Google has introduced updates to its BigQuery service aimed at advancing data automation for practitioners. The data engineering agent has transitioned to a comprehensive system capable of pipeline building, data transformation, and troubleshooting. It can now process natural language input, understand data schemas, and learn from metadata relationships. The data science agent has been integrated into BigQuery Notebook to streamline the creation of automated data science workflows, including multi-step planning and code execution.
"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.
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.
The data science agent, which was made accessible via Google's free, cloud-based Jupyter notebook service, Colab, to help data scientists automate feature engineering, is now integrated into BigQuery Notebook.
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