How can your business not only manage the growth of structured and unstructured data but also gain timely business insights from that data using existing data infrastructure techniques? Spark and Hadoop clusters provide data analytics, but they’re challenging to share as big data lakes continue to grow.
For the past two years, Red Hat has worked closely with enterprise customers struggling with multiple-petabyte workloads to devise and document a solution to these problems. These best practices are now delivered in the form of an architecture that decouples compute from storage to enable shared datasets on a massively scalable object store, Red Hat Ceph Storage. Red Hat data analytics infrastructure solution empowers data platform teams to focus higher up the stack and reduce data duplication. Above all, it helps to ensure that the right data is available for the right people at the right time
Red Hat data analytics infrastructure solution serves as an efficient, flexible platform for data lakes and a complement to big data analytic tools. Plus, with a common S3 interface and on-demand provisioning activated through Red Hat’s hybrid cloud infrastructure platform, data sets can be built the same way as in the public cloud. The result? A true hybrid cloud experience for data lakes, where users can improve overall analytics performance, gain faster insights from their data, and decrease acquisition and maintenance expenditures.