For decades now, data pipelines have been authored manually, either through hand-coding or interactive visual design. This has resulted in operational brittleness and a disproportionate amount of resources spent on preparing data versus analyzing it.
This begs important questions like :
- Can we use AI to create an autonomous data pipelining experience?
- If we can achieve this, how will autonomous pipelines impact business and engineering teams?
- What do autonomous pipelines mean for what’s possible in greenfield projects?
- How will autonomous data integration scale data engineering productivity?
- What aspects of autonomous data pipelines are being automated?
