Unlocking Data potential: Traditional Data Warehouse vs. cloud Data Warehouse
We live in a world of data: There is more of it than ever before, in a ceaselessly growing array of locations and forms. Dealing with Data is your window into the ways data teams are handling the objections of this new world to assist their organizations and their customers thrive.
The data industry has changed drastically over the decade, with perhaps some of the most significant changes occurring in the realm of data storage and processing. The atmosphere is expanding exponentially, and companies of all sizes are sitting on big data stores. And where does all this Data thrive? The cloud. Modern organizations are born on the cloud: Their systems are developed with cloud-native architecture, and their data teams work closely with cloud data systems rather than on-premises servers. The extension of cloud options has coincided with a low bar to entry for younger companies. Still, organizations have seen the sense of capturing their data online rather than on-premises.
The accelerating interest in cloud storage coincides with an increased demand for data processing engines that can handle more data than ever before. The shift to the cloud has opened many doors for teams to develop bolder products and infuse insights of all types into their in-house workflows, user apps, and so on. The cloud is the future, but how did we get there?
Let’s delve into the concept of the traditional data warehouse versus cloud data warehouses.
Traditional vs. Cloud Explained
Traditional data warehouses
Before the sprint to move infrastructure to the cloud, data captured and stored by businesses was already increasing. Thus there was a requirement for an alternative to OLTP databases that could process extensive data more efficiently. The business began to build what is now seen as traditional DWs. A traditional data warehouse is generally a multi-tiered series of servers, apps, and data stores. While the companies of these layers have been refined over the years, the interoperability of the technologies, the myriad software, and the orchestration of the systems make the management of these systems a big challenge.
Furthermore, these traditional data warehouses are basically on-premises solutions, making updating and managing their technology an additional layer of support overhead.
Cloud data warehouses
The traditional DWs solved the issues of synthesizing and processing huge volumes of data, but they presented new objections for the analytics process. Cloud DWs took the advantages of the cloud and applied them to DWs — bringing huge parallel processing to data teams of all sizes. Hardware, software updates, and availability are all managed by a third-party cloud vendor. Scaling the warehouse as business analytics requirements grow is as simple as clicking a few buttons. The warehouse hosted in the cloud makes it handier. With a sudden rise in cloud SaaS products, integrating an organization’s various cloud apps with a cloud data warehouse is easy.
Understanding on-premises traditional data warehouse architecture
There are several numerous characteristics attributed solely to traditional DW architecture. These characteristics involve varying architectural approaches, designs, models, components, processes, and roles — all of which influence the architecture’s overall impact.
Three-tier architecture approach
The three-tier architecture approach is typically found in approaches to on-premises data warehousing. The three tiers include a bottom, middle, and top layer.
Bottom Tier: The bottom tier contains the actual database server used to remove data from sources.
Middle Tier: The middle tier has a server for online analytical processing (OLAP) responsible for transforming data. It can either do relational operations or leverage a multidimensional OLAP model for multidimensional data operations.
Top Tier: The top tier is similar to a user interface layer. It consists of tools for common data warehousing analytics such as reporting.
Understanding cloud-based data warehouse architecture
Cloud-based DW architecture is relatively new when compared to legacy options. This data warehouse architecture means that the existing data warehouses are accessed through the cloud. There are several cloud-based DWs options, each of which has divergent architectures for the same advantages of analyzing, integrating, and acting on data from various sources. The difference between a cloud-based DW approach compared to that of a traditional approach involves:
Up-front costs: The disparate components required for traditional, on-premises data warehouses mandate costly up-front charges. Since the elements of cloud architecture are accessed through the cloud, these expenses don’t apply.
Ongoing costs: While enterprises with on-prem data warehouses must deal with upgrade and maintenance costs, the cloud offers a low, pay-as-you-go model.
Speed: Cloud-based DW architecture is substantially faster than on-premises, partly due to the utilization of ELT — which is an unusual process for on-premises.
Flexibility: Cloud DWs are designed to account for the variety of formats and structures found in big data. Traditional relational options are intended to combine likewise structured data.
Scale: The resilient resources of the cloud make it prompt for the scale needed for big datasets. Moreover, cloud-based data warehousing options can also scale down as needed, which is challenging with other approaches.
Final Thoughts
Enterprises can optimize their transition from on-premises options to cloud-based data warehouses by using comprehensive solutions to manage data movement in the cloud. Polestar Solutions focuses on providing well-governed and secure data management that facilitates the sustainability of cloud and hybrid-cloud workflows. It also provides your organization with easy access to your Data while supporting the latest cloud data warehouses in the market.
