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Often when working with new datasets, you’ll find it helpful to explore the data and test a hypothesis. Cloud Datalab, which is based on Jupyter Notebooks (http://jupyter.org), is a GCP tool for exploring, analyzing, and visualizing data sets. Widely used data science and machine learning libraries, such as pandas, scikit-learn, and TensorFlow, can be used with Datalab. Analysts use Python or SQL to explore data in Cloud Datalab.

Google Data Studio is useful if you want tabular reports and basic charts. The drag-and-drop interface allows nonprogrammers to explore datasets without having to write code.

As you prepare for the Google Cloud Professional Data Engineer exam, keep in mind the four stages of the data lifecycle—ingestion, storage, process and analyze, and explore and visualize. They provide an organizing framework for understanding the broad context of data engineering and machine learning.