The ability to manually scale, utilize initialization actions, and leverage preemptive virtual machines means Cloud Dataproc clusters can be tailored based on individual needs. Easily create and scale clusters to run native:
With a low price and minute-by-minute billing, customers no longer need to worry about the economics of running a persistant Spark or Hadoop cluster to unlock the benefits of fast, efficient, and reliable data processing.
Integration with Cloud Platform provides immense scalability, ease-of use, and multiple channels for cluster interaction and management. Seamless integration into other Google Cloud products means data is more accessable, operations are easy to monitor, and scaling is a non-issue.
A customer uses Spark standalone on one computer to perform data mining and analysis. The data is stored locally and they are using the Spark shell to examine the data along with Spark SQL.
How Dataproc addresses this need
Dataproc can create clusters that scale for speed and mitigate any single point of failure. Since Dataproc supports Spark, Spark SQL, and PySpark, they could use the web interface, Cloud SDK, or the native spark shell via SSH to perform their analysis safe from a single machine failure.
Dataproc quickly unlocks the power of the cloud for anyone without added technical complexity. Running complex computations now take seconds instead of minutes or hours.