Analyze your Asana data in Jupyter
Getting your data from Asana to Jupyter can be a pain. Managing pipelines for all your data sources just isn’t sustainable, but your reporting can’t be put on hold while you wait for IT or Engineering to get to your ticket.
With Panoply, you can eliminate that hassle—and all that wasted time. Set up code-free connections to both Asana and Jupyter with just a few clicks. Then Panoply will automatically sync your data so it’s ready to analyze when you are.
It’s that simple: Unblock your data with Panoply.
How it works
Automatically sync and store the data you want with table-level control.
See the Asana data Panoply collects
Query-ready data with just a few clicks
Code-free data pipeline
Fuel your analytics
Integrate with Asana in minutes
Asana is a web app that helps teams track and get their work done. Panoply makes it easy to collect Asana data, including tasks, projects, teams, and everything else related to Asana. With a few clicks you can ingest all of your Asana data into a Panoply data warehouse, making it easy to analyze how your team is getting work done.
With Panoply you don’t need a data engineering team or complicated ETL tool, you can quickly go from raw data to analysis in minutes.
Learn more:
Asana Data Warehousing
Asana Business Intelligence
Other connectors with Asana
Integrate with Jupyter in minutes
You live in Jupyter Notebooks for analytics and prototyping and you love the flexibility and expressiveness of Python, Pandas, R and Julia, but your business users need the ease of a user-friendly BI tool. Supporting both is no problem with Panoply. We make it easy to sync data from 30+ data sources to multiple analytics tools, no code required.
Along with eliminating tedious setup and maintenance, Panoply’s built-in storage makes your queries faster, your pipelines more robust, and ensures that all your reporting is based on a single source of truth. Don’t choose between the open source tool you love and the BI tool the rest of the company can’t live without—power both with Panoply.
Learn more:
Documentation
Other connectors with Jupyter
Try It Now
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