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What’s data-warehouse-as-a-service (DWaaS)? Definition, key functions and solution providers

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What’s data-warehouse-as-a-service (DWaaS)?

With the quantity of enterprise data growing at a breathtaking pace (e.g., IDC projects a 23% CAGR to 175 zettabytes by 2025), the adoption of modern data infrastructure is becoming inevitable. Companies of most sizes and sectors are inevitably adopting far better data solutions.

These organizations have to consolidate business data from multiple source systems for historical and trend analysis. That’s where data warehouses can be found in, enabling firms to help keep organized and clean business data within an aggregate summary form (primarily structured data that fits into rows and columns).

Once the requirement would be to handle structured data for a predefined business purpose, a data warehouse sometimes appears because the go-to choice. However, building and maintaining a data warehouse is fairly an activity. With the quantity of data growing continuously, organizations must scale the storage and compute components of their on-premise warehouse accordingly. This not merely takes a considerable investment, but additionally creates administrative overhead with a team keeping an eye overall infrastructure to help keep it ready to go while ensuring security and compliance.

The task, which acts as a significant roadblock for small companies, has been addressed with a cloud-based data-warehouse-as-a-service or perhaps a DWaaS model. In itl, something provider is in charge of establishing, maintaining, securing and upgrading a data warehouse filled with the handling of most associated software and hardware stacks. The client only must be worried about plugging in the info sources they would like to hook up to the warehouse and spending money on the managed service.

Key functions of a DWaaS offering

When an enterprise opts for a data-warehouse-as-a-service offering, it’ll get a few key services from the provider. However, it could choose more inclusive elements aswell. The set of basic services includes the next:

Data warehouse design and development

An organization providing DWaaS services first configures a custom data warehouse architecture for the client by considering its unique business requirements, existing data management strategy, data sources and quality practices. After the custom framework is ready and future-proofed (for aspects such as for example scalability), it works toward implementing it by selecting the best option hardware and software systems and processes.

Integration with sources

After configuring the custom data warehouse, the provider works toward integrating it with all existing data sources, like the transactional systems of the client. Based on the case, owner could leverage leading pipeline technologies or custom code to make sure high-integrity transfer of data to the warehouse. Some providers also integrate the warehouse with existing analytical solutions for in-house analytics.

Data cleaning and migration

Once integrated, the info from the connected data sources is merged, cleansed, enriched and regularly tested for accuracy, completeness and compliance with the core data model. The cleansed information is used in the cloud platform chosen by the client, however, many providers also support hybrid strategies, whereby some data is maintained on the clients premises plus some in the cloud.


After the warehouse is ready to go, the company performs the housekeeping of maintaining data quality, adding and removing sources and checking performance along with extract, transform and load (ETL) correctness every once in awhile. The provider means that the complete service from the info model to infrastructure is made in compliance with privacy, security and governance standards.

Continuous evolution

While maintaining the info warehouse, the provider keeps a watch on changing business needs and data sources to ensure the complete data environment receives regular upgrades, whether in software, compute or storage.

Top data-warehouse-as-a-service solution providers in 2022

With DWaaS solutions, numerous vendors supply the great things about data warehousing without requiring the clients to bear the strain of setup and maintenance. However, in accordance with comments from customers provided to G2 and Gartner, just a few players have made a strong-enough mark to be categorized as leaders.

Snowflake Data Cloud

Operating across multiple clouds, including AWS and Azure, the Snowflake Data Cloud provides warehousing capabilities with full relational database support for both structured and semi-structured data. It separates storage, compute and cloud services into different layers, permitting them to change and scale independently. In addition, it automates key maintenance aspects such as for example query caching, planning, parsing and optimization along with update processing. Globally, a lot more than 5000 companies use Snowflake Data Cloud to mobilize their data for artificial intelligence (AI) and analytics.

In accordance with customer ratings, the platform meets user requirements and sticks out in every categories, beginning with simple deployment, administration and used to aid quality, scalability, integrations and pricing flexibility.

Amazon Redshift

Being an AWS product, Amazon Redshift offers a fully managed and scalable cloud data warehouse which allows enterprises to perform complex analytical queries on terabytes to petabytes of data stored in S3 buckets. It operates by provisioning clusters of nodes, with each node providing CPU, RAM and storage for just one or even more databases. As warehousing needs evolve, clusters could be provisioned or de-provisioned manually in Redshift to scale up or down accordingly.

Redshift is nearly at par with Snowflake but falls behind in areas like quality of end-user training and option of third-party resources, in accordance with user feedback on Gartner.

Google BigQuery

BigQuery may be the fully managed data warehouse offering from Google. It includes serverless architecture, supported by automatic provisioning, and built-in features such as for example streaming data support, machine learning and geospatial analysis. In accordance with Google, BigQuery separates computing and storage for enhanced flexibility to scale and allows developers to utilize client libraries with familiar programming, including Python, Java, JavaScript, and Go, to transform and manage data.

The answer also enables centralized management of data and compute resources with tools for identity and access management. According to G2 ratings, customers using BigQuery reported they faced issues with deployment, use and support areas of the answer.


Like Google, IBM also offers a fully managed, elastic cloud data warehouse that delivers independent scaling of storage and compute using its IBM Db2 solution. The offering carries a highly optimized columnar data store, actionable compression and in-memory processing to accelerate analytics and machine learning. Plus, it automates maintenance tasks such as for example monitoring, uptime checks and backups.

The issue areas of the perfect solution is are also much like that of Googles BigQuery where users reported that they had faced problems with the solutions setup, deployment, use and quality of support provided.

Microsoft Azure Synapse Analytics

Azure Synapse Analytics includes data integration, warehousing and analytics capabilities to supply enterprises with a unified workspace to ingest, prepare, manage and serve big data for AI and business intelligence (BI) use cases.

The answer gives data professionals the freedom to query data using either serverless or manually provisioned resources. Additionally it is among the leading players in the area because of near-limitless scaling of storage and compute resources, a deeply integrated SQL engine, native integrations with Power BI and Azure ML and advanced usage of data controls.

Leading enterprises, such as for example Walgreens, Co-op, Marks and Spencer and GE Aviation, currently use Azure Synapse Analytics. In accordance with Gartner ratings, the issue areas here have already been pricing models and customization.

Other notable players in the category are SAP, Oracle, Yellowbrick, Cloudera and Teradata. Overall, the marketplace for DWaaS solutions is likely to grow 20% from $1.44 billion in 2020 to $4.3 billion by 2026.

The surge, in accordance with Mordor Intelligence, will primarily be driven by the growing interests of companies to comprehend the available information regarding business processes, products, customers and services to seize home based business opportunities.

Read next: Google Cloud federates warehouse and lake, BI and AI

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