Data and its analysis are now an indispensable tool for business competitiveness. Users often are dependent on reports, dashboards, and tools for analytics to have access to valuable insights from the gained data. Furthermore, they can then monitor business performance and derive important decisions from it. Data warehouses often support these tools.
Data warehouses without servers act like regular centered repositories of information that can be accessed for analysis to aid decision making. In this structure, data flows into the data warehouse with the help of ETL or ELT processes from various sources, such as transactional systems or relational databases. With (self-service) BI Tools, Spreadsheets, SQL, and other tools and services, business analysts, data engineers, and data scientists can access the data and work with it.
The advantage of a serverless data warehouse is that it is easier to operate compared to the classic on-prem data warehouse. The provider is responsible for the infrastructure. So, there is no need to set up servers, firewalls, etc., but rather, you get IT on-demand, so to speak. There is also no need to worry about scaling resources. If more resources are needed, they are provided automatically.
Advantages of a serverless data warehouse
- Accessibility and Ease of Use: Unlike on-premise data warehouses, one can get access to cloud data warehouses from anywhere in the world. Furthermore, today’s data warehouses feature certain control factors that make sure that the data needed for business intelligence can only be seen by the affected people working in a company.
It is interesting to note that data integrity is always ensured, even when the data within the data warehouse is accessed by multiple users simultaneously. Hence, companies don’t have to fear that they will limit their data quality because of possible accessibility problems.
In addition, modern data warehouses themselves or the associated BI layers are usually designed in a very user-friendly way, so that users from specialized fields can easily create and share their own reports.
- Scalability and Elasticity: The cloud architecture of SaaS-based data warehouses allows businesses to adjust their resource allocation to meet changing demands. With a cloud data warehouse, businesses with fluctuating requirements can pay only for the features and functionality they need. With on-premise data warehouses, you will have to buy more hardware if needed and still pay for it even when usage decreases. This automatic addition of resources (scaling) then works automatically and does not require any effort by your own IT staff.
- Improved Performance: SaaS data warehouses feature a distributed architecture where a number of servers carry the load together. The described servers make sure that the vast quantities of data are prepared at the same time. Cache and in-memory functions and more efficient architectures, such as column-based and nested data structures, make these databases even faster than their predecessors. Also, the efficient and coordinated interfaces to the front-end make data analysis better and faster.
- More and Cheap Data Storage: One of the most important reasons for moving your data warehouse to the cloud is the expanded storage options. Cloud-based data warehousing solutions often offer a pay-as-you-go model for companies to create an individual storage space without any waste. The previously described approach can be also used with other features so that organizations are able to approach their data warehousing projects in different ways. With the right know-how, you can also save money, because cloud storage is often more cost effective.
- Better Integration and More Data Formats: Today’s cloud data warehouse approaches are so created that they can include data from various sources such as cloud applications, databases, or file formats. Furthermore, even raw or semi-processed data can easily be filtered out and manifested within the structural architecture.
With existing interfaces to other services within a cloud platform, the data can be easily linked to spreadsheet tools, self-service BI tools, or ML services, for example. This makes integrations and sharing of data within the company much easier and helps the company become more data driven. A common approach for this is using a data lakehouse, which combines the concepts of data lakes and data warehouses.
- Data Governance and Security: Data privacy laws and regulations have been a hot topic in the data world for several years. One example is the GDPR in Europe, which threatens stiff penalties for non-compliance. Organizations do have to comply with the strict governance regulations that handle everything that has anything to do with consumer data.
A data warehouse should preferably feature data from various sources and make sure that the system is complete, without any gaps in between. Nonetheless, organizations can encounter difficulties if they omit any governance regulations or compliance requirements by mistake.
Companies must therefore use additional encryption techniques for cloud data warehouses because the data is no longer stored in the company's own locked server room but is outsourced to the cloud provider.
- Data Integration and Network: This means that data no longer has to be distributed only within the company's own network, but also possibly across multiple cloud platforms. This means that companies have more work to do to create, secure, and monitor interfaces. Since the data is routed via the network, companies must of course also ensure an appropriate connection to the Internet, which is the only way to quickly transfer large volumes of data.
- Know-How: A problem for companies that is often underestimated is the lack of know-how. The cloud is a different world than the one of onPrem. In addition to the areas of network and company, a lot has happened in the area of data warehouses and data analysis. New architectures such as column-oriented or NoSQL databases have been developed and new services came on the market. However, the market is tight and companies are looking for employees in this area, but may not be able to get them. Companies must therefore look for suitable personnel at an early stage, be they data architects, data engineers, or scientists who are also familiar with modern cloud platforms and data warehouses.