Analyze your Google Cloud Storage data in Python
Getting your data from Google Cloud Storage 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 Google Cloud Storage 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 Google Cloud Storage data Panoply collects
Query-ready data with just a few clicks
Code-free data pipeline
Fuel your analytics
Integrate with Google Cloud Storage in minutes
Panoply integrates with most popular cloud storage systems, including Google Cloud Storage. Google’s Cloud Storage is a secure, only storage system that stores objects via user defined buckets. Panoply makes it simple to move that data into your own Panoply Smart Data Warehouse without any ETL or ELT support.
Once added, your Google Cloud Storage data can be combined and analyzed with all other data sources, giving your analysts an opportunity to identify and drive business decisions from directly within Panoply. Create queries, generate reports, and develop actionable analyses using your Google Cloud data, and all other data you load to Panoply.
Learn more:
Google Cloud Storage Data Warehousing
Google Cloud Storage Business Intelligence
Other connectors with Google Cloud Storage
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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