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You're reading from  Apache Superset Quick Start Guide

Product typeBook
Published inDec 2018
Reading LevelIntermediate
Publisher
ISBN-139781788992244
Edition1st Edition
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Author (1)
Shashank Shekhar
Shashank Shekhar
author image
Shashank Shekhar

Shashank Shekhar is a data analyst and open source enthusiast. He has contributed to Superset and pymc3 (the Python Bayesian machine learning library), and maintains several public repositories on machine learning and data analysis projects of his own on GitHub. He heads up the data science team at HyperTrack, where he designs and implements machine learning algorithms to obtain insights from movement data. Previously, he worked at Amino on claims data. He has worked as a data scientist in Silicon Valley for 5 years. His background is in systems engineering and optimization theory, and he carries that perspective when thinking about data science, biology, culture, and history.
Read more about Shashank Shekhar

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Main configuration file

So, we have completed configuring Superset. Let's take a look at the complete Superset configuration file:

# Superset Configuration file
# add file superset_config.py to PYTHONPATH for usage

# Metadata database
SQLALCHEMY_DATABASE_URI = "postgresql+psycopg2://superset:superset@localhost/superset"

# Securing Session data
SECRET_KEY = 'AdLcixY34P' # random string

# Caching Queries
CACHE_CONFIG = {
# Specify the cache type

'CACHE_TYPE': 'redis',
'CACHE_REDIS_URL': 'redis://localhost:6379/0',
# The key prefix for the cache values stored on the server
'CACHE_KEY_PREFIX': 'superset_results'
}

# Set this API key to enable Mapbox visualizations
MAPBOX_API_KEY = os.environ.get('MAPBOX_API_KEY', 'mapbox-api-key')

# Long running query handling using Celery workers
class
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Apache Superset Quick Start Guide
Published in: Dec 2018Publisher: ISBN-13: 9781788992244

Author (1)

author image
Shashank Shekhar

Shashank Shekhar is a data analyst and open source enthusiast. He has contributed to Superset and pymc3 (the Python Bayesian machine learning library), and maintains several public repositories on machine learning and data analysis projects of his own on GitHub. He heads up the data science team at HyperTrack, where he designs and implements machine learning algorithms to obtain insights from movement data. Previously, he worked at Amino on claims data. He has worked as a data scientist in Silicon Valley for 5 years. His background is in systems engineering and optimization theory, and he carries that perspective when thinking about data science, biology, culture, and history.
Read more about Shashank Shekhar