Enhancing data exploration in BigQuery
In the previous sections, we outlined a few approaches for beginning to understand your data and beginning data exploration in BigQuery. Now we will outline some additional approaches and touch on best practices.
Advanced approaches
Jupyter Notebooks are popular for data exploration and analysis. They are tools commonly used by data scientists. However, notebooks are becoming more common in interacting with ML models and large datasets. You can leverage BigQuery’s integration with Jupyter Notebooks to write SQL queries and perform data exploration in a collaborative and interactive environment. By using libraries such as google-cloud-bigquery, you can execute queries, fetch results, and visualize data within the notebook itself.
BigQuery Studio provides an analytics workspace of notebooks within the BigQuery console. BigQuery Studio helps integrate tools into a single experience that can reduce the need to utilize other tools,...
