You work for MDFT Pro, a well-known training agency, and you have a Fabric tenant that contains JSON files in OneLake. The files have one billion items. You plan to perform time series analysis of the items. You need to transform the data, visualize the data to find insights, perform anomaly detection, and share the insights with other business users. The solution must meet the following requirements:
- Use parallel processing
- Minimize the duplication of data
- Minimize how long it takes to load the data
What should you use to transform and visualize the data?
a Microsoft Power BI report that uses core visuals is incorrect. Power BI core visuals don't provide the parallel processing and transformation capabilities needed for time series analysis on billion-row datasets.
the PySpark library in a Fabric notebook is correct. PySpark supports parallel processing on Spark clusters, works directly on OneLake data, and provides visualization libraries for time series analysis and anomaly detection.
the pandas library in a Fabric notebook is incorrect. pandas operates on a single node without parallel processing and would duplicate data by loading it into memory, failing the requirements.