PixieDust

Graph databases are relative newcomers in the NoSQL database landscape. What are some graph model and design considerations when choosing a graph database in your architecture? Let's take a tour of a couple graph use cases that we've collaborated on recently with our clients to help you better understand how and why a graph database can be integrated to help solve problems found with connected data. Presented at DataWorks Summit San Jose - IBM Meetup on June 18, 2018. https://www.meetup.com/BigDataDevelopers/events/251307524/

PixieDust: an Open Source Library that simplifies and improves Jupyter Python Notebooks

Data & Analytics

@MargrietGr
Margriet Groenendijk
Developer Advocate for IBM Cloud Data Services
Python DBBUG meetup
Bristol
28 November 2016
PixieDust: an Open Source Library that simplifies
and improves Jupyter Python Notebooks

@MargrietGr
https://github.com/ibm-cds-labs/pixiedust

@MargrietGr
Run Spark locally in a Python notebook
https://www.continuum.io/downloads
http://spark.apache.org/downloads.html
Create a new kernel to use in a
Jupyter notebook

@MargrietGr
Spark installation
•http://spark.apache.org/downloads.html
•Spark release: 1.6.2
•package type: Pre-built for Hadoop 2.6
•mkdir dev
•cd dev
•tar xzf ~/Downloads/spark-1.6.2-bin-hadoop2.6.tgz
•ln -s spark-1.6.2-bin-hadoop2.6 spark
•mkdir dev/notebooks

$@MargrietGr • create directory ~/.ipython/kernels/pyspark1.6/ • create file kernel.json • cd ~/dev/spark/conf • cp spark-defaults.conf.template spark-defaults.conf • add to end of spark-defaults.conf:   spark.driver.extraClassPath=<HOME DIRECTORY>/data/libs/* { "display_name": "pySpark (Spark 1.6.2) Python 2", "language": "python", "argv": [ "/Users/sparktest/miniconda2/bin/python", "-m", "ipykernel", "-f", "{connection_file}" ], "env": { "SPARK_HOME": "/Users/sparktest/dev/spark", "PYTHONPATH": "/Users/sparktest/dev/spark/python/:/Users/sparktest/dev/spark/python/lib/py4j-0.9-src.zip", "PYTHONSTARTUP": "/Users/sparktest/dev/spark/python/pyspark/shell.py", "PYSPARK_SUBMIT_ARGS": "--master local[10] pyspark-shell", "SPARK_DRIVER_MEMORY": "10G", "SPARK_LOCAL_IP": "127.0.0.1" } }$

@MargrietGr
Jupyter Notebooks in the Cloud
• Server-client application to edit and run
notebook documents via a web browser
• Cells with:
•Code
•Figures and tables
•Rich text elements
• Different kernels: Python, R, Scala,
Spark

@MargrietGr
PixieDust: an Open Source Library that simplifies and
improves Jupyter Python Notebooks
• PackageManager
• Visualizations
• Cloud Integration
• Scala Bridge
• Extensibility
• Embedded Apps
https://developer.ibm.com/clouddataservices/2016/10/11/pixiedust-magic-for-
python-notebook/
@DTAIEB55

@MargrietGr
Install Spark packages or plain jars in your Notebook Python
kernel without the need to modify configuration file
Uses the GraphFrame Python APIs
Install GraphFrames Spark Package

@MargrietGr
One simple API: display()
Call the Options dialog
Panning/Zooming
options
Performance statistics

@MargrietGr
Easily export your data to csv, json, html, etc. locally on your laptop
or into a cloud-based service like Cloudant or Object Storage

@MargrietGr
Scala Bridge
Define a Python variable
Use the Python var in Scala
Define a Scala variable
Use the Scala var in Python

@MargrietGr
Easily extend PixieDust to create your own visualizations
using HTML/CSS/JavaScript
Customized
Visualization for
GraphFrame
Graphs

@MargrietGr
Encapsulate your analytics into compelling User
Interfaces better suited for Line of Business Users

@MargrietGr
Sign up for beta: http://datascience.ibm.com/features#machinelearning

@MargrietGr
https://developer.ibm.com/clouddataservices/author/
mgroenen/
Thanks!
Slides will be here:
http://www.slideshare.net/MargrietGroenendijk

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