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:
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:
Let's explore three modern industry use cases for cloud data warehousing.
The global IIoT market will reach a value of over $2 trillion by 2030. Almost every industry, from manufacturing to energy and gas, utilizes the capabilities of IIoT devices. This means that vast volumes of data from these industrial smart devices need to be managed and put to good use. Traditional data warehouses aren’t the optimal solution here, making mining IIoT data one of the most important use cases for highly scalable, cost-effective, and accessible cloud data warehousing.
Legacy data isn’t data that is outdated. It’s data that exists in outdated environments. Businesses need to realize their legacy data is of tremendous value; the challenge is to unlock that value.
Cloud data warehouses provide the tools to integrate legacy data into contemporary data streams, providing companies with holistic insights.
One of the biggest advantages of cloud data warehousing is how quickly and confidently organizations can make strategic pivots. This is fueled by insights gathered from advanced analytics and reporting.
The capabilities of cloud data warehousing allow businesses in numerous industries to get highly customized reports that address and answer very specific questions. This, in turn, greatly enhances decision-making.
Cloud data warehouses are the wellspring of business intelligence. The advancements in big data analytics mean that companies will use their cloud data warehouses to deliver complex business intelligence that would have previously taken a lot of time and resources to gather.
Business intelligence isn’t created. It’s uncovered. Cloud data warehousing provides the speed and tools to sift through mountains of data to uncover hidden insights and answers.
Leveraging data from disparate sources without standardizing it can range from being laborious and counterproductive to downright impossible. Businesses need a unified view to make sense of data from diverse formats and sources.
Data blending integrates various datasets from heterogeneous sources and combines them in a way that is accessible, logical, and contextual. Therefore, one of the primary applications of cloud data warehousing is integrating high-potential raw material into a unified system so it can evolve into a key asset.
The modern world doesn’t wait for anyone. This means that businesses can’t rely on pre-scheduled analytics and reporting. You need answers to complex questions immediately. That’s why real-time cloud data warehousing becomes a critical element to a business’s success.
Ad hoc analytics can be transformative. For example, whenever organizations leverage cloud data warehousing for ad hoc analytics, they can quickly analyze data as needed without using predefined reports or queries and make more informed decisions.
Data apps utilize data as raw material to generate intelligence and kickstart logical next steps based on that intelligence. There are many kinds of data applications businesses can use or develop. Some are client data apps that customers can interact with, while others are internal apps for employees and in-house teams to use as support. As data becomes more established in the cloud data warehouse, the data apps that use it become more advanced and robust.
Event processing involves the analysis of an event or series of related events and a corresponding action based on that analysis. Businesses can utilize this to predict positive and negative outcomes to events and take proactive steps to either maximize gains or avoid/minimize potential damage.
There is tremendous work going on in the field of artificial intelligence and machine learning around the world. Businesses are rightly intrigued and excited by the potential of AI and ML. That being said, they might also struggle to know where and how to integrate AI/ML into their operations. Cloud data warehousing is the perfect opportunity for companies to understand and utilize the full capabilities of data to power AI/ML.
Before delving into the intricacies of cloud data warehousing, it’s important to get acquainted with key data warehousing terms, concepts, and the basics to help you navigate the world of cloud data warehousing.
An enterprise data warehouse (EDW) is typically defined by four key characteristics...
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