Why Databases Are Moving to the Cloud
A greater comfort with cloud deployment is emerging. Businesses that are migrating databases to the cloud or building databases there are usually doing so as a result of one of the factors outlined in this article.
- By William McKnight
- October 26, 2016
One fact circulated last month at Teradata Partners was that 90 percent of Teradata customers believe they will be on some form of hybrid cloud by 2020 and 40 percent of workloads will be run there.
Teradata certainly believes this; it has rallied around that market direction with "Teradata Everywhere" initiatives that put the database on a large variety of platforms, especially in the cloud. Teradata is now available on Amazon Web Services, Azure (soon), Teradata Managed Cloud, VMware, and the future of Teradata, Intelliflex.
In the past, the only clear choice for most organizations was on-premises data -- oftentimes using an appliance-based platform. However, the costs of scale are gnawing away at the notion that this remains the best approach for all (or even some) of a company's analytical needs.
4 Factors Leading to More Cloud Databases
A greater comfort with cloud deployment is emerging. Businesses that are migrating databases to the cloud or building databases there are usually doing so as a result of one of these factors.
1: Scaling the Appliance Model Has a High Cost
Though appliances bring power and familiarity, the business case to scale up with additional appliances (for example, to expand storage, analytics workload capacity, or disaster recovery) is sometimes difficult to sell based on its finances. Cost goes beyond the purchase price to include maintenance, real estate needed, and necessary in-house talent.
2: The Cloud Is Becoming the "Center of Gravity"
Companies are sharing and accessing data in the cloud at an unprecedented pace. Organizations that are pulling data down from the cloud, integrating it, transforming it, and storing it on premises are moving the heavier data load to the analytics, rather than the preferred model of moving the analytics to the data.
3: On-Premises Databases Are Reaching Capacity
The dreaded expansion conversation these days quickly considers the cloud. Non-functional requirements (NFRs) such as the separation between development and testing, QA and production, and disaster recovery are being ruggedized and modernized and this means additional environments. The cloud is finding a strong value proposition in supporting NFRs.
4: Emerging Use Cases Are Specific to Cloud
There are many emerging technical use cases that are exclusive to the cloud model. Separating compute nodes from storage nodes allows you to independently scale compute and storage in the likely event you need more of one than the other. Immediate elasticity gives you maximum flexibility and serenity as your workloads grow and shrink seamlessly.
Considering these factors (and potentially others), organizations are turning to the cloud -- some stepwise and some en masse -- to mitigate the challenges they face managing and maintaining an expanding data ecosystem.
Considering Full-Cloud, Secondary-Cloud, and Hybrid-Cloud Deployments
Many enterprises are fully embracing a cloud-based enterprise data warehouse and making it accessible across their current infrastructure and data ecosystems. Many others are adopting the cloud as a secondary platform for a specific purpose such as disaster recovery. An active secondary platform also serves as an accessible backup system should it be needed.
Some organizations have already moved to cloud-based solutions of various scales and varieties. Others are taking a stepwise approach and pivoting components of their architecture to accommodate the cloud, thereby creating a modern, cost-effective, and scalable hybrid architecture.
Either way, the cloud's price/performance value proposition is now very strong for databases and should be given priority consideration for new or renewed data efforts. Don't let the lack of an enterprise cloud strategy be a barrier. Start it with the data. Start it with you.
McKnight Consulting Group is led by William McKnight. He serves as strategist, lead enterprise information architect, and program manager for sites worldwide utilizing the disciplines of data warehousing, master data management, business intelligence, and big data. Many of his clients have gone public with their success stories. McKnight has published hundreds of articles and white papers and given hundreds of international keynotes and public seminars. His teams’ implementations from both IT and consultant positions have won awards for best practices. William is a former IT VP of a Fortune 50 company and a former engineer of DB2 at IBM, and holds an MBA. He is author of the book Information Management: Strategies for Gaining a Competitive Advantage with Data.