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Data Science Experience from IBM gets an update
Tuesday, October 3, 2017
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Richard Harris |
IBM Analytic announces new updates for their Data Science platform, Data Science Experiment (DSX).
In a blog post on Medium in the section for IBM Analytics, by their own Vikram Murali, the company announced that IBM was going to update its Data Science Experience (also called DSX). DSX is a tool for amateur and professional data scientists alike who are looking to use an insightful tool for the data science trade.
"With this release, we’ve focused on expanding support for external tools and doubling down on ease-of-use." said the IBM Analytics blog post.
- Manage all assets, including RStudio and data sets, from the Assets tab.
- Reserve Apache Spark resources from the new Runtimes tab in your project.
- Connect DSX Local projects to relational databases using Data sources and Remote data sets instead of Connections. (Data sources allow you to securely store information about your database and credentials.)
- Use DSX Local notebooks to retrieve data from relational databases using APIs from third party modules.
- Store objects in the file system instead of an object store.
- Configure alert thresholds, dashboard refresh, and log and metric rotations.
- Use REST APIs to manage files and folders.
- Submit an Apache Kafka streaming application that connects to a Kafka broker over SSL
"There’s an interesting pattern with technology. You see it time and again as new ideas and new capabilities come on the scene: While the futurists, journalists, and bloggers are busy touting the potential and debating the downsides, there’s always a group of actual users who simply sit down and get to work," said Vikram Murali. "Data science is no different. The hype goes on, but in the meantime the work is underway. Across every industry, dedicated data scientists are hammering through data to gain the insights that will help their organizations thrive. Along the way, those data scientists have developed strong processes to get things done - workflows, infrastructure, and ways to ingest, store, and manage the data they use."
"With this release, we’ve focused on expanding support for external tools and doubling down on ease-of-use." said the IBM Analytics blog post.
What is IBM Data Science Experiment used for?
- Collaborate with data scientists
- Work with machine learning
- Streaming analytics tools
- Enabling developers to collaborate with other disciplines
Key Highlights
- Manage all assets, including RStudio and data sets, from the Assets tab.
- Reserve Apache Spark resources from the new Runtimes tab in your project.
- Connect DSX Local projects to relational databases using Data sources and Remote data sets instead of Connections. (Data sources allow you to securely store information about your database and credentials.)
- Use DSX Local notebooks to retrieve data from relational databases using APIs from third party modules.
- Store objects in the file system instead of an object store.
- Configure alert thresholds, dashboard refresh, and log and metric rotations.
- Use REST APIs to manage files and folders.
- Submit an Apache Kafka streaming application that connects to a Kafka broker over SSL
"There’s an interesting pattern with technology. You see it time and again as new ideas and new capabilities come on the scene: While the futurists, journalists, and bloggers are busy touting the potential and debating the downsides, there’s always a group of actual users who simply sit down and get to work," said Vikram Murali. "Data science is no different. The hype goes on, but in the meantime the work is underway. Across every industry, dedicated data scientists are hammering through data to gain the insights that will help their organizations thrive. Along the way, those data scientists have developed strong processes to get things done - workflows, infrastructure, and ways to ingest, store, and manage the data they use."
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