Apache Spark is seen by data scientists and analysts as a preferred platform to manage and process vast amounts of data to quickly find insight and knowledge from big data frameworks. However, the programming effort required to build pipelines in Spark often creates a barrier to its successful adoption.
View and learn how users of KnowledgeSTUDIO for Apache Spark, a wizard-driven productivity tool for building Spark workflows, have overcome these challenges.
Learn how data science teams can:
• Utilize interactive workflows with an automated design canvas for building, displaying, refreshing, and reusing analytic models
• Automatically generate code that can be customized and incorporated into production scripts
• Include manually written code within the graphical workflow
• Leverage advanced modelling with open source packages such as Spark ML, Spark SQL
• Avoid overhead costs of parallelization when datasets are very small
• Build, explore data segments, and discover relationships using patented Decision Tree technology