Consume instances only when jobs are running

Pay solely for what you use

Optimize resources by separating compute and storage

Build on an ODPi-compliant stack with pioneering data science tools with the broader Apache Spark ecosystem.

Define clusters based on your application's requirement. Choose the appropriate software pack, version, and size of the cluster. Use as long as required and delete as soon as application finishes jobs.

Configure clusters with third-party analytics libraries and packages as well as IBM’s own enhancements. Deploy workloads from IBM Cloud services, such as machine learning.

Spin up compute-only clusters on demand. Because no data is stored in the cluster, clusters never need to be upgraded.

Provision more IBM Cloud Object Storage (or other data stores) on demand with no extra costs for compute cycles not used.

Adding and removing data nodes based on live demand is possible via REST APIs. Also, overhead costs remain low because there is no data stored in the compute cluster.

Using a multilayered approach significantly simplifies the individual cluster security implementation, while enabling access management at a more granular level.

Clusters are spun up to meet the needs of the job versus forcing jobs to conform to a single software package/version. Multiple different versions of software can be run in different clusters.

If you’re working with Apache Spark, but unsure how much resource is needed, provision a Serverless Spark instance that only consumes compute resource when running an app. Pay only for what you use.