Originally posted on LinkedIn
Strata Data Conference is fast approaching, and we're excited about the agenda of the week ahead (March 25 - 28) in Moscone West.
Each year Strata convenes some of the brightest minds in industry to talk about their uses cases. People talk about everything from the power of their solutions to the struggles they faced while developing them. We're particularly excited this year for the Jupyter Track focus within the wider conference. With so many people buying into Jupyter Notebooks as their enterprise standard, we want to hear how they’re adopted in different workflows.
For those who are unfamiliar with the technology, Jupyter notebooks are an interface where you can do all your exploratory data analysis, data cleansing, and data visualizations. At their most basic level, you can think of the notebooks as a common interface for communicating across different job functions simply and powerfully. Depending on the tooling, they can be used to execute functional programming languages from Python and R to Scala.
As with any standardized tool or common medium, there are myriad different ways to use the technology. It can be a powerful blank slate on which you can craft your thoughts and communicate the analysis of your data.
For the Jupyter sessions on Wednesday, March 27, come hear everything from how it's being integrated into education at UC Berkeley, to how it's essential for end to end workflows at GitHub and 8x8. If you're particularly curious about how to scale notebook use across your enterprise, come hear some IBMer's talk about the OSS project Jupyter Enterprise Gateway.
IBM is particularly excited about the Jupyter Enterprise Gateway (JEG) project because it's the perfect confluence of us wanting to help our enterprise customers who need to think about things like security and scaling, while also allowing us to contribute back to the open source community. Jupyter Enterprise Gateway is an open source project started from the Center for Open-Source Data & AI Technologies (CODAIT) group designed to make your notebook workflow scalable. Problematically, Jupyter notebooks on their own run as local processes, which limits their scalability to what's resources are available on that single local node. They also don't control what information users can see about each other, and therefore have potential security limitations. JEG addresses each of those concerns by leveraging resources managers across a compute cluster, and adding additional security via Kerberos.
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Are you not able to make it Strata, but curious about IBM's presence is Open Source projects more broadly? Check out some of our hottest open projects, or come join our conversations in the IBM Data Science community.#GlobalAIandDataScience#GlobalDataScience#IBM#Jupyter#JupyterNotebooks#notebooks