November 2, 2015

Using Python on distributed computing technologies like Hadoop and Spark make it easier to create and deploy advanced analytics into production environments.

But managing your packages in multiple environments including your distributed cluster can be a full-time job.

In this webinar, Kristopher Overholt will show you how to use the Python packages you know and love across your cloud-based or bare metal cluster – and provision them with the same conda packages you use on your local machine.

Remote Python Package Management Made Easy

Using Python on distributed computing technologies like Hadoop and Spark make it easier to create and deploy advanced analytics into production environments.

But managing your packages in multiple environments including your distributed cluster can be a full-time job.

In this webinar, Kristopher Overholt will show you how to use the Python packages you know and love across your cloud-based or bare metal cluster – and provision them with the same conda packages  you use on your local machine.

You’ll Learn To:

  • Conda Install Your Favorite Packages on Every Node in Your Cluster – In One Command
  • Interactively Query 1 TB of Data using Distributed SQL engines like Impala and Hive with Blaze
  • Use NLTK & Python on a Cluster to Perform Distributed Natural Language Processing
  • Use Spark, NumbaPro, and SciPy to Perform Distributed Image Processing on GPUs
  • Interactively Explore Big Data in the Browser with Bokeh

About the Author

admin has been with the Anaconda Global Inc. team for over 2 years.

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