Data-driven insights have been hailed as a differentiator, one that may render companies obsolete if they are unable to take advantage of this trend. With this realization, business and IT leaders are keen to get started quickly in order to exploit the capabilities of big data as a competitive differentiator in their industries. However, businesses often struggle to find resources with expertise in big data technologies. According to research conducted by TechRepublic, 73% of the big data adopters experienced difficulty in finding qualified employees to lead these initiatives.
The shortage of technical expertise not only makes it difficult for big data projects to get off the ground, they also force organizations to allocate their valuable IT resources to the platform rather than the critical business use case. The true value of big data is realized when businesses build data applications that help them reduce risk, identify new sources of revenue or reduce the cost to improve their operational efficiency. If a significant portion of a company’s big data program resources are dedicated to building and maintaining big data infrastructure that means the business cannot build those value-added data applications as quickly.
Companies are increasingly moving mission-critical applications to big data and Apache Hadoop environments where reliability and uptime are of paramount importance. Combined with the fact that open source projects such as Hadoop and associated processing engines innovate at a fast pace with frequent release cycles compounds operational complexity.
Hortonworks newly launched subscription service, Hortonworks Operational Services is designed to simplify big data deployments and bring projects to production faster. The service abstracts away the complexity of building, deploying and managing big data implementation by letting Hortonworks experts manage critical platform tasks leaving organizations to focus on their critical business applications. Customers can now focus their resources on building value-add data applications, while Hortonworks expert support teams focus on managing and maintaining the platforms.
With Hortonworks Operational Services, organizations can be confident of the operational continuity of their HDP and HDF production environments through 24x7x365 management coverage with monitoring and incident management. Hortonworks Operational Services also ensures the reliability of their big data environments by applying the latest upgrades and patches, hotfixes, and the latest configuration best practices.
Hortonworks is uniquely qualified to deliver this service to customers. As a leading provider of big data platforms – HDP and HDF, Hortonworks has extensive experience building, managing and supporting the entire Apache Hadoop stack at scale and in the most demanding production environments. Hortonworks teams of support personnel have visibility into best practices across industries to operate and optimize the cluster performance and support a wide variety of use cases and data-driven applications.
It is common for companies to underestimate the importance of tasks related to platform management and maintenance as they embark on their Hadoop journey. The difficulty in acquiring in-house expertise on each of the individual big data technologies, as well as the required resources to ensure interoperability and upgradeability of the platform, can lead to delays in achieving the business value associated with adopting big data technologies. It is critical for businesses to focus on building the applications that will provide them with competitive advantage rather than investing in plumbing or the platform. Hortonworks Operational Services empowers organizations to embark on their big data journey with confidence and utilize their valuable resources in a way that give them the competitive advantage that they are seeking from data-driven insights.
To learn more about Hortonworks Operational Services, visit https://hortonworks.com/services/support/operational-services/ or talk to your account executive.