Panoply is a cloud-based data platform built for analytics to streamline the journey from raw data to analysis, saving time and adding value for data analysts, engineers, and scientists. By automating the entire data pipeline, Panoply allows you to go ;from raw data held in many data sources to analysis in less than 10 minutes, reducing the overhead associated with preparing and modeling data and managing infrastructure – cutting down development time by 80%.
After you are logged in or sign up for a free trial, check out our Getting Started guide where we cover the basics of importing your data, running queries, performing data analysis, and setting up user permissions.
If you have an account and understand the basics, you’re ready to learn how to collect data. You can set up any major database as a data source, ingest data via file upload, or choose one of the APIs Panoply supports. This guide also contains our SDKs for Python, Node.js, and Ruby. For detailed instructions specific to your data source, visit our Data Sources documentation.
After your data sources are set up, read the guide on how to manage your data. Here you’ll learn about primary and incremental keys in Panoply, our approach to nested data, history tables, transformations, string formats, and archiving.
To analyze your data, Panoply integrates with Business Intelligence tools such as Metabase, Tableau, Data Bricks, Looker, Chartio, Re:dash, Zeppelin, iPython Notebook, Shiny Apps by RStudio, and Sisense. In this guide, learn where to find the credential details necessary to connect Panoply with your preferred data visualization tool.
We value our customers, so we use the latest security measures. The Data Security guide explains how Panoply protects your data, our approach to access control, and details about IP whitelisting.
Frequently Asked Questions
Answers to your frequently asked questions about Panoply can be found in the Panoply Community. The Community contains announcements, along with conversations with Panoply experts and other Panoply customers about collecting, managing, and analyzing data.