Should you run your database on premises or in the cloud? Different types of database management systems explained

data warehouse as a service (DWaaS)

What is data warehouse as a service (DWaaS)?

Data warehouse as a service (DWaaS) is an outsourcing model in which a cloud service provider configures and manages the hardware and software resources a data warehouse requires, and the customer provides the data and pays for the managed service.

With DWaaS, an organization doesn't have to spend money upfront to buy data warehouse hardware and software and then install the system in its own data center. It also doesn't need to worry about managing the underlying system infrastructure or doing routine administration work on the database that's at the heart of the data warehouse. DWaaS vendors handle those tasks for customers.

DWaaS deployments are growing rapidly as more organizations shift from on-premises systems to cloud data warehouses. According to Emergen Research, the global cloud data warehouse market was estimated at $5.8 billion in 2022 and is expected to grow 22.3% in revenue from 2023 to 2032.

The increasing adoption of DWaaS environments is part of a broader move toward cloud databases overall. For data that's generated in the cloud, DWaaS is a more natural fit than an on-premises data warehouse. Data warehouses are often compared to data marts and data lakes, which serve similar purposes. Data marts are for housing and accessing data sets focused on specific subject areas and vertical markets, while warehouses have a much broader scope and data lakes are even broader.

DWaaS components

Cloud data warehouses are similar to in-house warehouse data models from an architecture and technology standpoint. With that in mind, the main components of a typical data warehouse implementation include the following items:

  • DBMS. A data warehouse requires a database management system (DBMS) to store, process and access the data it contains. Most commonly, data warehouses use mainstream relational databases that store data in rows, but they can also be built on columnar databases that use column-based storage. Because data warehousing is focused on write-once/read-many operations, using a columnar engine can improve the efficiency and performance of analytical queries. A relational DBMS that offers columnar database support is another alternative.
  • Data storage. Like the DBMS and the server hardware it runs on, data storage devices are provided as part of a DWaaS environment. A variety of storage options can be used, including traditional hard disk drives, solid-state drives and cloud object storage services.
  • Metadata management tools. Metadata characterizes data, providing documentation so data sets can be understood and more easily used. It answers the who, what, when, where, why and how questions for users of the data. Without metadata management capabilities, it's difficult to use a data warehouse effectively.
  • Data pipelines. Data warehouses are designed to support business intelligence (BI) and data analytics uses. The systems automate the movement of transaction data from operational systems into a data warehouse; the data also needs to be transformed to better organize and format it for analytical querying. Data integration tools that support extract, transform and load (ETL) processes are required DWaaS components. Other integration methods are usually supported, too. That includes extract, load and transform, an alternative to ETL often used with big data sets that are transformed for different analytics uses after being loaded into a warehouse.
  • Reporting and analytics tools. The primary purpose of a data warehouse is to enable data analysts and business professionals to glean actionable insights through analyses of operational data. BI tools that support querying, analytics and reporting functions against the data warehouse are a must.

All of the above can be provided and managed by the DWaaS vendor for the benefit of the user organization. But there are different methods of purchasing, installing and configuring the required hardware and software infrastructure to support a data warehouse in the cloud.

One approach is to deploy traditional data warehouse software on cloud infrastructure. This approach is similar to on-premises data warehousing. The expertise to build and manage the data warehouse resides with the customer, while the implementation and much of the ongoing support of the data warehouse system resides with the cloud platform provider.

On the other hand, a pure DWaaS approach relies on the platform provider or another data warehouse vendor that runs its software on a cloud platform to deliver a complete data warehouse environment. The DWaaS vendor also provides ongoing management of the data warehouse, including configuration, performance management and data integration support. Customers can scale computing and storage resources up and down based on their needs, and payments are based on the resources they use. System resources can be provisioned on demand as needed or reserved to get discounted pricing.

List of benefits of data warehouse as a service.
Businesses can reap many benefits from data warehouse as a service.

Benefits of DWaaS deployments

The benefits of DWaaS are similar to those of any cloud computing service, including easier deployment and reduced IT management responsibilities. For example, a database administrator responsible for a data warehouse no longer needs to install new releases of the database software being used, and an organization's IT team doesn't have to install, upgrade or replace the underlying hardware.

The potential benefits of using a DWaaS environment also include the following:

  • Lower costs. Overall spending on IT and data management is reduced because DWaaS eliminates the need for capital expenditures on hardware and software. It also decreases operating costs in on-premises data centers, making DWaaS a cost-effective option.
  • Easier scalability. DWaaS users can quickly add more data processing and storage capacity when necessary and scale their systems back down when resources are no longer required. In addition, that can be done without the need to add or upgrade hardware or to continually renegotiate contract terms and conditions.
  • Reduced staffing needs. Because the service provider does most of the administration and management, an organization doesn't need to add new workers to support a data warehouse. This makes DWaaS a good choice for organizations with small or limited IT departments, although cloud data warehouses can also handle mission-critical analytics workloads in large organizations.
  • Faster access to new software features. Instead of having to wait for a new release of a vendor's data warehouse software and then install it, as in on-premises systems, users can take advantage of software updates that DWaaS vendors make on an ongoing basis.

DWaaS also offers the same general benefits as on-premises data warehouses, including expanded access to different types of data from multiple sources for end users and improved data quality with better accuracy and consistency. Ultimately, that can lead to more effective BI and analytics applications to help drive better business insights and decision-making.

DWaaS challenges and considerations

As with any cloud-based offering, performance and availability are primary considerations for DWaaS users. Because these systems run in the cloud, they require a reliable internet connection for users to access the data warehouse. If connectivity is impaired or lost, the system might perform poorly or be unavailable. Customers also have to rely on the DWaaS vendor to manage performance and ensure high availability. Service outages similarly affect cloud-based data warehouses.

Latency can also be an issue on DWaaS implementations. The following two aspects of DWaaS latency must be considered and managed:

  • The delay in getting data from operational systems into the data warehouse, which is a data integration issue.
  • The delay in accessing data once it's in the data warehouse for querying and analysis.

The amount of data that must be moved from operational systems to the data warehouse is the primary latency factor when attempting to integrate data. Typically, the more data that must be added, the longer it takes to migrate from the data source into a DWaaS environment. Similarly, analytical queries that return large amounts of data are most at risk for data latency issues.

Another DWaaS challenge is to mitigate vendor lock-in. It isn't always easy to move from one DWaaS provider to another because every offering is different. As such, it's wise to choose a DWaaS system with underlying components that your IT and data management team is knowledgeable about to help preserve your ability to migrate to another provider at a future point in time.

In addition, organizations might have concerns about data security, regulatory compliance and risk management in a DWaaS environment. Cost can also become an issue if use of a cloud data warehouse exceeds expectations or if unneeded system resources aren't identified and removed.

DWaaS vs. on-premises data warehouses

Businesses have the option of building on-premises data warehouse infrastructure. This approach lets them circumvent certain DWaaS challenges such as vendor lock-in. However, the on-premises route has its own challenges. Assembling all the components needed for an on-premises data warehouse takes time and requires significant investment. It also requires a team of skilled employees. DWaaS allows businesses to sidestep the time, money and human resources needed to set up a traditional warehouse infrastructure with quick deployments.

DWaaS is also more scalable than a physical data warehouse, enabling businesses to quickly meet demand for increased data volumes. An on-premises approach requires a much larger effort to scale and meet ever-increasing data needs.

Finally, real-time data ingestion and analytics are easier with DWaaS. When the data pipelines, storage and computing power needed to collect and analyze data are condensed in a single location, there are limits on how much real-time activity can occur.

DWaaS use cases

DWaaS platforms have permeated various industries as more businesses recognize their practicality, particularly in facilitating data analysis tasks. Some of the most common industry-specific use cases for DWaaS platforms include the following:

  • Finance. Financial institutions use DWaaS to analyze massive data sets for market and customer trends as well as assess risks. For example, stockbrokers can benefit from these analyses, especially when conducted in real time for up-to-the-minute information.
  • Healthcare. DWaaS in the healthcare industry enables trend research and can predict future outcomes related to patient health. Additionally, DWaaS tools manage data that's readily used to create treatment reports.
  • Retail. DWaaS tools are used in retail and other commercial businesses to analyze customer buying trends and forecast industry trends. Other uses include tracking items as they are moved throughout a store.

Top DWaaS vendors and technologies

DWaaS vendors include the leading cloud platform providers -- Amazon Web Services, Google Cloud, Microsoft and Oracle -- and other makers of open source cloud data warehouses that use one or more of those platforms to run their software.

The following technologies are some of the prominent DWaaS offerings available to organizations:

  • Amazon Redshift.
  • Firebolt.
  • Google BigQuery.
  • IBM Db2 Warehouse.
  • Microsoft Azure Synapse Analytics.
  • Oracle Autonomous Data Warehouse.
  • Panoply.
  • SAP Datasphere.
  • Snowflake.
  • Teradata Vantage.
  • Yellowbrick Data Warehouse.

Business users in industries such as healthcare and finance make more use of enterprise data warehouse services and frequently have the budgets to cover the cost of the technology. Smaller businesses typically have less need for DWaaS and often can't afford these services.

DWaaS vs. data lakes vs. data marts

Businesses can choose to use DWaaS, data lakes or data marts to store and manage data. Their specific needs determine which is the right approach. There are key differences that potential users should know.

Data lakes

DWaaS services may be broad in scope, but data lakes are broader in accommodating data formats ranging from structured and semistructured data to raw and unstructured data. DWaaS services are geared toward structured data sets that businesses require easy access to for analysis. A data lake, on the other hand, serves as a repository for an array of data sets and artifacts that could serve some purpose in the future.

Data marts

DWaaS services provide repositories that are used collectively by all departments within an organization. A data mart is tailored to a specific department's needs and areas of expertise. For example, a sales department can store data on customers, products and sales performance in a data mart for easy access and analysis in a way that would be less efficient when stored in a data warehouse.

As data warehouse platforms improve over time, traditional data warehouses could become obsolete. Learn why and how data warehouses should be modernized.

This was last updated in February 2024

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