Explore and visualize Google Professional Data Engineer GCP

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  • Involves data exploration and visualization
  • to better understand the results of the processing and analysis.
  • Insights gained to drive improvements
  • Apply statistical methods and ML



  • An interactive web-based tool
  • Used to explore, analyze and visualize data.
  • Built on Jupyter notebooks earlier called IPython.
  • Launch an interactive web-based notebook to write and execute Python programs to process and visualize data.
  • The notebooks maintain their state and can be shared.
  • Support popular data-science toolkits, like pandas, numpy, and scikit-learn
  • Supports visualization packages, like matplotlib.
  • Supports Tensorflow and Dataflow.
  • Can load and cleanse data, build and verify models, and then visualize the results


Data science ecosystem

  • Deploy data science tools on GCP
  • Can deploy RStudio Server or Microsoft Machine Learning Server on a Compute Engine instance.
  • Deploy Jupyter or JupyterHub on Compute Engine instances.
  • Apache Zeppelin also supported


Visualizing business intelligence results

  • Number of reporting and dashboarding tools in GCP
  • Google Data Studio is a drag-and-drop report builder
  • The charts and graphs in the reports can be shared and updated.
  • Reports can contain interactive controls.
  • Access data from data files, Google Sheets, Cloud SQL, and BigQuery.
  • Visualize data in a spreadsheet, in Google Sheets