Start analyzing your Microsoft Azure SQL Data Warehouse data with Jupyter
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With over 80 data source connections, you can gather all your business data in one place in minutes. You’ll never have to worry about silos separating your CRM, database, advertising, analytics, or other business data.
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Seamlessly scale from gigabytes to terabytes without worrying about performance or maintenance. Panoply combines a data-warehouse-as-a-service with integrated ETL pipelines in a simple management console that requires no engineering experience.
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With Panoply, your data has the power to transform your business. Panoply prepares your data into easy-to-analyze tables and connects to all popular BI tools and analytical notebooks. From the executives to the analysts, everyone will have the most up-to-date data and the insights they need to drive your business forward.
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Integrate with Microsoft Azure SQL Data Warehouse in minutes
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.
Other integrations with Microsoft Azure SQL Data Warehouse
Integrate with Jupyter in minutes
With open source Jupyter Notebook, programmers create and share interactive code, visual graphics, and text. This BI tool supports script in over 40 languages. It integrates to large data platforms like Apache Spark from Python, R and Scala. You can share Jupyter Notebook work through email, Dropbox, GitHub or the Jupyter Notebook Viewer. As with any data operations, you need an ETL to extract, transform and output your Jupyter Notebook code work. Panoply’s Jupyter Notebook integration makes the ETL process easy and seamless. This fully automated pipeline manages the entire ETL workflow on the cloud in real time. With Panoply, your data is continuously uploaded, cleaned and streamed to the client side. So the output you share is always the most relevant and up to date.
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