Simplifying Data Management with ROBIN Hyper-Converged Kubernetes Platform and DataStax – White Paper
EXECUTIVE SUMMARY Enterprises across industries struggle to manage massive quantities of data and data entering systems at a high velocity. While NoSQL has emerged as a solution in some cases, many applications still rely on a relational database management system (RDBMS). To further complicate the management of these systems, the NoSQL space has been one of rapid change with many offerings emerging. While data scientists, architects, and developers are choosing the system that best matches their uses cases; it’s the administrators that are forced to manage all of these complex systems and meet business SLAs.
Robin Systems has teamed up with DataStax so that administrators can deploy DataStax Enterprise (DSE) on Robin’s application-aware infrastructure software which is optimized for container technologies. DataStax Enterprise is the best distribution of Apache Cassandra™ and also includes developer tooling, administration and monitoring, search, operational analytics, and graph — all in a unified, always-on data platform. By working with ROBIN Hyper-Converged Kubernetes Platform , an administrator can now also achieve:
- Productivity improvement by simplified operations and user experience
- Cost reduction by guaranteed performance, even in shared multi-tenant environments, to enable hardware consolidation
- Risk reduction by repeatable and automated processes such as 1-click cluster provision and patch/upgrade
- Agility optimization with full Application & Data Lifecycle Management with significantly reduced time and storage.
A PARTNERSHIP TO ENABLE EFFICIENT MANAGEMENT OF DSE RCP is a container-based, application-centric, server and storage virtualization platform software which turns commodity hardware into a high-performance, elastic, and agile big data & database consolidation platform. RCP is designed to cater to not just stateless applications, but also to performance and data-centric applications such as DataStax Enterprise Clusters. DataStax administrators were facing the following challenges:
- Low Server Utilization -Underlying hardware has to be sized for peak workloads, leaving large amounts of spare capacity and idle hardware due to varying load profiles.
- Sizing Production Workloads During Development – To accurately size environments an organization must estimate the read and write performance that is expected from the designed configuration. This requires testing and experimentation, and yet the infrastructure might be over or under-provisioned.
- Availability Planning – While Cassandra is designed to withstand temporary node failures, permanent node failures require resolution by addition of replacement nodes which causes additional load on the remaining active nodes.
- Cloning Data for Dev/Test Environments – Typical scenarios where a subset of the production data is required are – for debugging bugs, performance and stress testing, split read workload across multiple clusters, etc. DataStax does not have an automated way to clone a subset of production data.
- Scaling Out vs Up – Administrators need the ability simply add and remove resources (scale up or down) dynamically to their cluster, in real- time, to deal with temporary load variations.
- Patch & Upgrade Automation – Administrators have to periodically orchestrate updates across all nodes of Cassandra without any downtime, and be able to rapidly revert changes in case of failures.
RCP dramatically simplifies application and data lifecycle management with features such as one-click database deploy, snapshot, clone, time travel, dynamic IOPS control, upgrade and performance guarantees.