“We have now evolved to a full end-to-end agent capability that spans pipeline building, data transformation and pipeline troubleshooting,” Yasmeen Ahmad, product manager of data and AI at Google Cloud, told InfoWorld.
This means that the agent, while accepting input in natural language, can now understand schemas, learn from existing metadata, and grasp the relationships between different data assets, enabling data practitioners to engage with it across the entire data pipeline lifecycle, she added.
These engagements could include asking the agent to perform tasks such as generating a data pipeline, modifying existing pipelines, and even troubleshooting issues, since it can analyze code and logs to identify the root cause of a problem and suggest or apply a fix.
