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Data analytics

Configuring Apache Superset for planet level scaling

​Apache Superset is a popular open-source data visualization and exploration platform that can handle large-scale data, but configuring it for planet-level scaling requires careful planning and attention to detail. Here are some steps you can follow to help ensure that your Apache Superset setup is ready for planet-level scaling: 

  1. ​Plan your hardware resources: Before scaling Apache Superset, you need to determine how much hardware resources you need to support your data. This will depend on the size of your data, the number of users, and the number of queries being executed. 
  1. Choose a suitable database: Apache Superset can connect to various types of databases, including relational databases such as PostgreSQL, MySQL, and SQLite, as well as NoSQL databases like Apache Cassandra and Amazon Redshift. You will need to choose a database that can handle large-scale data and provides good performance. 
  1. Configure database connection pooling: To avoid overloading the database server, it’s important to configure connection pooling, which allows multiple clients to reuse the same database connection. This can be done in the Apache Superset configuration file. 
  1. Use caching: Caching can help speed up the performance of Apache Superset by reducing the number of queries that need to be executed. You can use cache providers like Redis or Memcached to store frequently used data. 
  1. ​Use a load balancer: To distribute the load across multiple Apache Superset instances, you can use a load balancer, such as HAProxy or NGINX, to route traffic to different servers. 
  1. ​Monitor and tune performance: Regularly monitor the performance of your Apache Superset setup and make changes as needed to ensure that it continues to perform well. This may include tweaking the database configuration, caching settings, and other performance-related parameters. 
  1. Consider using an auto-scaling solution: An auto-scaling solution, such as AWS Auto Scaling, can help you automatically scale your Apache Superset setup as the number of users and queries increases. 

​By following these steps, you can help ensure that your Apache Superset setup is ready for planet-level scaling and can handle large-scale data with ease. 

​If you’re looking to scale Apache Superset for your data intelligence needs, the above steps can certainly help you get there. However, with the complexity involved in planet-level scaling, you might need expert assistance to ensure that your configuration is optimized for maximum performance. At Niograph, our Data Intelligence services can help you achieve the scalability you need to handle even the largest datasets. Contact us today to learn more. 


Anish Bapna

Anish is the Founder and Managing Partner at Niograph. He currently leads Tech Consulting and System Implementation Services for Niograph. His expertise lies in architecting large scale Digital Transformation initiatives, with a focus on Cloud Computing, Data management, and Artificial Intelligence. Anish has a broad range of experience in Enterprise Portfolio Rationalization, Enterprise and Solution Architecture, Product Management, and Data Platform Engineering.