that helps in achieving one functional unit of the machine learning data pipeline. The team will comprise team members of various expertise areas.
This approach gives data engineers room to bring the feasibility of a solution to the discussion table.
7. Implementing observability tools in the data pipeline
Observability tools help you to understand what’s happening in the data pipeline. Observability tools encompass: monitoring, alerting, tracking, making comparisons, analyzing, recommending the next best actions. They diagnose the root cause of your processing pipelines issues.
8. Create a decision tree to decide the usage of a new tool
Use a decision tree to decide whether you should add another tool or adjust the existing one on your data science pipeline. One should refrain from adding a new tool until it is necessary as the addition of a new tool will incur additional costs and efforts in maintaining.
9. Check your solution from four dimensions of data quality
Check if your data analysis pipeline stands solid on aspects of fitness, lineage, governance, and stability. A good solution will be well balanced on these aspects.
10. Document the work done
Make a habit of documenting the work done with evolution made to the system. Creating a log file helps you in detecting the probable cause of issues. It will add durability to your visualization pipeline.
A well-planned data pipeline will save efforts, time, and money. So, pick out the best-suited solution and evaluate it from 4 aspects of data quality. Comment your thoughts on the strategies mentioned above.