What is data warehouse as a service (DWaaS)?

Data warehouse as a service is a managed cloud service model that allows organizations to gain the insights, data consistency, and other data benefits of a data warehouse without having to build, maintain, or manage its infrastructure. With DWaaS, the cloud service provider is responsible for setting up, configuring, managing, and maintaining the hardware and software resources for the data warehouse.

As data becomes more diverse and increases in volume and velocity, the need for data warehouse modernization has become inevitable. On-premises data warehouses have been a staple of enterprise business intelligence (BI) for years, but they also come with significant hardware and software costs and require ongoing maintenance.

Data warehouse cloud services collect, store, and process data, allowing organizations to address cloud data management needs and easily access critical data. They also instantly scale up or down based on real-time demand, making them more cost-effective than their on-premises counterparts. At the same time, DWaaS solutions minimize the upfront investment and administrative overhead normally associated with traditional data warehousing. Customers are only responsible for providing the data and paying a fee for the managed service.

Enterprise data warehouse services like BigQuery are helping power data-driven innovations in every industry, providing built-in machine learning and BI capabilities across clouds that easily scale to meet data needs.

Get started for free

Key components of a cloud DWaaS offering

Cloud-based data warehouses have similar components to on-premises solutions. A cloud data warehouse implementation includes the following key components:

Data warehouse: DWaaS offerings provide a data warehouse architecture that can be configured based on your organization’s specific business needs and existing data sources, data management strategy, and data quality processes. Cloud data warehouses include analytical databases, centralized data storage, and other critical capabilities for queries, resource and data management, and data access management. With DWaaS, there’s no need to provision storage resources or reserve capacity. It is automatically allocated when you load data in the system.
Data integration tools: Since data warehouses collect data from enterprise business systems, DWaaS must include components that support ETL (extract, transform, load) and ELT (extract, load, transform) processes and other forms of data integration.
Reporting and data analytics: DWaaS solutions come with features to support powerful query processing and built-in tools for BI, advanced analytics, machine learning and AI, and reporting. These tools are key for helping organizations glean business insights from their enterprise data through data mining, statistical reporting, spatial analysis, and more.

Benefits of using a DWaaS system

Here are some of the main benefits organizations get from transitioning to data warehouse as a service:

Easy setup and configuration

DWaaS solutions simplify implementation, making it easy to get started fast. There’s no need to deploy any infrastructure for data storage—or have a specialized team in place to configure, manage, or maintain it.

High scalability

With DWaaS, you can allocate resources on demand. This allows you to quickly scale up to add more capacity for data processing and storage, or scale down when those resources aren’t needed.

Lower costs

With database as a service models, the service provider carries out most of the administration and management. As a result, DWaaS solutions not only lower upfront hardware and software costs, they also drastically reduce the resources needed to maintain data warehouse infrastructure.

Improved security

Data warehouse as a service providers are responsible for regularly updating hardware and software, which helps eliminate the threat of security vulnerabilities related to delayed updates. Certain providers also offer additional data security features, including data encryption, multi-factor authentication, and more.

Accelerated insights

DWaaS is optimized to run on cloud infrastructure, accelerating query processing and providing continuous improvements that enhance performance. This ultimately speeds up the delivery of data, improving the time it takes to get valuable insights.

Customized for you

Managed cloud data warehouse services enable you to create a data environment that can adapt and evolve according to your data sources, changing business requirements, and overall long-term goals.

Why BigQuery is a safe choice for a DWaaS provider

BigQuery is Google Cloud’s fully managed, serverless, multicloud enterprise data warehouse solution. With no infrastructure to set up or manage, you can rapidly analyze complex datasets, transform data into actionable insights, and adopt AI and machine learning to help power real-time decision-making that drives innovation.

In recent years, data warehousing has become a complex task as companies work to gain insight into business operations. Beyond applying descriptive analytics to ever-increasing quantities of stored enterprise data, organizations are adopting technologies like AI and ML to extract data patterns and make predictions.

While it’s possible to build a scalable, highly available, and secure data warehouse in-house, it can take years and significant investment to make it happen—a luxury that most organizations don’t have. BigQuery is designed for serverless data warehouse operations and eliminates the burden of infrastructure maintenance and platform development, so you can focus on developing prescriptive analytics that help guide actions in real time. You can use SQL queries to answer your most pressing questions, querying terabytes in seconds and petabytes in minutes. It also has built-in features that make managing and analyzing your data easier, from machine learning to geospatial analysis to business intelligence.

With data volumes increasing by the second, it’s more critical than ever to ensure that you have the storage and processing resources needed to get value from it. BigQuery provides scalable, flexible, and cost-effective access to structured data storage, powerful processing, and advanced analytics capabilities like machine learning across all data typesstructured, semi-structured, and unstructured.

BigQuery separates compute for analyzing data from your storage, enabling you to scale compute and storage independently based on demand. You can analyze data directly in BigQuery or use it to analyze data where it resides, including across clouds. BigQuery supports federated queries from external data sources and streaming for continuous data updates.