Empowering the P&E industry(Power & Energy) with Modern Predictive Analytics
In the P&E industry, accuracy isn’t just a metric—it’s a requirement for stability. Whether it’s predicting energy demand for a city grid or forecasting throughput for an oil pipeline, the “cost of being wrong” is immense.
I’m seeing two powerful patterns emerge for solving this at scale within Google Cloud:
- Path 1: The Pro-Code Approach (BQML in Dataform)
- BigQuery ML (BQML): Build and deploy production ready forecasting models using SQL without
moving the data in BigQuery. - Dataform: Implement robust MLOps. Manage your SQL pipelines, version control, dependency,
documentation with a dataform framework. (use composer for pipeline beyond BQ along with
Dataform. )
- BigQuery ML (BQML): Build and deploy production ready forecasting models using SQL without
- Path 2: The AI-First Approach (BigQuery Data Agents)
The future of data engineering is generative. We now have Data Science and Data Engineering
Agents built into BigQuery:- These agents leverage Gemini to assist in building complex transformations.
- They use Dataform in the background to automate and orchestrate the workflow.
- They allow you to go from a natural language prompt to a deployed pipeline in minutes.
The Result?
A seamless flow from data to decision. Visualize your forecasts in Looker Studio for executive dashboards or push them directly into downstream operational apps to drive real-time impact.
