Why did we build the features we did with Dash Enterprise? How are these features different from traditional BI software like Tableau? What are the stories and who are the people that motivated this new breed of analytics platform?
We’ve found that many Dash open-source developers aren’t aware of Dash Enterprise. You all know me from open-source and this forum, but what you may not know is that I spend 80% of my time helping customers achieve business mandates through Dash Enterprise capabilities.
Dash Enterprise for Python analytics at scale
10 years ago, we knew that Python was the future of analytics. It had the power of MATLAB, but was free and advancing more quickly in capabilities and community. Today, that foresight could not have proven to be more true. Python is the backbone of almost all advancements in AI, ML, data science, and scientific computing. Dash Enterprise is the platform on which these advanced analytics techniques are delivered to business users.
Analytics used to mean reports or dashboards of summary statistics. But today, techniques such as deep learning, computer vision, predictive analytics, and natural language processing have become essential to the business operations of corporate America. You need Python for these advanced analytics techniques.
Dash Enterprise brings these techniques to a dashboard format, so that business users can run these Python models through a Dash UI, with the same simplicity and security of a Tableau dashboard.
With Python, there is plenty of room at the bottom. Every day we see Tableau, PowerBI, and Excel users pick up Dash as their first Python project. They are delighted by how easy it is get something working. Dash puts a visual face on Python models to make them exploratory. Dash Enterprise makes it simple to publish these Dash apps for seamless operation in business environments.
Open-source is not enough
Open-source projects, including Dash, are terrible at addressing enterprise needs. One reason is security: It’s difficult to communicate the exact specifications of your company’s authentication protocol in a public GitHub issue. These issues are better worked out over private channels. Another reason is the Few versus Many problem: Open-source projects thrive with a broad user coalition of academics, researchers, small companies, hobbyists, and large companies. Only the large companies care about enterprise-grade features: Corporate authentication, auditability, zero-downtime infrastructure, etc. For everyone else in the open-source community, these features are boring and irrelevant.
At the same time, open-source projects stall if they do not successful stratify as layers of digital infrastructure among large companies. Without large company adoption, open-source projects fail to attract the funding needed for long-term, multi-decade support.
Dash Enterprise is the closed-source subset of Dash features that are needed for seamless deployment and operation of Dash apps in high-security, high-stakes business environments. Almost 10% of the Fortune 500 uses Dash Enterprise, doubling every year. We’ll only work with your business on the Dash Enterprise platform - it’s the only platform that has been battle-tested behind corporate firewalls for large scale deployment of Python analytic applications.
Plotly serves Enterprises who are operationalizing advanced Python analytics through Python and Dash. With Dash Enterprise, we’ve built our dream platform for developing & deploying Python Dash applications in high security, high stakes corporate environments. We believe that Python analytics will continue to unlock the innovations that we desperately need for climate change, drug development, autonomous vehicles, quantum computing, and AI ubiquity.
In this series of essays, I’ll provide the stories behind Dash Enterprise’s cornerstone features, and how we arrived at their essentiality for enterprise deployments. Since Dash is an analytic app building framework, we call these features the Analytic App Stack:
The Dash Enterprise Fieldnotes Series: