In a world where the distributed workforce has become a common model for many enterprises — first as a result of the COVID-19 pandemic, then as a trend increasingly popular with employees and business leaders alike — the cloud has become the epicenter of many essential corporate operations. Big data, meanwhile, serves as those operations' foundations, which necessitates cloud data management: the control and orchestration of data across cloud deployments, using a purpose-built platform and services.
Effective cloud data management is critical to comprehensive data governance. Here, we'll examine the fundamentals of data oversight in a cloud computing environment and review potential pros and cons of management processes. We'll also discuss key best practices and look at how a leading-edge cloud data management platform can help optimize performance.
What is cloud data management?
The aim of cloud data management is to ensure consistent governance of all enterprise data by leveraging one cloud platform or spreading the responsibility across multiple cloud deployments. This may be accomplished entirely through the use of cloud services or in conjunction with an organization's on-premises data center and other related infrastructure.
The broad concept of management encompasses a variety of tasks. Sometimes, it's just a matter of migrating data from on-premises data sources to the cloud. In other instances, data integration, archiving, tiering, replication, or protection — or some combination thereof — is necessary to ensure that critical data is stored and categorized exactly as it should be, regardless of whether it originated in the cloud or on-premises.
An enterprise that adopts a cloud-centric or cloud-first data management strategy can oversee the flow of workloads and other essential application data by predominantly relying on on-demand resources available in the cloud. Allocating the resources needed for this purpose is fairly simple. It's just a matter of spinning up virtual machines in the cloud or other appropriate resources to handle spikes in data traffic, and phasing them out once they're no longer needed.
This is a significant contrast from upscaling such resources in an on-premises data management model. New hardware or software often has to be purchased to scale up, and those tools can't simply be returned if you later learn that you didn't ultimately need them.
Benefits and potential challenges of cloud-based data management
It's possible to realize numerous advantages by turning to the cloud as your enterprise's primary instrument for data management and storage.
Keeping all enterprise data under the umbrella of a cloud database management system (DBMS) allows for easy access and oversight, no matter whether data is on-premises or off-premises. This integrated approach to data is an example of the single source of truth (SSOT) framework. It helps eliminate problematic inconsistencies and redundancies, and prevents data from becoming siloed or otherwise isolated. The cloud is also ultimately much more cost-effective for data management because you pay for the resources you use and don't have to worry about buying servers that don't get used.
The flexibility that the cloud's scalability and elasticity offer—features that are pivotal to many cloud computing trends—can make cloud data management even more beneficial. Unlike what is necessary when data is strictly on-premises, enterprises don't have to worry about completing Olympic gymnast-level feats of capacity planning when centralizing data governance in the cloud. The cloud service providers that manage their deployments can provide near-limitless storage and compute resources whenever upscaling is required, and also handle virtually all management tasks. This makes the cloud much more cost-effective than relying solely on an on-premises data management framework. Last but certainly not least, many major providers offer security features and automated backups for disaster recovery.
Although long-term cost efficiency can be realized with cloud data management systems, there are also ways in which it quickly becomes expensive. It's typically free to migrate data to the cloud, but removing it and bringing it back to on-premises—or transferring it to another cloud—racks up data egress fees. Cloud data management operations may also disrupt on-premises file-based applications.
Additionally, providers' native security features may be insufficient for very sensitive internal or customer data or in jurisdictions subject to strict privacy regulations, such as the EU. Along similar lines, despite the cloud's ability to back up data, it may still be necessary to maintain on-premises backups as an emergency safeguard.
Last but not least, a cloud data management strategy that doesn't include appropriate data governance will limit enterprises' ability to leverage the full value of their data. The lack of a uniform governance framework impedes data initiatives by inevitably leading to a host of performance bottlenecks, inconsistencies, and redundancies, which data professionals must address before returning to their primary responsibility.
Essential features of cloud data management services and tools
Any cloud-centric data management strategy must include analytics. You need single-pane-of-glass visibility into all cloud-stored and on-premises data, and that information has to be quantified, reported on, and properly leveraged. Understanding the usage, operational costs, and layout of that data is just the beginning, as a robust, agile analytics engine will also help unlock critical actionable insights to drive strategy across the enterprise.
You must establish thorough control over the migration process and facilitate integration across all data sources. This includes establishing and maintaining consistent access permissions across on-premises and the cloud, as well as ensuring security in all locations. The ability to set up policies that automate certain processes, such as data archiving, replication, and tiering, makes for a valuable efficiency boost. Thorough governance must also be established by data team leaders, with data resources made flexible and scalable within cloud-enabled infrastructure and efficient data design patterns. This helps ensure end users and application developers have access to the necessary data for high-priority business initiatives.
The tools you use for cloud data management, ranging from analytics solutions to data warehouses and lakes, should be as flexible as the cloud itself. For example, you should have the assurance that your enterprise's data should flow just as smoothly, and be just as accessible, whether you use a hybrid cloud or multi-cloud deployment model. Similarly, these technologies should aid you in finding the right balance between storage and compute in your cloud environment, while keeping those functions decoupled for greater cost-effectiveness.
Leveraging Teradata Vantage for optimal cloud-first data management
The enterprise-level business universe is cloud-first now — and it's quite likely to remain cloud-first forever. Your data ecosystem must be structured and arranged to account for this evolutionary shift, with accessibility and integration standing out as the most critical aspects of data management.
Teradata Vantage is the modern cloud-first data analytics platform that every organization needs to achieve key data goals. It unites data management and analytics in one dynamic solution that offers remarkable flexibility and portability. Vantage is compatible with multi-cloud, hybrid cloud, and single-tenant on-premises setups, and easily integrates with key services from AWS, Microsoft Azure, and Google Cloud. The platform is also scalable across multiple dimensions. Through elastic resource allocation, you never have to fear being overwhelmed by surges in high-priority workloads. Insights Vantage offers based on data from all sources can aid strategic future planning to determine your organization's direction.