data pipelines are systems that pull in data points from different sources and consolidate them into a streamlined repository for analysis and modeling. Your data pipeline could be handling thousands of data points from multiple sources, depending on your company's scope. Beyond that, the data itself may be of varying types (qualitative vs. quantitative, for example), and analytics dependent on that data is at the mercy of the quality of your pipeline.
A data pipeline is a predominantly automated process, and running jobs through it involves an intricate series of extraction, preparation, and analysis. If you decide to make your pipeline, accounting for each step of data consolidation is the key to success and continued efficiency.
Each situation is different, but a common method used by companies to create their pipeline is to script the system themselves. While this procedure offers more control over the flow of data, it comes with specific challenges, namely:
When deciding whether or not to create an in-house data pipeline, it is essential to consider these drawbacks. Don’t get us wrong, there are numerous benefits towards having more control over where your data is flowing, and how you present it. But executing this process efficiently can be a challenge.
Besides the fact that data is continuously being collected and consolidated, maintaining a pipeline provides unforeseen drawbacks that can affect the speed of your operations. By examining industry leaders, we’ve found that the most common issues in pipeline development come from the problems in practicality.
Designing a pipeline is not a one-shot deal. You will always be expanding your pipeline as your enterprise's scope grows, which is a natural part of owning a business that relies heavily on analytics. Constantly integrating numerous REST APIs can end up costing money in the time needed to troubleshoot and test; personnel to consistently adjust the network also accrue added costs.
When expanding your business, it is vital to keep the data you collect clean for auditing purposes. Reducing noise in the data collection process is only part of the solution; the other part is designing a pipeline that is able to adapt to handling newly-introduced endpoints in the system. The reproducibility of your data is critical, but this is only possible by maintaining a flexible data pipeline. Say, for instance, you have a collaborator who wants to run post hoc analyses on a data set your team collected, but they want to debug your code; let’s say they want to change variable types. A flexible data pipeline will allow for these instances to happen, but if not done with care, debugging can take hours.
When you call a job with a specific analysis plan in mind, you depend on the data remaining consistent.
