Analyze your Facebook Pages data in Python
Getting your data from Facebook Pages to Python 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 Facebook Pages and Python 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 Facebook Pages data Panoply collects
Query-ready data with just a few clicks
Code-free data pipeline
Fuel your analytics
Integrate with Facebook Pages in minutes
Panoply comes with several Facebook integrations, including Facebook page data. Facebook’s business page analytics provides thorough data ripe for analysis, including active users, cohorts, funnels, and more. That data can be instantly integrated into your Panoply data warehouse, where your logged event and page data can be combined and analyzed with all of your data sources.
With Panoply, Facebook Pages data instantly becomes a usable part of your existing datasets, without requiring any ETL or ELT process or any custom scripts. From within the Panoply platform, you can start to query and analyze that data right away, and leverage your business page to better support your company’s marketing and sales efforts.
Learn more:
Documentation
Facebook Pages Data Warehousing
Facebook Pages Business Intelligence
Other connectors with Facebook Pages
Integrate with Python in minutes
Love the power of Python for analytics and machine learning but need a way to support it alongside other BI tools? Panoply makes it easy to pipe data to multiple analytics tools with 30+ code-free connectors you can set up in minutes.
Along with eliminating API configuration and maintenance, Panoply automatically stores a copy of your data in the cloud, making your queries faster and more stable. Plus, by powering all your BI and analytics tools with the same centralized data you can ensure that key metrics are consistent across the company, no matter who’s doing the reporting.
Learn more:
Documentation
Other connectors with Python
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