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dbt + BigQuery Starter for Retail Sales Forecasting

A production-shaped dbt project on BigQuery that turns raw Corporación Favorita grocery data into clean, tested, forecast-ready models. Clone it, point it at your warehouse, and you have a working analytics-engineering layer in an afternoon.

Time to Implement:

4 - 8 Weeks

  • dbt

  • BigQuery

  • Vertex

  • MLflow

  • Prefect

  • XG-Boost

  • Optuna

Favorita: dbt + Prefect Orchestration

Wrap the Favorita dbt project in a Prefect flow so your transformations run on a schedule, retry on failure, and tell you when something breaks — instead of someone running dbt by hand and hoping.

Time to Implement:

One to two days

    Favorita: Vertex AI for ML

    Take the forecast-ready Favorita tables and train, evaluate, and serve a sales-forecasting model on Google Vertex AI — a managed path from clean data to predictions you can actually call from production.

    Time to Implement:

    Two to four weeks

      Favorita: XGBoost Train & Predict

      A focused XGBoost pipeline for forecasting Favorita grocery sales — feature engineering, training with cross-validation, and prediction. The fast, interpretable baseline every forecasting project should start with.

      Time to Implement:

      Three to five days

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        See the latest.

        Blog

        Medium

        LinkedIn

        Github

        Check us out.

        Portfolio

        Contact Us

        © 2026 theDataStrategist

        See the latest.

        Blog

        Medium

        LinkedIn

        Github

        Check us out.

        Portfolio

        Contact Us

        © 2026 theDataStrategist