The Best of Both Worlds with H2O and Spark
Sparkling Water allows users to combine the fast, scalable machine learning algorithms of H2O with the capabilities of Spark. Spark is an elegant and powerful general-purpose, open-source, in-memory platform with tremendous momentum. H2O is an in-memory platform for machine learning that is reshaping how people apply math and predictive analytics to their business problems. Integrating these two open-source environments provides a seamless experience for users who want to make a query using Spark SQL, feed the results into H2O to build a model and make predictions, and then use the results again in Spark. For any given problem, better interoperability between tools provides a better experience.
Key Features of Sparkling Water
- Access to H2O Algorithms
- Drive Computation from Scala, R and more…
- Simple Deployment
Access to H2O algorithms developed from the ground up for distributed computing and for both supervised and unsupervised approaches including Random Forest, GLM, GBM, XGBoost, GLRM, Word2Vec and many more.
Drive computation from Scala, R, or Python and use the H2O Flow UI, providing an ideal machine learning platform for application developers.
Easy to deploy POJOs and MOJOs to deploy models for fast and accurate scoring in any environment, including very large models.
How It works
Distributed, In-Memory Machine Learning
Sparkling Water is designed to be executed as a regular Spark application. It provides a way to initialize H2O services on Spark and access data stored in data structures of Spark and H2O.
Advanced Machine Learning for Spark
Use the best algorithms for distributed in-memory computing with your existing Spark implementation.
Deploy results in Spark
Results from H2O can easily be deployed using H2O low-latency pipelines or within Spark for scoring.
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Featured Use Cases
Providing predictive insights to decision makers and frontline employees is critical to improving customer satisfaction and decreasing operating costs across industries.
Detecting fraud even before it happens can prevent significant losses for financial institutions and prevent headaches for customers that can damage relationships.
Finding ways to improve the claims process can save money but also makes sure that customers and patients with legitimate issues are taken care of.
Related Case Studies
Data Engineer, Capital One
"H2O Sparkling Water allowed us to do rapid prototyping with a wide variety of algorithms."Watch the Video