Analyze your Twitter Ads data in Python
Getting your data from Twitter Ads 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 Twitter Ads 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 Twitter Ads data Panoply collects
Query-ready data with just a few clicks
Code-free data pipeline
Fuel your analytics
Integrate with Twitter Ads in minutes
Managing, optimizing, and reporting on Twitter advertising is easier than ever with Panoply. Twitter Ads, the on-site advertising service on the Twitter platform, provides a wealth of data for analysts and marketers. Panoply stores this data directly onto the Panoply Smart Data Warehouse, where it can be analyzed and combined with all other advertising or data sources. Panoply’s end to end data management platform then makes it possible for companies to extract, query, report, and monitor Twitter ad data. This helps you analyze how advertisements are performing, develop more effective campaigns, and identify new social media marketing opportunities all instantly from within the Panoply platform.
Learn more:
Documentation
Twitter Ads Data Warehousing
Twitter Ads Business Intelligence
Other connectors with Twitter Ads
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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