Data systems > Data pipelines
The Problem with Traditional Data Pipelines
Data Pipelines play a critical role in every aspect of the Data Engineering life cycle; ensuring that data flows smoothly from source to destination; maintaining states at each stage; and math the software platforms that follow microservice architecture, these pipelines increase rapidly, resulting in a complex web of data flows and schemas that can become difficult for reliability, maintainability, and operability.
At the end of the day to engineering life cycle, we see the data has to be used by consumers for analytics, machine, learning, business, intelligence data, science, project, analytics, etc. How many times is this a single consumer data pipelines are made from a single source to a single consumer once you start adding multiple consumers you are no longer in just a single pipeline situation. You’re building a whole system of sewage management for your company. So why are we still building pipelines when we should be building systems for multiple consumers to do many things with our data?
