Big Data Evangelist, Strategy and Marketing, Database Software and Systems, IBM Software Group
Big Data is at the heart of many cloud services deployments. As private and public cloud deployments become more prevalent, it will be critical for end-user organizations to have a clear understanding of Big Data application requirements, tool capabilities, and best practices for implementation.
Big Data and cloud computing are complementary technological paradigms with a core focus on scalability, agility, and on-demand availability. Big Data is an approach for maximizing the linear scalability, deployment and execution flexibility, and cost-effectiveness of analytic data platforms. It relies on such underlying approaches as massively parallel processing, in-database execution, storage optimization, data virtualization, and mixed-workload management. And cloud computing complements Big Data by enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction.
The Cloud Standards Customer Council recently published a whitepaper providing practical guidance on how best to deploy new and existing Big Data analytics applications to the cloud. The paper, available as a free download, is organized to guide you systematically through the key steps:
- Build the business case
- Assess workloads
- Develop a technical approach
- Address privacy, security, and compliance
- Deploy, integrate, and make production-ready
If you’re already doing any of this, the strategic question on cloud-based big data is not about where you start. As cloud-based big data services mature and continue to improve in price/performance, scalability, agility, and manageability, the real question will be how you plan to evolve your deployment to address new requirements while preserving interoperability with legacy investments.