Growing Popularity Cloud data warehouse solutions increasingly popular because, historically, the only clear choice for most organizations was on-premises data, often using an appliance-based platform. Now the costs of scale are gnawing away at the idea that this is the best approach for all or some of a company’s analytical needs.
 

Scaling Challenges

Modern appliances deliver power and familiarity, but budget constraints make it difficult to scale up additional appliances to expand storage, analytical workload capacity, or disaster recovery. Purchase and maintenance costs for an additional appliance can hinder information management and analytical capabilities. Other concerns are the real estate needed for additional appliances, complexity, and in-house talent needs. Still, the business need for data creates a need for warehouse appliance capacity.
Growing Data Volume and Sources
Significant amounts of important data are sourced from the cloud. Other valuable analytical data are born in the cloud. The sheer mass of data “out there” rather than “in here” causes data gravity to shift externally, so organizations pulling data from the cloud—integrating, transforming, and storing on-premises—move the heavier data load to the analytics, rather than just take the analytics to where the data lives.

Improved Economic Options

The cloud now offers attractive options with better economics such as pay-as-you-go, which is easier
to justify and budget, improved logistics, and better scale (elasticity and the ability to expand a cluster within minutes).
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Multiple Adoption Possibilities

Many enterprises fully embrace cloud data warehousing, making it accessible across their current infrastructure and data ecosystems. Others adopt a secondary platform for disaster recovery and offloading analytic and data transformation workloads.

Organizations studying cloud-based information management solutions should consider:

Change Data Capture

Data staging and change data capture processes

Data Quality

Quality assurance, data validation, profiling, and data quality

Error Handling

Exception handling processes, load job restarts, rollbacks, audits, and data traces

Level of Service

Data availability service-level agreements and windows for extraction

Data Protection

Security, privacy, and encryption

Emergency Management

Disaster recovery

A Secondary Platform Many enterprises are adopting a secondary platform for purposes such as disaster recovery and offloading analytic and data transformation workloads. The EDW can serve as an asset, coexisting and spanning both on-premises and in the cloud—whether public, managed, or private cloud. In some cases, organizations consider load balancing by moving integration or analytic workloads off on-premises EDWs to an EDW in the cloud. With a hybrid architecture, a company has many choices and doesn’t have to abandon one in favor of the other.

Cloud Bursting to Dynamically Scale

Teradata has a robust hybrid cloud capability and offering. Customers can use cloud bursting to dynamically scale capacity among multiple EDW environments on-premises or in the cloud.

A More Manageable Jump

Data synchronization is the key to making most hybrid cloud use cases work. A hybrid cloud architecture allows organizations to enter cloud-based data warehousing to solve workload, performance, compliance, and disaster recovery.
CLOUD DATA WAREHOUSE SECURITY Cloud computing revolutionized the way organizations manage their data. Along with this accelerating trend comes questions about security. Executives want to leverage as-a-service offerings to take advantage of agility and on-demand consumption—and at the same time there may be anxiety about entrusting core elements of IT infrastructure to an external provider.
CLOUD SECURITY COMPLIANCE A cloud data warehousing service architecture should comply with strict international standards such as Cloud Security Alliance (CSA), General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), International Standards Organization (ISO) 27001, Payment Card Industry (PCI), and Service Organization Control (SOC) 1 and 2.
  • Cloud Warehousing and Business Outcomes

Customer Experience

Analyze big data to help deliver relevant, personalized experiences in real-time, influence customers’ journeys to align with desired business outcomes.

Quality Assurance

Radically improve product quality and enable continuous yield improvement to achieve zero accident/zero defect manufacturing.

Operational Excellence

Manufacture on demand, assemble to order and make to stock efficiently and responsively by creating adaptive manufacturing capabilities.

Production Innovation

Companies must create new products, improve existing ones, and manage multiple product lifecycles while addressing product and supply chain issues.

  • Customer Experience
  • Quality Assurance
  • Operational Excellence
  • Production Innovation

Ready to mitigate the challenges of managing and maintaining an on-premises data ecosystem?

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