The volume of enterprise data is growing at an incredible pace (e.g. IDC projects a 23% CAGR up to 175 zettabytes in 2025). the adoption of modern data infrastructure is now a necessity. All sizes and sectors of companies are adopting more efficient data-warehouse-as-a-service solutions.
What is data-warehouse-as-a-service?
These companies need to consolidate data from multiple sources in order to perform historical and trend analyses. This is where Data Warehouses are useful. They allow firms to organize and maintain clean business data in an aggregate summary form.
If structured data is required for a predefined purpose in a business, A data warehouse can be regarded as the best choice. But, it is not easy to build and maintain a data warehouse. With the volume of data growing continuously, Organizations must adapt the storage and compute components of their on-premise warehouse to meet increasing demands. This requires significant investment, but also adds to the administrative overhead. With a team that is always on top of the infrastructure, it’s easy to maintain a healthy environment. It will be up and running while maintaining security and compliance.
The challenge, which acts as a major roadblock for small companies, is being addressed with a cloud-based data-warehouse-as-a-service or a DWaaS model. DWaaS service providers are responsible for the establishment, maintenance, security, and updating of a data warehouse. Complete with all software and hardware stack management. Only the customer has to worry about connecting data sources they want to pay for managed service and connect to the warehouse.
The key functions of a DWaaS offer
When an enterprise opts for a data-warehouse-as-a-service offering, it will receive a few key services from the provider. It may also choose to include more elements. These are the basic services:
Data warehouse design and development
The company that provides DWaaS services creates a custom data warehouse structure for each customer. This is done by reviewing the customer’s unique requirements, current data management strategy, data sources, and quality practices. Once the custom framework has been completed and is future-proofed (for features such as scalability), It works towards implementing it by choosing the best hardware and software systems, and processes.
Also read: The Definition of Cloud Data Warehouse
Integration with other sources
Once the provider has configured the custom data warehouse, he or she will work towards integrating it. It can be synchronized with all data sources such as customer transactional systems. Depending on the case the vendor might use leading pipeline technologies or custom codes to ensure data transfer to the warehouse with high integrity. Some warehouse providers integrate with analytical solutions already in place for in-house analysis.
Data cleansing and migration
After integration, data from connected sources are merged, cleansed, and enriched. The information is regularly checked for compliance with the core data model and accuracy. The customer chooses the cloud platform to store the cleansed data. However, some providers support hybrid strategies. This means that some data is kept on customer premises while others are stored in the cloud.
After the warehouse is operational, the service provider takes care of the housekeeping and maintaining data quality, including adding and removing data sources, checking performance, as well as extract transform load (ETL), and correctness from time to time. The service provider ensures that all aspects of the service, from the data model to infrastructure, are built in accordance with privacy and security standards.
Maintaining the data warehouse, The provider monitors changes in business requirements and data sources to ensure that the entire data environment is updated regularly with software or computing.
Top data-warehouse-as-a-service solution providers in 2022
With data-warehouse-as-a-service solutions, Many vendors offer data warehousing services without the need for customers to pay the setup and maintenance costs. According to Gartner and G2 customer feedback, however, only a handful of players have been strong enough to be considered leaders.
1. Snowflake Data Cloud
The Snowflake Data Cloud can operate across multiple clouds including AWS and Azure. It provides full relational database support and warehousing capabilities for structured and semi-structured information. It allows storage, compute, and cloud services to be separated into separate layers that can change and scale independently. It automates key maintenance tasks such as query caching and planning as well as updating processing. Snowflake Data Cloud is used by more than 5000 organizations worldwide to store their data for analytics and artificial intelligence (AI).
Customer ratings indicate that the platform is easy to use and meets all requirements. This includes ease of deployment and administration, quality and scalability, integrations, and pricing flexibility.
2. Amazon Redshift
Amazon redshift is an AWS product that provides a fully managed, scalable cloud data warehouse. This allows enterprises to run complex queries on terabytes or petabytes of data stored in S3 buckets. Each node provides CPU, RAM, and storage for one or several databases. It works by creating clusters of nodes. Clusters can be manually provisioned and de-provisioned in Redshift as warehousing requirements change.
According to Gartner user feedback, Redshift is nearly equal to Snowflake, but it falls behind in areas such as quality of end-user training or availability of third-party resources.
3. Google BigQuery
This solution allows for central management of data and computes resources, as well as tools for access and identity management. According to G2 ratings, BigQuery customers reported that they had issues with the deployment, use, and support of the solution.
4. IBM Db2
IBM, like Google, also offers an elastic cloud data warehouse. This allows for independent scaling of storage, compute, and storage with its IBMDB2 solution. It includes an optimized columnar data store, in-memory processing, and actionable compression to speed up analytics and machine learning. It automates maintenance tasks like monitoring, backups, and uptime checks.
Users reported similar problems to Google’s BigQuery, where they experienced issues with the solution’s setup, deployment, and use, as well as the quality of support.
5. Microsoft Azure Synapse Analytics
Azure Synapse analytics combines data integration, warehouse, and analytics capabilities to provide enterprises a unified workspace for ingesting, preparing, managing, and serving big data for AI (and business intelligence) use cases.
Data professionals have the option to query data with serverless or manually provisioned resources. The solution is also a leader in this space because of its near-limitless storage and compute resource scaling, a deeply integrated SQL engine, native integrations to Power BI and Azure ML, and advanced access to data controls.
Azure Synapse Analytics is used by leading enterprises like Walgreens and Co-op, Marks and Spencer, and GE Aviation. Gartner ratings indicate that pricing models and customization are the main problem areas.
The category also includes Oracle, Yellowbrick, and Cloudera, as well as SAP and Teradata. The market for DWaaS solutions will increase 20% to $1.44 billion in 2020 and $4.3 billion by 2026.
According to Mordor Intelligence, the surge will be driven primarily by companies’ growing interest in understanding the available information about business processes, customers, and services to seize new business opportunities.