More and more companies are moving their Business Intelligence (BI) analysis processes to cloud-based data warehouses. Learn everything you need to know to decide whether to shift your BI processing activities to a cloud-based data warehouse.
More and more companies are moving their Business Intelligence (BI) analysis processes to cloud-based data warehouses. Is it the right move for you?
This guide explains everything you need to know to decide whether to shift your BI processing activities to a cloud-based data warehouse.
A data warehouse, also known as an enterprise data warehouse (EDW), is a type of enterprise data management system designed to enable and support business intelligence (BI) activities, especially their analytics component. Data warehouses perform queries and analysis and often hold and consolidate a significant amount of structured and unstructured data from many sources, such as marketing automation platforms, application log files, point-of-sale transactions, customer relationship management systems (CRMs), and more.
The analytical capabilities of data warehouses make it possible for companies to generate valuable business insights from their data to improve decision-making. Over time, data warehouses build a historical record that can be invaluable to business analysts and other company stakeholders.
Key point: A data warehouse is often considered an organization’s single source of truth.
A data warehouse typically includes the following components:
These data warehouse capabilities and benefits have made them a central component of enterprise analytics programs that help support making sound and informed business decisions.
Businesses house traditional data warehouses on-premises. The data flows into them from relational databases, transactional systems, business applications, and other sources. Traditional warehouses are typically structured to capture subsets of data in batches and store them based on rigid schemas. Because of this, they are not suitable for spontaneous queries or real-time data analysis.
With traditional data warehouses, organizations must secure their own hardware and software. They are typically costly to scale and maintain over time. Storage space is limited in a traditional warehouse, so data is usually processed and discarded quickly to free up storage space.
Data analytics is central to virtually all business activities and the decisions related to them, including generating revenue, containing costs, improving operational efficiency, and enhancing the customer experience.
As data analysis evolves and diversifies, companies require more robust data warehouse solutions and advanced analytic tools for storing, managing, and analyzing large quantities of data from across all parts of an organization.
Modern warehouse systems must be scalable, reliable, highly secure, and flexible. They should be able to handle a wide variety of data types and use cases. Data warehousing requirements go beyond the capabilities of traditional solutions, which is why many businesses today are turning to cloud-based data warehouses.
Cloud-based data warehouses extend the capabilities of traditional ones because they operate in the cloud. Cloud data warehousing offers instant scalability to meet changing business needs and processes. They are designed to support today’s complex analytical queries involving vast amounts of data.
With a cloud data warehouse, businesses can enjoy the benefits of conducting analysis in a cloud environment and its more predictable costs. The initial investment in cloud computing is usually much lower than traditional systems, and ramp-up times are shorter because the cloud service provider manages and maintains the warehouse infrastructure, which is readily accessible.
Like traditional data warehouses, cloud-based ones collect, integrate, and store data from multiple internal and external sources.
Data pipelines typically transfer the data into and out of warehouses. Data is pushed out of different source systems, transformed as needed (for instance, into different formats), and then loaded into the data warehouse. This process is known as extract, transform, and load (ETL). Data can be converted through ETL in a central repository, as well. From there, business intelligence (BI) tools are used to access, mine, and report on the data. All this can happen in real- or near-real time.
Cloud data warehouses offer a full range of services, including:
You can also combine cloud warehouses with cloud data lakes to collect and store unstructured data. This will unify your data lakes and warehouses into a centrally managed source of company data.
Different providers offer different forms of cloud data warehouse services.
Data warehouse providers also offer:
More and more businesses today are shifting from traditional data warehouses to cloud-based ones. Here are some of the key reasons:
Cloud data warehouses provide great flexibility. They offer almost unlimited storage capacity. You can quickly, efficiently, and cost-effectively scale capacity up or down as your business needs change. You only pay for the storage you use.
Businesses that take advantage of cloud data warehouses can readily access and operationalize machine learning models and AI technologies. This can improve data mining, predict business outcomes, and optimize other areas, including data life cycle management, business processes, and operational costs.
Data cloud warehouse providers must meet service level agreement (SLA) standards. These standards promise — and typically deliver — more dependable performance than traditional warehouses. On-premises data warehouses often have limitations that negatively impact performance.
Businesses that move to cloud data warehousing find pricing more flexible and dependable. Providers charge by throughput, per hour per node, or for a defined amount of resources. With cloud warehousing, you avoid the significant costs associated with on-premises data warehouses that run constantly, whether they are being used or not.
A cloud data warehouse allows you to outsource data storage and management to cloud providers. This typically results in significant operational savings. Plus, it keeps your team focused on more critical tasks that will contribute dollars to your bottom line.
Cloud data warehouses provide more powerful computing that supports streaming data, allowing you to query data in real-time. As a result, you can access and use data much faster than with an on-premises data warehouse, allowing you to get more accurate insights faster and make more informed business decisions.
Here are some common reasons why companies turn to data warehousing in the cloud:
Data warehouses make all of these activities possible.
If your business has, does, or is interested in any of the following, it’s smart to consider a data warehouse:
Cloud-based data warehouses allow organizations to analyze large amounts of different kinds of data and extract significant insights from it. Four unique things allow data warehouses to deliver this significant benefit.
A well-designed data warehouse leverages these factors to perform queries quickly, deliver high data throughput, and provide the flexibility required for users to manipulate data for closer examination to meet different needs. The data warehouse is where middleware BI environments provide end users with reports, dashboards, and other interfaces.
When designing a data warehouse, an organization must:
Once this is done, you can set up the logical and physical design for the data warehouse.
Data warehouse design must address the following:
What’s key to effective data warehouse design is meeting the needs of analysts and other stakeholders. A common issue during the design process is that end users often can’t express their needs until a specific one arises. The planning process must include a significant period of proactive, scenario-based discovery to anticipate needs. On top of this, the data warehouse design should allow room for expansion and evolution to keep pace with technological changes and new user needs.
Your organization’s unique BI requirements will determine the optimal architecture of your data warehouse. Some common architectures are:
Selecting the right architecture for your data warehouse will increase the value your business gets from it.
Companies processing large amounts of data from various sources use data warehouses and data lakes to accommodate it. The choice of what to use and how depends on what the organization intends to do with the data. The following describes what each is best suited for:
Data warehouses were introduced in the late 1980s to help data flow from operational systems into decision-support systems (DSSs). The earliest data warehouses required a significant amount of redundancy. Most organizations had several DSS environments that served different users. Although the environments used much of the same data, the gathering, cleaning, and integration of it were replicated for each environment.
Over time, data warehouses became more efficient. They evolved from information stores that supported business intelligence platforms into actual, more comprehensive analytics infrastructures that support many types of applications.
The future of data warehousing is the autonomous data warehouse. It leverages AI and machine learning to eliminate the need for manual tasks and simplifies setup, deployment, and data management. A cloud-based autonomous data warehouse requires no human database administration, hardware configuration or management, or software installation. This increases efficiency and allows team members to do more valuable tasks, like leveraging the insights gained through BI to run your business more effectively.
Ready to explore the possibilities of using data warehouses in your organization? Contact the experts at Jarrah to find out how they could benefit your business.
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