Run databases and data analytics in a consistent way across clouds to accelerate delivery of cloud-native applications
Databases and data analytics provide methods for ingesting, storing, processing, and analyzing datasets from a variety of sources for use cases such as, mobile and ecommerce applications, AI/ML, business intelligence, and more.
Deploy and manage modularized databases and data analytics workloads anywhere with speed, allowing faster project execution and more frequent updates.
Dynamic scaling of compute resources to meet the changing needs of databases and data analytics workloads.
Containerize once, deploy and move anywhere.
Downtime and data loss
Failures, outages, data corruption
Architecture and operations (e.g. containers, storage, networking, data protection), performance tradeoff
Lack of ISV support
ISV endorsement or support documentation
Lack of expertise
Gaps in skills and processes
Kubernetes Operators simplify and automate the deployment, scaling, and lifecycle management of containerized databases and data analytics on Red Hat OpenShift. This helps enable DevOps, and allows Database Administrators (DBAs) to focus on more strategic tasks such as controlling user access and security.
Consistency and portability
Secure deployment, operations, and portability in a consistent way across the hybrid cloud. Run containerized databases and data analytics in the same manner as the other components of the cloud-native application(s).
Partnerships and integrations with ISVs
Red Hat has strategic partnerships and integrations with key database and data analytics ISVs such as Microsoft, Cloudera, MongoDB, Crunchydata, Couchbase, Starburst, along with Red Hat’s AMQ Streams (Kafka on Kubernetes) using Kubernetes Operators to help make our mutual customers successful.
Simpler way to buy and deploy container-based software on OpenShift, including databases and data analytics workloads.
Complementary capabilities for efficiently running databases and data analytics on OpenShift.