What is a Data Warehouse? Data Warehousing Defined

Businesses have evolved to rely on deep data analysis. Companies of all sizes rely on analytics solutions to draw insights from fragmented data, monitor their KPIs, and generate reports to support smart decision-making to stay competitive in the industry. Data warehouses are specialized computer programs structured to store data securely, efficiently, and instantly offer simultaneous query responses to data analysts and business decision-makers.

What Is a Data Warehouse and How Does It Work?

A data warehouse is a computer system that stores and analyzes vast quantities of organized or semi-structured data. It is a central repository for authorized business users who rely on data analysis to make better decisions. Most business intelligence (BI) initiatives include a data warehouse as a significant component.

Data from multiple transactional systems, relational databases, and other sources are routinely processed and fed into a data warehouse. Data engineers, scientists, business analysts, and decision-makers use BI technologies and other analytics applications like machine learning to access the data and use it to populate dashboards and generate reports.

Important Points to Remember

Companies must base their choices on data to be competitive, and a data warehouse is required to do so effectively.
Large volumes of historical data are analyzed, and business insights are derived using data warehouses.
Data warehouses today are used to supplement relational databases and work with data from various sources.
The data warehouse of the future will be hosted in the cloud.

Definition of Data Warehouses

Data warehouses are computer systems that store, query and analyze enormous amounts of historical data from various sources. Over time, it accumulates a historical record that can be extremely useful to data scientists and business analysts. The data housed in a data warehouse is of high quality since it goes through several cleansing and preparation operations before entering. As a result, the records of a data warehouse are frequently regarded as an organization’s authoritative source of reliable data.

The following items are commonly found in data warehouses:

Data pipelines linking management systems to the warehouse that are automated and safe
Data cleaning and preparation software
Tools for managing data and information
A semantic layer for reorganizing data for quick, complicated analytics and searches.
Fundamental to complex analytics and reporting software is available.
There are a variety of ways to convey information in a meaningful way.

What Is a Data Warehouse and How Does It Work?

For the sole purpose of analysis, a data warehouse turns relational data and other data sources into multidimensional schemas. Metadata is created during this translation to speed up inquiries and searches. On top of this data layer, a semantic layer organizes and maps complex data into common business terms like ‘product’ or ‘customer,’ allowing analysts to develop studies without knowing database table names swiftly. Finally, an analytics layer sits atop the semantic layer, allowing authorized users to access, visualize, and analyze the data.

What Is the Purpose of Data Warehouses?

In a non-production setting, a data warehouse is used to examine many various types of business data. Instead, the operational databases can continue to record transactions and assist the business using a data warehouse. Companies utilize data warehouses to find patterns, trends, outliers, and other long-term links in their data.

A data warehouse can also evaluate data from several sources and retrieve data from various storage systems. It also protects the integrity of a company’s data by allowing employees to query it without inadvertently modifying or disrupting the production environment.

When Should a Data Warehouse Be Used?

While there are several benefits to using a data warehouse, the following four stand out:

If you need to combine data from a variety of sources. If you want to track the behavior of your most valuable customers, you may have to combine payment details from your credit card processor with financial information from your accounting system and data generated by these customers when they use your product or service. When data from all three sources can be stored and managed in one single area, this becomes considerably easier.
If you need to keep your analytical and transactional data separate. For example, your analysts might want to collect and analyze data from a production application’s activity logs. Still, they don’t want to risk disturbing the business by working directly in the database. They can avoid this problem by sending the data to a data warehouse geared for complicated querying and working on it there.
If the data source you’re working with isn’t queryable. The great majority of BI tools, for example, do not work well with NoSQL databases. Analysts must first move this data to a data warehouse, where the BI programs can access it.
If you want your inquiries to go faster. Standard queries will be extremely slow when your transactional data contains hundreds of thousands of rows. Using a data warehouse to build summary tables that aggregate the data, which can be searched much faster, is significantly more efficient. This will also free up space in your transactional database, allowing it to run more efficiently.

Data Lakes vs. Data Warehouses

A data warehouse may process large amounts of relational data from various sources, including transactional systems, operational databases, and line-of-business applications. Hundreds and thousands of gigabytes of data can be involved. The data might serve as the company’s definitive version of information because it is heavily vetted.

A data lake can examine various forms of data, including both structured and unstructured data. Therefore, machine learning, data discovery, extensive data analysis, and profiling are common data lake applications.

Databases vs. Data Warehouses

Databases are designed to keep track of transactions as they happen. They collect data from a single source, such as a credit card processing system, “as is.” As transactions are processed, they do this continually and in real-time.

On the other hand, data warehouses are designed to analyze large amounts of data from various sources. Data warehouses quickly query massive volumes of data after it has been recorded, rather than registering individual data additions at maximum speed.

Data Marts vs. Data Warehouses

A data mart is a branch of a data warehouse dedicated to a single function or business unit, such as finance, marketing, or sales. A data mart is a smaller, more specialized version of a data warehouse that collects data from fewer sources. It may be installed as a standalone system or integrated into a more extensive data warehouse.

What Is the Relationship Between Data Warehouses, Databases, and Data Lakes?

Many firms store and analyze their data using a combination of databases, data lakes, and data warehouses. The information might be stored in operational databases before being supplied to data warehouses for additional analysis. However, not all of their data originates from an organized database with tabular data.

Unstructured data can be used in some applications, such as big data analytics, full-text search, and machine learning. This type of information is collected and sent into a company’s data lake, where it can be prepared for analysis in the data warehouse.

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Functions of a Data Warehouse

A data warehouse is a particular type of database that is used to analyze data. It usually requires sifting through vast amounts of data from many sources to uncover various trends and relationships shown by the data. It serves two primary purposes:

Data processing and management for sophisticating analytics and reporting.
To store data that may be accessed by a range of business intelligence and analytics applications.

When combined, these basic capabilities allow a wide range of analytics tools to incorporate diverse types of data from various sources and then analyze it to answer questions, discover business patterns, and forecast future performance.

Types of Data Warehouses

Initially, all data warehouses were on-premises, but they rapidly shifted to the cloud, just like other information technology. Here’s a breakdown of the various options and what they have to offer.

Data warehouse that is hosted on-premises. With an on-premises solution, the company that uses it purchases, licenses, deploys and maintains all of the necessary gear and software. This method is still in use, and it provides businesses with various benefits:

Complete control over the system, including who has access to it and how they utilize it.
The capacity to adapt and tailor the data warehouse to meet unique needs while improving its performance.

Appliance for storing data. A data warehouse appliance is one sort of on-premises data warehouse. Companies can more easily extend their data warehouse architecture to support their business analytics requirements as they develop and expand using these self-contained hardware devices; however, businesses shift to the newest type of data warehouse. These appliances and on-premises systems are generally being phased out.

Data warehouses in the cloud. Like all cloud-based apps, cloud data warehouses don’t require an organization to buy or maintain any hardware or software. A company just pays for the subscription, storage space, and processing power needed at any given time. It’s as simple as adding more cloud resources to increase the capacity of a cloud data warehouse; there’s no need to hire employees to operate or maintain the underlying technology infrastructure because the cloud service provider handles these activities. A corporation can reap various benefits by adopting a cloud-based data warehousing strategy. These are some of them:

Scalability: With a cloud data warehouse, capacity may increase or decrease based on the business’s changing needs.
Reduced costs: There are no physical servers to acquire or set up with a cloud data warehouse, decreasing upfront costs and potentially lowering the total cost of ownership. A company just pays for the storage and CPU time it requires. Thanks to this pay-as-you-go model, the business no longer needs to make capital expenditures, including the extra capacity to address demand spikes. In addition, because the cloud provider handles all system maintenance, administration, and upgrade needs, those costs are also reduced.
A diverse set of applications: Many associated cloud services, including identity and access management and whole suites of data analytics tools, are available from top-tier cloud data warehouses. This lowers the cost of software development.
Security: While security concerns have been raised as a reason to avoid shifting to the cloud, increased security is an advantage. Most firms cannot match the security safeguards that top-tier cloud service providers spend.
High availability: this is another area where cloud service companies put a lot of money into. Cloud data warehouses often offer a guaranteed service level of 99.9% availability. Their ability to duplicate data across many geographic areas inside the cloud environment ensures that a business can access its data even if a local system fails.
Reduced time to market: employing a cloud data warehouse results in fewer delays and roadblocks. A company’s business analysts and data scientists may get insights faster, which means faster turnarounds and shorter lead times for new products and services.

The architecture of a Data Warehouse

A data warehouse’s design or architecture typically consists of three tiers:

A layer of Analytics The analytics layer is the user-facing front-end that uses data visualization tools to show the results of an analysis.
A layer of semantics. The analytics engine used to access and evaluate the data is part of the semantic layer.
Data Layer is a type of data layer. The database server is the data layer of the architecture, where data is translated, loaded, managed, and saved.

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While those three levels remain identical, the design of any data warehouse is frequently customized to meet the firm’s demands. All data warehouses have a central database where metadata, summary data, and raw data are stored. This repository receives data and allows company decision-makers to access it for analysis. Additional techniques, such as the following, are based on this fundamental architecture:

Simple with a staging area: data warehouses include a staging section where data is readied before entering the core warehouse repository.
Hub and spoke: This technique provides data marts with data subsets specific to a company’s many business divisions, allowing them to conduct analysis faster and more efficiently.
Sandboxes: Virtual sandboxes are secure computing environments that allow businesses to “play” with their data safely. Anything goes here; data does not have to follow the rules that govern the central repository of their data warehouse.

Schema for Data Warehouses

A schema is a blueprint or logical description of how data is organized that underpins all data warehouses. It contains the names and descriptions of the various types of records held by the warehouse. There are three basic models to choose from:

The schema of a star Data tables in a star schema are one-dimensional, meaning each table provides information about a single attribute, such as time, location, or sold units.
Snowflake diagram. A snowflake schema is more complicated but requires less storage space and is easier to manage. Its data tables are multidimensional, with related attributes separated into multiple tables rather than a single attribute. For instance, a table on sales might have a location feature that links to another table with more information, such as city and street. The city entry in the location’s table can also be linked to another table that contains information about the state, province, or nation where the city is located.
The diagram of the galaxy. A constellation schema is a hybrid between a star and a snowflake in that it can hold one-dimensional and multidimensional data tables.

The Advantages of a Data Warehouse

A data warehouse’s principal benefit is that it enables a corporation to analyze vast amounts of various sorts of data and keep a historical record of it. The advantages of a data warehouse, in particular, include the potential to:

Combine data from a variety of sources.
Maintain the highest standards of data consistency, correctness, and quality.
Perform data analysis without interfering with the organization’s transactional databases or the business processes they support.
Analyze historical data sources.
And depending on the foregoing, assist the company in making better judgments.

The Drawbacks of Using a Data Warehouse

Data warehouses have several disadvantages in addition to their many advantages. The following are some of the major concerns:

Scaling data warehouses can be expensive, and they’re not excellent at handling raw, unstructured, or complex data.
Operating a data warehouse may entail additional labor for some elements of the company. The IT team often generates the required data for each line of business or departmental activity, the data required is usually caused by the IT team. This might be as simple as copying data from a current database, but it can also entail obtaining extra information from customers, staff, or other sources.
A data warehouse is a large IT project that consumes many resources. It may not get enough use if it isn’t done well enough to justify the time and money spent.
Having so much critical information in one location poses a security risk. A data warehouse that leaks client information can be a nightmare regarding privacy compliance — and public relations.
The data warehouse’s structure may limit its flexibility and utility to the company. The data sets are generally static, and by the time they are used, they are days or weeks old, and the way the data is arranged may limit the types of queries that can be run. Furthermore, data warehouses are challenging to fine-tune for speedier searches and processing.

Examples of Data Warehouses

The following are three examples of how data warehousing is commonly used to assist company operations in three different industries:

Data warehouses are used in the financial and insurance industries to assess consumer and market trends. Foreign currency exchange (forex) and stock trading are two industries where data warehouses play a critical role, as tiny variations in these markets can result in massive losses. Data warehouses typically leverage real-time data streaming for financial and trading applications.
Data warehouses are primarily utilized with BI and forecasting applications in the retail industry. Tracking product performance, determining optimal pricing, assessing promotional efforts, and monitoring client buying habits are examples.
Data warehouses have become commonplace in the healthcare industry, where they’re used to predict treatment results, track and analyze population metrics, share data with insurance companies, and collect and evaluate research data.

Data Warehousing’s History

As computer systems became more common and advanced, and the amount of data they handled increased, the requirements for storing, accessing and analyzing that data grew significantly. The first attempts to improve data warehouse efficiency were made in reaction to this. They date back to when mainframes ruled the data processing industry and microprocessor-based personal computers had yet to be established.

Here are some of the significant turning points in the data warehouse’s development:

1960: Dartmouth University and General Mills collaborated on a research study that resulted in some of the first data tables and schemas concepts.
1970: Nielsen and IRI first launched data marts for retail sales.
1983: Teradata introduced a database management system intended primarily for decision assistance.

The Business Data Warehouse, created by IBMers Paul Murphy and Barry Devlin in the late 1980s, gave birth to the modern data warehouse concept. However, William Inmon is regarded as the “Father of the Data Warehouse” because he was the first to develop the idea and link it to the concept of a “Corporate Information Factory.”

Data Warehouses in the Future

The data warehouse of the future will be hosted in the cloud. The corporate world’s hunger for more data is being whetted by successful outcomes with big data and data analytics. A corporation can cost-effectively extend its data warehouse capacity to keep up with its ever-growing analytics requirements by placing it within cloud computing services.

Furthermore, a corporation with a cloud-based data warehouse will no longer have to worry about keeping its analytics software up to date, which is a crucial concern with on-premises data warehouses. That issue will vanish completely once accountability is delegated to a service provider, that issue will vanish totally. Cloud-based data warehouse deployments will become standard for these reasons, including improved security and lower startup costs.

For Today’s Data, a New Data Warehouse Is Born

Today’s businesses can’t compete unless they use their data to their advantage. To stay current with their offers and relevant to their clients, businesses of all sizes rely on data-driven insights. Companies need a lower-cost, easier-to-deploy, easier-to-use cloud-based data warehouse to fully use their data and extract all the insights they can.

The NetSuite Analytics Warehouse is a new cloud-based data warehouse based on Oracle Autonomous Data Warehouse and Oracle Analytics Cloud technology but explicitly designed for use with NetSuite’s cloud-based business applications. The NetSuite Analytics Warehouse is pre-configured to automatically translate and visualize NetSuite application data into data warehouse formats. It can be integrated and analyzed with data from many external sources to produce more powerful business insights. It will run queries rapidly and provide data analysts and business decision-makers more flexibility in slicing and dicing their data to fit a range of demands.

Businesses must empower everyone, from product engineers to sales managers, with data insights that enable people to perform their jobs more efficiently. In addition, employees need to engage in the kind of data analysis that leads to inventive work that propels a business forward as commerce increasingly transitions to the digital environment. Otherwise, they will simply be left behind by those who do. As a result, well-designed data warehouses, which serve as the cornerstone for business intelligence, have become a requirement for businesses of all kinds.

Frequently Asked Questions about Data Warehouses

Q: What is the purpose of a data warehouse?

A: Without the limits of a traditional database, a data warehouse can be utilized to examine a wide range of business data. Unlike conventional relational databases, it can evaluate data from several sources and retrieve data from various types of storage systems. It also protects a company’s data’s integrity by allowing users to query it without mistakenly altering or upsetting it.

Q: What does a data warehouse look like?

A: Data warehouses are utilized in the retail industry for forecasting and business intelligence. Tracking product performance, determining ideal pricing, reviewing promotional techniques, and studying client purchasing habits are examples of applications.

Q: What is the procedure for data warehousing?

A: A data warehouse is a system that collects and organizes massive amounts of data from various sources. Over time, it accumulates a historical record that can be extremely useful to data scientists and business analysts. The data is of the greatest quality, and the records kept in the data warehouse are frequently regarded as definitive, serving as an organization’s “single source of truth.”

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