Using Anaconda to Embrace Python 3 And Support Python 2
The data science community received a special delivery in December with the release of Python 3.6. At the time, I had a conversation with The New Stack to discuss what’s new with this release, and why we at Continuum see 2017 as the year that Python 3 is beginning to dominate the data science landscape, […]
Conda’s New Noarch Packages
Beginning with conda version 4.3 and conda-build 2.1, two new types of noarch packages are supported. Noarch Python packages cut down on the overhead of …
Reinforcing Open Data Science Foundations with conda 4.3 Release
At Continuum Analytics, we talk a lot about Open Data Science—this new world order of analytics that is rapidly accelerating the pace of innovation…
Productionizing and Deploying Data Science Projects
An end-to-end data science workflow includes stages for data preparation, exploratory analysis, predictive modeling, and sharing/dissemination of the results. At the final stages of the workflow, or even during intermediate stages, data scientists within an organization need to be able to deploy and share the results of their work for other users (both internal analysts […]
Anaconda Easy Button – Microsoft SQL Server and Python
Previously there were many twisty roads that you may have followed if you wanted to use Python on a client system to connect to a Microsoft SQL Server database, and not all of those roads would even get you to your destination. With the news that Microsoft SQL Server 2017 has increased support for Python, […]
Anaconda Joins Forces with Leading Companies to Further Innovate Open Data Science
In addition to announcing the formation of the GPU Open Analytics Initiative with H2O and MapD, today, we are pleased to announce an exciting collaborati…
Open Data Science is a Team Sport
As every March Madness fan knows, athletic talent and coaching are key, but it’s how they come together as a unit that determines a team’s su…
The Conda Configuration Engine for Power Users
Released last fall, conda 4.2 brought with it configuration superpowers. The capabilities are extensive, and they’re designed with conda power users, devops engineers, and sysadmins in mind. Configuration information comes from four basic sources: hard-coded defaults, configuration files, environment variables, and command-line arguments. Each time a conda process initializes, an operating context is built that […]
Announcing Anaconda Project: Data Science Project Encapsulation and Deployment, the Easy Way!
One year ago, we presented Anaconda and Docker: Better Together for Reproducible Data Science. In that blog post, we described our vision and a foundational approach to portable and reproducible data science using Anaconda and Docker. This approach embraced the philosophy of Open Data Science in which data scientists can connect the powerful data science […]
Self-Service Open Data Science: Custom Anaconda Management Packs for Hortonworks HDP and Apache Ambari
As part of our partnership with Hortonworks, we’re excited to announce a new self-service feature of the Anaconda platform that can be used to generate custom Anaconda management packs for the Hortonworks Data Platform (HDP) and Apache Ambari. This functionality is now available in the Anaconda platform as part of the Anaconda Scale and Anaconda […]