Analyzing your data

Data analysis within kdb Insights Enterprise is facilitated from the UI primarily through use of the Query Window. Aimed principally at data scientists and data engineers it provides an isolated location where you can prototype analytics and explore available data in an area which will not impact production workloads.

The scratchpad provides you with a location to analyze data you have queried from the database. Analysis can be completed in Python or q allowing you to to create functions for use throughout kdb Insights Enterprise, interrogate your data to learn more about its properties or to prototype pipelines prior to deployment to production environments. Querying the database is completed through use of a query builder, SQL or via q allowing you flexibility in choosing how you access your data. During analysis users can inspect their data via a console output, a table formatted return or through generation of visualization in the visuals tab.

To get started with development in this environment follow the quickstart guide outlined here, this will provide you with an end-to-end guide that allows you to query data, generate a set of analytics and produce visualization of this analysis.

Once you have completed the quickstart guide the documentation you will follow in this section outlines the following aspects of the Query Window in greater detail.

• Querying Data from the database
• Developing Python code within the scratchpad
• Developing q code within the scratchpad
• Visualizing output graphically and in console.