Google Drive

Google Drive is a cloud storage service developed by Google which allows you to store, access, and share files online.

Panoply makes it easier than ever to access your Google Drive data. Explore it in SQL or connect your favorite BI tools and analytical notebooks for custom data visualization and advanced analytics.

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Google Drive BI Integrations

Modernizing Google Drive BI

Preparing for analytics

Do you spend more time on data preparation or analysis? If you’re like most data analysts, you spend most of your time exporting CSVs, hacking spreadsheets, maintaining Python scripts, and managing databases. And you do all this hard work just for managers to ask why your reports don’t include today’s data. Your data deserves better, and so do you.

Automating data management

That laborious process of syncing and synthesizing data is called data management. Yes, it’s important—everyone knows good analysis requires good data. But every minute spent managing data is a minute that could be spent on analytics. The good news is that these processes can be automated and managed in the cloud without any complicated scripts or engineering expertise.

Your Google Drive data analytics platform

Panoply is the easiest way to sync, store, and access your Google Drive from all your business intelligence tools and analytical notebooks. It only takes a few minutes to connect your Google Drive data to Panoply. Once connected, Panoply will automatically refresh your data as it changes. There are no scripts to maintain or processes to manage. You can focus on what matters your data and your business.

Panoply makes your Google Drive data accessible
so you can analyze it

Expected Google Drive Data
The Google Drive data source allows you to import data from files stored in your Google Drive account. Panoply supports, various files types including character-delimited files (CSV, TSV), JSON, plain text files, and Microsoft Excel files. Archive-type files like .tar and .gzip are also valid, provided that they contain the aforementioned supported files. eFor file sources that support structured or semi-structured data, like CSV or Excel files, Panoply uses the first row of their data as the columns of their target table. Any rows after that become the records. Sources that contain nested data, usually JSON files, have their nested data extracted and moved to their own tables with foreign keys provided. Lastly, archive files are decompressed, and then their individual files processed accordingly.

Screenshots

Panoply has given us drastic improvements in query speed and brought all our data in one place. Our visualizations are much more fast and efficient.
Andrew Zeck - CTO and Co-founder, Saucey
Read the customer success story

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