Explore and visualize Google Professional Data Engineer GCP

• 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

Datalab

• 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