Cloud data warehouses help businesses store and manage data in the cloud. They represent a significant evolution in data storage, enabling flexibility, scalability, and affordability in managing increasingly large and complex data. In this whitepaper, we will use the terms "cloud data warehouse" and “data warehouse" interchangeably.
Every aspect of data management is conducted under the virtual roof of a cloud data warehouse. Most importantly, a cloud data warehouse transforms data into assets companies can use to improve their capabilities, fuel innovation, and enhance profits.
Traditional data warehouses are physical structures typically on-site. While these have served businesses well for many years, a series of challenges, including high costs and complexities with legacy hardware, have rapidly antiquated them. Cloud data warehouses are a robust and ultramodern alternative to traditional data warehouses, one that can lead to profound success for modern businesses. However, enterprises that have to adhere to special compliance or connectivity requirements still leverage on-premises solutions.
By 2026, the market value of the cloud data warehousing industry is forecast to hit $12.9 billion, a compound annual growth rate of 22.3%. While North America and Europe hold the highest market share, the fastest-growing segment in cloud data warehousing is the Asia-Pacific region, powered by the booming megamarkets of China and India.
The key factor behind these numbers is that data now drives the world, including business. Cloud data warehouses are highly scalable and provide a safe, secure environment backed up by the expertise of leading high-tech companies.
Industries that benefit the most from cloud data warehousing include manufacturing, energy and utilities, healthcare, IT, government, retail, and BFSI (banking, financial services, and insurance). Since cloud data warehousing is such a flexible solution, the use cases are diverse. But one thing is certain: cloud data warehousing is now the norm and the foundation upon which the future will be constructed.
Data warehouse architecture comprises three tiers. The top tier represents the front-end client that offers results via analysis, reporting, data mining tools, and other management. The second or middle tier comprises ELT, which organizations use to access and analyze data. The third or bottom tier of data warehouse architecture is essentially the database server where enterprises load and store data.
Data can be stored in two ways. Organizations can either leverage high-speed storage to enable quick and frequent access or implement cheap object storage for infrequently accessed data. The data warehouse will move frequently accessed data into "fast" storage to optimize query speeds. Depending on access requirements, organizations can use different logic as well.
Online analytical processing (OLAP) is a type of data processing that occurs in a data warehouse and serves different workloads and requirements.
Data consistency is optional for OLAP systems since they typically use data snapshots. OLAP systems handle large data volumes and use denormalized database designs leveraging star schema or snowflake schema. This approach increases data redundancy, improves query performance, and accelerates data-driven decision-making.
Data warehouses continuously collect and organize data into a dedicated comprehensive centralized repository. Data collected from various sources are systematically sorted into tables based on the data type and layout.
Insights harvested from a data warehouse help businesses better understand their target audience or customers and be alert to emerging trends. For example, enterprises can gain a competitive advantage by forecasting market changes, formulating a robust pricing strategy, or developing better products.
There are three layers of data warehousing:
For instance, a doctor can access the ODS to get the most recent patient data like lab results or medication history; and a pharmacist can verify prescriptions to prevent harmful drug interactions. Thus, the ODS provides timely, integrated data to healthcare providers, improving patient care.
An EDW stores static data, whereas an ODS integrates dynamic operational data. The data mart creates specialized data views over the EDW.
Enterprises can configure data warehouses into one or multiple of the following system configurations:
Software tools and hardware used for storing, transforming, and analyzing data are called data warehouse appliances.
With the concepts introduced above, we can highlight three key ways in which a data warehouse can work: