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Key takeaways
- The post 'In-memory Big Data: Spark + H2O' described architecting a simple RDD data import and export in H2O to connect Spark SQL workflows with H2O's Data Science and Deep Learning tools.
- The post said Spark users could use SHARK to extract and fuse datasets and then use H2O for predictions.
- The post described calling h2o.deepLearning() from a Scala interface alongside Spark, using an integration referred to as Shalala, to make the workflow more seamless.
Big Data has moved in-memory. Customers using SQL in their Join & Munging efforts via SHARK and Apache Spark need to use Regressions and Deep Learning. To make their experiences great & seamlessly weave SQL workflows with Data Science and Machine Learning, we are architecting a simple RDD data import-export in H2O. This brings continuity to their in-memory interactive experience. And support for Spark MLI using our native Scala API – Shalala. Big Data users can now use SHARK to extract and fuse datasets and H2O for better predictions.
Calling h2o.deepLearning() from within Scala interface alongside Spark (via Shalala) will make the workflow even more seamless for end users.