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Below is our recent interview with Jack Parmer, CEO at Plotly Technologies:
Q: Jack, tell us something more about the company and your history?
A: Plotly began as a small group of scientists and engineers in Montreal, Canada. In five years, we’ve grown into a technical computing leader with 40 employees (and growing fast). We’re transforming how scientists, engineers, and business analysts visualize data through precedent-setting, open-source software.
We’re well-known for our namesake data visualization library, plotly.py, which has surpassed 5.5 million downloads. It’s completely free and open-source, and has been used by NASA, Google, Amazon, and Red Hat, to name a few.
Last year, we introduced Dash, another open-source product that’s making big waves. With Dash, a Python programmer can create responsive, interactive data analytics apps that previously required a whole team of designers, engineers, and analysts to make. We love Dash because it’s productive and open-source — a real collaboration. And Dash apps are web-based, so end-users can collect and share insights from their data in real time, also.
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Q: I read something about your partnership with Université de Sherbrooke, how did that start and where do you see the relationship going?
A: Yes, we’re collaborating with CoBIUS Lab at Université de Sherbrooke to further both their bioinformatics research and the development of Dash for use in analyzing and visualizing genomic data. Imagine being able to visually, interactively explore complex protein or DNA molecules in 3-D, or map the network of interactions between genes, all in Python. It’s pretty exciting.
At Plotly, we’re really focused on the local talent market. Many of our interns and staff come from local universities, and we’re glad to be playing a growing role in retaining technical talent here in Montreal.
Q: You’ve recently introduced Dash DAQ; what is it, and how does it work?
A: Dash DAQ is our solution for a key step in the Python data acquisition process. Scientists who use computers to collect data from physical instruments often work in Python. The problem is that there haven’t been many good ways to build graphical user interfaces in Python that are tailored for test benches, robots, scientific devices, or whatever’s at the end of the wire.
Dash DAQ allows Python users to build beautiful, flexible GUIs quickly so that they can get on with the work of collecting data, doing research, and sharing results. A Dash DAQ app can have virtual thermometers, knobs, sliders, LED displays — features you might use for controlling a device and collecting data. Users can connect with vendor-provided instrument drivers, or write their own with common Python libraries.
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Q: Can you give us insights into your other products?
A: We’re really proud of our open-source products because in developing them, we can really harness the power of a great developer community that spans the globe. The plotly library began as a JavaScript library; now it’s available for Python, R, and SQL users. Dash is available for Python users, and we’re expanding its functionality. Our open-source developer community is the heart of Plotly and the reason that these products are what they are today.
Enterprise users who want the power of Plotly or Dash in a private cloud or on-premise server can purchase our Plotly On-Premise or Dash Deployment Server products. These are premium products, and are supported by our excellent engineers. With a Dash Deployment Server (DDS), companies can deploy apps that report the latest, real-time data so decision makers are always up-to-date. Or they can deploy more ephemeral Dash apps for solving the analysis problem du jour. Through the DDS Portal, anyone in the company can log in for access.
Q: What’s next for Plotly Technologies? Do you have plans to grow, expand or diversify?
A: We are actively growing right now. We’re hiring, and we’ve expanded and renovated our offices (check out our Instagram account!). Our customers are really excited about Dash and the Dash Deployment Servers because together, they’re a truly enterprise-ready way to implement publication-quality, collaborative, real-time data analytics using one of today’s most popular programming languages.
