Using the `xl` function, you can load connected data into a Pandas DataFrame, seamlessly combining Excel’s familiar interface with Python’s advanced analytical tools.

However, Python’s methods for parallelizing operations often require data to be serialized and deserialized between threads or nodes, while Julia’s parallelization is more refined.

Meanwhile, the big data orchestration team provide services and tooling for scheduling and executing ETL (Extract, Transform, Load) of data and adhoc data pipelines.

You’ve got some nice tools for data scientists, or folks that aspire to be data scientists.” You can also Python for DevOps, system scripting, web development, and data science, Silge said.