The Anaconda team had a great time presenting and networking at Strata this year.

On Tuesday, September 11, Senior Solutions Architect James Bednar presented a tutorial on Making Interactive Browser-Based Visualizations Easy in Python. While Python lets you solve data-science problems by stitching together packages from the Python ecosystem, it can be difficult to assemble the right tools to solve real-world problems. James demonstrated how to use the 15+ packages covered by the new PyViz.org initiative to make it simple to build interactive plots and dashboards, even for large, streaming, and highly multidimensional data.

And on Wednesday, September 12, SVP of Products & Marketing Mathew Lodge presented Conda, Docker & Kubernetes: The Cloud Native Future of Data Science. Big data architectures like Hadoop and Spark solve the distributed database problem, but assume your code is written in Java or another JVM-based language like Scala. However, data science, predictive analytics, and machine learning don’t happen in JVM-based languages—they happen in Python, R, and, to a lesser extent, C/C++. Furthermore, all the major players like AWS, Microsoft, Google, IBM, Red Hat, and Docker are lined up behind Kubernetes. Containers and Kubernetes make great language-agnostic distributed computing clusters, as it is just as easy to deploy Python as it is Java. Mathew shared his perspectives on the promise of cloud native data science and where it’s heading next.

We also were pleased to welcome guest speakers to the Anaconda booth! Andrew Wilson and Fintan Quill of Kx Systems presented Wall Street’s Best Kept Secret Now on Anaconda. Andrew and Fintan discussed time-series database platform kdb+. With its built-in programming language q, kdb+ is used for high-performance trading analytics at most of the world’s financial institutions. Now available for free for personal use on Anaconda, it is being used by developers of all types. Andrew and Fintan talked about the interoperability of q with the Anaconda platform, and what you can do in q vs. Pandas.