More than just data storage
With the increasing complexity of digital processes, companies need precise analyses and reporting in order to make important, insight-based decisions. At the same time, IoT and cloud solutions are transforming the way data is stored, structured and retrieved - for example in the cloud data warehouse.
The integration of various data sources in cloud data warehouses is increasingly providing the basis for necessary insight-based decisions. The information in these cloud data warehouses usually comes from a combination of different data sources, some of which are networked, such as CRM, product sales or machine data. Cloud data warehouses provide an organized schema for this networked data and make it easier for end users to both access and interpret this information.
Cloud data warehouse especially for large amounts of data
The switch to the cloud makes it possible to meet the changed characteristics and requirements for query performance with large volumes of data. By directly linking storage and data processing, traditional data warehouse infrastructures can quickly become outdated and expensive.
A traditional data warehouse solution quickly becomes inflexible and unfavorable, especially during peaks when the amount of data generated is significantly higher than average. With the capabilities of cloud data warehousing, companies can now scale horizontally to meet either compute or storage requirements as needed. This has significantly reduced concerns about wasting financial resources by over-provisioning servers to cope with data queries or a short-term project.
The clear advantages of the cloud data warehouse lie in its flexibility. In the past, IT teams had to estimate how much storage capacity needed to be provided for their business units, often several projects and development stages into the future. Miscalculations were costly and irrevocable. The easy scalability of cloud data warehouses virtually eliminates this risk.
Building a cloud data warehouse
A phased approach, where the simplest and least technical workloads are migrated to the cloud-based data warehouse first, is often the most sensible approach. Variable workloads, such as financial reporting, are good candidates for initial migration as they do not continuously use data warehouse resources. Business unit-specific workloads as part of a department-specific data mart are also good candidates for initial migration.
Sustainable workloads, such as daily reporting that drives tactical business decisions, can be migrated later, as can business-critical, audit-intensive workloads. The step-by-step approach can build confidence in the cloud data warehouse and the experience gained is beneficial for migrating more complex workloads and use cases.
Data integration via ELT or ETL
Traditionally, ETL (Extract, Transform, Load) tools are used to move and integrate data from transactional systems into a data warehouse. In the context of cloud DWH, ELT (Extract, Load, Transform) is also becoming increasingly common. In both cases, the data is consolidated and transformed before it ends up in the cloud data warehouse. It is important for companies to ensure that existing data flows are validated for cloud implementation with the support of cloud-native technologies.
For organizations taking their first step into data warehousing, it may be best to look for native cloud data integration tools (ETL or ELT as a Service) designed specifically for the challenge of integrating data from transactional on-premises database systems into a cloud repository.
Advantages of the cloud data warehouse
In recent years, there has been an increasing trend away from traditional data warehouse architecture towards cloud-based solutions and away from traditional on-site solutions. To summarize some of the advantages of a cloud DWH compared to on-premise solutions:
- Scalability. Scaling data warehouses in the cloud is much easier compared to on-site warehouses
- Cost. Cloud-based warehouses are cheaper to set up as there is no hardware or upfront license costs.
- Time-to-market. It is quick and easy to get a data warehouse up and running in the cloud. Deploying a local data warehouse takes much longer
- Performance. Cloud data warehouses are optimized for analysis. They use columnar storage and massively parallel processing (MPP), which enables significantly better performance when executing complex queries
