The 5th Generation of Dash Enterprise

The latest release offers industry-leading user experience within an IT-friendly architecture

Recorded on November 8, 2022

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Flexible connectivity, infinitely customizable chart types, and the power of advanced analytics – these capabilities go beyond BI. Data scientists need a tool that is purpose-built for Python and offers enterprise-grade app management and security compliance.

These needs are what fueled the recent development of Dash Enterprise. In the 5th generation, the low-code data app platform includes following:

  • A cloud-native, Kubernetes architecture
  • An out-of-the-box identity access integration wizard
  • A simple deploy button, app collaboration features, and Workspace IDE
  • Drag-and-drop data uploads capable of processing millions of rows of data
  • Enhanced admin user and app management with full platform observability

Tune into the launch event recording to hear directly from Plotly’s Chief Product Officer, Chris Parmer. He shares how the first class data app platform enables enhanced scaling and stability, while reducing development time and network cost. Chris is joined by special guest, Russ Zaliznyak, Principal Data Scientist at Intuit. Russ demos Intuit’s Sequential Probability Ratio Testing (SPRT) app built with the 4th generation of Dash Enterprise and share how over 500 internal stakeholders now have a central hub for statistical analysis.

Meet the Speakers

Chris Parmer

Chris Parmer is the Chief Product Officer and Co-Founder of Plotly. As the creator of Dash, Chris leads development efforts to make the framework the fastest way to build, deploy, and scale interactive data apps. As data science teams become a standard establishment within the enterprise, Chris works to ensure that even the most advanced analytic insights are accessible by everyone – whether or not they know how to code.

Russ Zaliznyak

Russ Zaliznyak serves as the Principal Data Scientist and Experimentation Leader at Intuit. He specializes in statistical methodology, machine learning, and ETL. He is well-versed in pySpark and Python and uses object oriented programming to help create apps that solve enterprise-wide problems. Additionally, he is passionate about teaching and sharing the benefits of experimentation.