Cloud Data Warehousing Benefits and Considerations

What is cloud data warehousing and how does it work?

A cloud data warehouse performs all of the functions you would expect of a traditional data warehouse, such as data processing, collation, integration, cleansing, loading, and reporting. But it does so within a public cloud environment. Examples include Microsoft Azure SQL Data Warehouse, Amazon Redshift, Teradata Autonomous Knowledge Platform, Google Cloud’s BigQuery, and Snowflake Cloud Data Platform.

Like its on-premises counterpart, an enterprise data warehouse deployed in the cloud is typically a relational database, focusing on structured and semi-structured data. This is the kind you'd see in various customer relationship management (CRM), enterprise resource planning (ERP), and point-of-sale applications, to name just a few. Unstructured data, meanwhile, is typically aggregated using a data lake framework, which can also be cloud-based.

At the most granular level, the majority of data stored in a warehouse is characterized as either facts, measures, or dimensions:

• Facts: Data points connected to specific events or transactions, e.g., "Compensated employee John Smith $4,000 for the month of $48,000 per year gross salary."
• Measures: Precise numbers connected to facts — based on the above example, one of the measures would be "Monthly pay: $4,000."
• Dimensions: These categorize facts and measures with more structured contextual information, such as "Employee name: John Smith" and "Direct deposit dates: Nov. 15 and Nov. 30."

A cloud database is distinguished by its versatility, and as such it can easily be multi-dimensional. In addition to its ability to easily manage many dimensions of both current and historical big data in a single venue, a modern cloud database can operate on serverless architecture, which can help minimize an enterprise's data management responsibilities. Alternatively, cloud databases may use the cluster-and-node approach, in which two or more physical servers are used.

Traditional vs. cloud architecture

Traditional enterprise data warehouses utilize on-premises, multi-tier architectures where organizations manage their own hardware provisioning, storage, and compute resources. Data flows through several layers before it becomes available for reporting and analytics.

Most traditional warehouse architectures follow a three-tier structure. At the base is the database layer that collects data from operational systems and other sources. The middle tier contains online analytical processing (OLAP) services that transform and organize data for analysis. At the top tier, reporting and analytics tools provide dashboards, visualizations, and query interfaces for business users.

Traditional setups range from centralized enterprise warehouses aggregating business-wide data to department-specific data marts or virtual warehouses that query multiple databases simultaneously.

Cloud data warehouses take a different approach. Instead of relying on fixed infrastructure, cloud data warehouses utilize elastic cloud platforms, allowing organizations to dynamically scale compute and storage capacity alongside growing data volumes. Cloud deployments typically fall into two broad architectural patterns: cluster-based systems, which use multiple nodes to process queries, and serverless systems, where the cloud provider dynamically manages compute resources.

Another key difference between traditional and cloud data warehousing lies in how infrastructure is shared or isolated across customers. Cloud providers commonly offer several tenancy models that balance performance, cost efficiency, and operational control.

Cloud data warehouse tenancy models comparison

Deployment Model	Architecture Description	Performance Characteristics	Cost Profile	Level of Control	Typical Use Cases
Single-tenant cloud data warehouse	Dedicated infrastructure for a single organization. Compute and storage resources are not shared with other customers.	High and consistent performance due to isolated resources and predictable workloads.	Higher cost because infrastructure is reserved for one tenant.	Maximum control over configuration, security policies, and resource allocation.	Highly regulated industries, sensitive workloads, and enterprises requiring strict performance isolation.
Multi-tenant cloud data warehouse	Multiple organizations share the same underlying infrastructure managed by the cloud provider.	Performance can vary depending on resource allocation and workload sharing across tenants.	Lower cost due to shared infrastructure and economies of scale.	Limited control since infrastructure management is handled primarily by the provider.	Organizations prioritizing cost efficiency, rapid deployment, and simplified operations.
Hybrid cloud data warehouse	Combines dedicated resources with shared cloud services or integrates on-premises infrastructure with cloud environments.	Balanced performance depending on which workloads run on dedicated versus shared infrastructure.	Flexible cost structure that allows organizations to optimize spending across environments.	Moderate to high control depending on deployment design and integration with existing systems.	Enterprises migrating from on-premises systems or running mixed workloads requiring both control and scalability.

Benefits of using cloud data warehousing for analytics

Amassing and collating all of those gigabytes upon gigabytes (and, eventually, terabytes) of data isn't about storage or operations. The insights it can reveal are capable of being the foundation for strategic development that drives growth and the bottom line — and they must be unlocked with analytics tools.

Running data analytics and reporting on a data warehouse hosted via a cloud solution is quite different from completing the same tasks for an on-premises warehouse. In fact, it's arguably one of the most exciting cloud computing trends in the enterprise world right now.

A cloud data warehouse delivers cost-effective scalability and elasticity for analytics workloads across single, multi-cloud, or hybrid deployments. Queries will run more quickly than they would in an on-premises warehouse, at a lower overall cost due to the lack of hardware overhead.

With the right data analytics engine for the cloud, you can give your organization the flexibility to craft and implement algorithms as sophisticated as your circumstances require, using the programming languages you're familiar with, such as SQL, Python, SAS, and R. Scalable analytics in this context brings to bear leading-edge machine learning processes, clustering and segmentation, sentiment parsing, text extraction, graphing, and geospatial or time series analysis.

Additionally, running data warehouse analytics in the cloud allows you to integrate with numerous data management services: Amazon EBS, S3, SageMaker, Glue, and Lambda, as well as Azure Blob Storage, Data Factory, ML Studio, and PowerBI, are just a few examples.

How to select and deploy a cloud-based data warehouse

First, you must consider whether a cluster-based or serverless warehouse architecture will be right for your organization's cloud deployment.

Clustered warehouses have more predictable pricing and allow more direct oversight, but the latter advantage comes at the cost of devoting more time and resources to managing elasticity, capacity, and cluster health. By contrast, serverless models are completely overseen by your CSP and elasticity is scaled automatically, but you pay either per query or based on utilization, which can be difficult to predict.

Pricing, in fact, may be the most complicated aspect of choosing a cloud data warehouse, regardless of model. One of the chief advantages of a strong cloud platform is its elasticity, but at times when data workloads are steady, you may come upon cost inefficiencies. Additionally, it's critical to monitor any costs associated with workflows that move data out of the cloud, as well as complicated budgeting and cost controls that can quickly spin out of control.

Last but not least, initial implementation of a cloud-based warehouse may come with slower-than-expected performance and require users to change their practices to accommodate this early hiccup.

Maximizing a cloud data warehouse requires an agile, multi-cloud data platform like the Teradata Autonomous Knowledge Platform. Its cloud service, Teradata Cloud, is purpose-built for enterprise workloads, and its Fixed + Flex pricing combines a committed fixed baseline with on-demand flex capacity. The platform supports cloud, on-premises, and hybrid deployments to process data where it resides, minimizing data movement.
 

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