The data warehouse has been an essential part of many organizations’ data architecture and IT infrastructure for 30 years. Despite this legacy, there needs to be more clarity surrounding the concept today.
With the advent of data lakes, big data, and advanced analytics, some industry experts wonder if the data warehouse is still relevant. The short answer is: of course. For a more in-depth explanation, check out this article on what you need to know before buying a data warehouse.
Despite the hype around big data, companies across all industries continue to use data warehouses to provide BI data that can serve as decision-making tools for department heads and CEOs. Data warehouse platforms provide companies with a history of their business. While big data and analytics have their place, a data warehouse is a practical and proven tool for analyzing structured business data and implementing data-driven decision-making strategies.
What Is a Data Warehouse?
A data warehouse can be defined by the type of data stored and who uses it. A data warehouse is designed to support decision support and BI implementation and is distinct from the everyday OLTP (online transaction processing) applications that support the core business. It reduces the friction between operational transactions and analytic queries.
The data warehouse is read-only, and data is organized according to business constraints rather than computer processing. The platform categorizes information into topics of interest to business analysts and service managers, such as customers, products, or accounts. Data is loaded into a data warehouse and then made available to business users for analysis.
The information stored in the data warehouse is organized hierarchically and contains transactions that have already taken place. Therefore, data is stored in a reduced or aggregated form to make it easier to search, access, and analyze. Redundant data is often included in a data warehouse to provide users with multiple views of information presented in a logical and understandable grouping.
Data files contain categorized information from operating systems and external data such as cash register data. The data is consolidated and archived in a uniform format for the organization, even if operational data is formatted and archived according to different templates. This allows business analysts to visualize information without the need for data transformation.
For example, a query in a data warehouse might be, “What were the total sales of a particular product in a specific region in the first quarter of last year?
Even if the stored data no longer changes, new data is periodically loaded into the data warehouse. The frequency of additions is related to the latency constraints of BI applications and data warehouse-based BI systems. Many modern platforms operate in near real-time, which means that the delay between the two stages is minimal: as soon as data is created or changed in the production system and as soon as new data is transferred to the data warehouse.
The most specific function of a data warehouse is to enable a company to analyze data over time. Operational databases, on the other hand, support an organization’s production systems. Operational data is atomic, constantly changing (updated by OLTP applications), and reflects only the current value of the last transaction.
Data Warehouses: Why?
A data warehouse supports OLAP (online analytical processing), which allows advanced users to analyze transactions using stored data. Thus, business executives can improve their business strategies and decision-making processes by using a data warehouse to analyze business processes, results, and trends.
Here is a list of tasks that a data warehouse can accomplish:
· Monitoring, managing, and improving business performance;
· Monitoring and modifying marketing campaigns;
· Optimizing logistics and operations;
· Improving management efficiency and product development;
· Consult, summarize, and access a variety of information and data from a variety of sources;
· Improve customer relationships;
· Anticipate growth opportunities;
· Cleaning and improving the quality of corporate data.
There are many advantages to using a data warehouse. First, from an IT perspective, separating the analytical processes of a data warehouse from the operational processes of production applications improves productivity. From a business perspective, a data warehouse can be a handy tool for visualizing historical data without interfering with day-to-day operations. Thus, by querying and analyzing data in a data warehouse, companies can optimize their operations and implement more efficient business processes, ultimately leading to increased sales and profits.
Data warehouse tools can include many solutions, such as database management systems, database appliances, reporting and OLAP tools, BI tools, dashboards, ETL applications, and other data integration applications. All of these tools are used to create, deploy, and manage data warehouse environments.
Different Types of Data Warehouses
The best-known data warehouse is undoubtedly the database management system. In most cases, it is a relational system, but all types of DBMS can be used. This system has features and functions that allow it to be used for data storage and business management. It is also possible to connect the data warehouse to other software, such as those mentioned above. Of course, this can affect cost, but it can also provide better management capabilities (integrated features are easier to use).
In General, There Are Four Types of Data Warehouses:
Traditional database management systems (DBMSs). Most are relational, but not all: IBM, Microsoft, Oracle, and SAP make up the majority of technology vendors.
Specialized analytical DBMSs. Data warehouses in this category are not traditional DBMSs. They are “extended” to support specific data warehouse tasks. Examples include column-oriented databases such as those from HP and SAP.
Devices. They are designed to provide off-the-shelf data storage services. They usually combine software and hardware with a pre-installed and pre-configured relational or analytical database. The server has sufficient memory and data storage capacity. The vendor installs it, and the customer plugs it in and turns it on (vendors say). Teradata, IBM, and Oracle offer them in their catalogs.
Data storage in the cloud. The DBMS is not installed on-site but is available over the Internet. By offering data storage in the cloud, you get data storage as a service, with no DBMS or hardware on-site. Microsoft, Amazon, and IBM are among the experts in this field.
The market for data warehouse vendors is diverse. While traditional DBMS vendors dominate it, there are also specialized DBMS vendors offering analytical databases and cloud providers that may be of interest to a specific group of customers. There are also a number of hybrid solutions that have different functions for storing and accessing structured and unstructured data, such as Pivotal, and many others.
Who Builds, Manages, And Uses Data Warehouses?
A business usually decides to acquire and implement a data warehouse or IT manager in collaboration with the company. Creating a data warehouse requires the involvement of several people, such as business people, data architects, database administrators, developers, SQL query specialists, and finally, project managers.
Teams of database administrators usually manage the data warehouse, and it can be updated and improved by data architects and data analysts. The users, however, are enterprises.
