Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Portable & Scalable Data
Visualization Techniques
for Spark & Notebook based Analytics
Douglas Moore
Enterprise Solutions ...
Why Portable Data Visualizations?
Portability is Hard
https://www.anaconda.com/blog/python-data-visualization-2018-why-so-many-libraries
Graphic by Jake VanderPlas
Blog by Jame...
Strategies
Summary: Portable Data Visualization Strategies
▪ Image buffer, Image file
▪ HTML/JS in-line
▪ Data Lake to Data Visualiza...
Resources
Assets: https://github.com/dmoore247/spark-ai-summit-2020
▪ Demo Notebook
References
▪ Python Visualization Land...
Feedback
Your feedback is important to us.
Don’t forget to rate and
review the sessions.
Data Lake Visualization Drawn to scaleBronze
Portable Scalable Data Visualization Techniques for Apache Spark and Python Notebook-based Analytics
Portable Scalable Data Visualization Techniques for Apache Spark and Python Notebook-based Analytics
You’ve finished this document.
Download and read it offline.
Upcoming SlideShare
What to Upload to SlideShare
Next
Upcoming SlideShare
What to Upload to SlideShare
Next
Download to read offline and view in fullscreen.

Share

Portable Scalable Data Visualization Techniques for Apache Spark and Python Notebook-based Analytics

Download to read offline

Python Notebooks are great for communicating data analysis & research but how do you port these data visualizations between the many available platforms (Jupyter, Databricks, Zeppelin, Colab,…). Also learn about how to scale up your visualizations using Spark

  • Be the first to like this

Portable Scalable Data Visualization Techniques for Apache Spark and Python Notebook-based Analytics

  1. 1. Portable & Scalable Data Visualization Techniques for Spark & Notebook based Analytics Douglas Moore Enterprise Solutions Architect, Databricks June 2020
  2. 2. Why Portable Data Visualizations?
  3. 3. Portability is Hard
  4. 4. https://www.anaconda.com/blog/python-data-visualization-2018-why-so-many-libraries Graphic by Jake VanderPlas Blog by James A. Bednar
  5. 5. Strategies
  6. 6. Summary: Portable Data Visualization Strategies ▪ Image buffer, Image file ▪ HTML/JS in-line ▪ Data Lake to Data Visualization ▪ Hooks ▪ Headless Chrome browser ▪ Add a proxy ▪ Scale out w/ Spark
  7. 7. Resources Assets: https://github.com/dmoore247/spark-ai-summit-2020 ▪ Demo Notebook References ▪ Python Visualization Landscape ▪ pandas_bokeh ▪ pyviz.org
  8. 8. Feedback Your feedback is important to us. Don’t forget to rate and review the sessions.
  9. 9. Data Lake Visualization Drawn to scaleBronze

Python Notebooks are great for communicating data analysis & research but how do you port these data visualizations between the many available platforms (Jupyter, Databricks, Zeppelin, Colab,…). Also learn about how to scale up your visualizations using Spark

Views

Total views

277

On Slideshare

0

From embeds

0

Number of embeds

0

Actions

Downloads

14

Shares

0

Comments

0

Likes

0

×