Getting your data from Microsoft Azure SQL Data Warehouse 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 Microsoft Azure SQL Data Warehouse 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.
Panoply provides end to end data automation, from extraction to analysis, all with an ETL-free pipeline that is capable of connecting to both structured and semi-structured data sources. One of its newest data warehouse integrations is with Microsoft Azure SQL Data Warehouse.
It takes only a few minutes to setup Panoply’s integration with Azure SQL Data Warehouse, allowing businesses of all sizes to import their historical data using Panoply’s data connectors into their Azure SQL Data Warehouse with only a few clicks. With Panoply, your Azure SQL Data Warehouse can be populated with data from dozens of sources and can be analyzed along with any additional data sources you may collect. With Panoply, you can start ingesting data the pain-free way with Panoply for Microsoft Azure SQL Data warehouse.
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.
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