What is a Data Warehouse – All You Need To Know

Summary: A data warehouse centralises and integrates data from various sources to support analytics and reporting. It enhances decision-making, enables accurate predictions, and manages diverse data types efficiently. Modern data warehouses offer faster processing and real-time integration compared to traditional systems.

Introduction

If you’re looking to understand the most essential concepts of what is a data warehouse, then this post is definitely for you!

Companies are collecting more data than in previous decades, and those with a competitive edge are leveraging it to their advantage. The operational database is a separate environment from the analytical database and can run many analytical queries.

Today’s enterprises or businesses depend on efficiently gathering, storing, and integrating data from various sources for analysis and understanding. These data analytics tasks now form the core of profit maximisation, cost control, and revenue development. 

As a result, it is not surprising that the quantity and variety of data sources have increased. Further, the amount of data collected and evaluated has multiplied. It also increases the need for repositories or warehouses where we can store data efficiently.

Further in the article, we will explore the other areas of data warehousing and how they work together to store all the data efficiently.

Read Blog: Exploring Differences: Database vs Data Warehouse.

Benefits of a Data Warehouse

In the above section, we talked about what a data warehouse is. Let’s move further to know its benefits. There are so many benefits of data warehousing let’s understand them one by one –

Helps businesses in decision-making

A data warehouse allows businesses to utilise better all their data, including sales, financial, and marketing data. It makes retrieving and analysing this information easy and ensures that the data is clean and standard. 

A data warehouse also helps businesses make more informed decisions. Data warehouse users can leverage historical data to make smarter decisions and drive greater efficiency.

Data has an incredible dollar value in today’s economy, and a data warehouse will help businesses create more standardised, higher-quality data that translates into significant revenue gains. Better business intelligence also helps companies make better decisions, resulting in a greater return on investment and a more substantial business over time.

Ability to make accurate predictions

Another benefit of a data warehouse is its ability to make accurate predictions. Predictive analytics enables organisations to detect inefficiencies before they happen, which saves money. 

Companies can also use predictive analytics to find and respond to business opportunities before they arise. Building a full-scale data warehouse takes time and money, but a modern data warehouse can be built quickly and without a significant initial investment.

Manage all kinds of data easily

A data warehouse can combine all data types and create a holistic view of customers and prospects. By analysing data from multiple sources, companies can make more informed decisions and refine their products and messaging, ultimately reducing customer churn. 

So, with the help of a data warehouse, you can easily store all kinds of crucial data.

Save time

Business users can swiftly make educated decisions on important initiatives since they can easily access crucial data from several sources on a single platform. They won’t spend time gathering information from various sources.

In the absence of any assistance from IT, users may query the data on their own, saving even more time and expense. As a result, business customers won’t have to wait for IT to provide the reports, and diligent IT analysts can concentrate on keeping the company functioning.

Customised data

A data warehouse can create a comprehensive view of business data that can be customised to address specific departmental needs. In the healthcare industry, a data mart stores patient information, including financial and insurance data. Healthcare data marts can also store personal information and feedback.

Provide a secure & regulatory system of data

A data warehouse can also help your organisation with security and regulatory compliance. Cloud-based solutions use strong encryption, including AES256, and they provide access control, user authentication, and identity management features. 

However, a data warehouse built in the cloud will be far more flexible than one built in-house. Using a cloud-based data warehouse can help companies scale the size of their warehouse as needed. However, they will need to optimise the data warehouse regularly.

Data Warehouse Architecture

After learning what a data warehouse is, it’s time to understand its architecture and many other things. It relies on computing resources provisioned on-premise. 

However, data warehouses also have cloud-based alternatives. The cloud-based data warehouse architecture is significantly faster than its on-premises counterpart. Cloud data warehouses rely on an ELT process to extract data from different sources, allowing data to be retrieved much faster.

While it is highly customisable, best practices and frameworks are common. Some popular architecture standards include 3NF, Data Vault modelling, and star schema. 

The architecture is designed to ensure a single source of truth for businesses. This helps to support data consolidation and automation. It also allows for metadata sharing and coding standards to ensure system efficiency.

Traditional data warehouse architecture generally consists of three tiers: a database server at the bottom tier, which stores data, and an OLAP server at the middle tier. The top tier is a front-end client layer and consists of query and reporting tools. Some modern data warehouses also incorporate data mining functionality.

Metadata is a critical component of an EDW. It helps users access and analyse the data. Different metadata types describe various aspects of the data, including its origin, contents, purpose, and lifecycle.

Features of Data Warehouse Architecture

Data warehouses have many different components, but their primary purpose is to serve as a central repository for data from all departments and lines of business. They enable companies to process data for business intelligence, reporting, and analysis. 

They also help organisations reduce costs and streamline data access. So, The design depends on the system’s purpose and function.

The architecture should be flexible and scalable. It should also allow for adding new integrations into the data warehouse. Moreover, the architecture should support the development of analytical reports and be extensible. The architecture should be based on standard best practices and support future growth. 

Different architectures have different features. For example, one type can store unstructured data, while another may use structured data. Both types of data stores can be used together.

The Evolution of Data Warehouses—From Data Analytics to AI and Machine Learning

Data warehousing is not a new concept; it has just evolved with technological advances. Here in this section, we will talk about the evolution of data warehousing –

• Punch cards & paper tapes were the first data warehousing devices for data storage. After some years of technological advances, magnetic tape technology started to emerge.
• Magnetic tapes can be used to read and write data; however, they are not a reliable way to store data. Disk storage can help store and access large volumes of data.
• Then, there is DBMS storage, 4th generation (4GL), and personal computers.

What is a Cloud Data Warehouse?

To help the company operate, accessing and analysing all the data gathered from numerous sources and stored in a data warehouse is possible. The Internet of Things, relational databases, data systems, and numerous data streams are potential sources of this information.

Cloud-based data warehouses benefit from the critical advantages of on-demand computing, such as –

•     broad user access
•     seemingly endless storage
•     increasing computing power
•     flexibility to extend while only paying for what is required

What is a Modern Data Warehouse?

It is an advanced system designed to handle and analyse large volumes of data efficiently. At its core, it adheres to an extract-load-transform (ETL) architecture. Data is first extracted from various sources in this setup and loaded into the data warehouses. 

Unlike traditional methods, where transformation occurs before loading, modern data warehouses perform most transformation operations directly within the warehouse system. This approach allows for more streamlined processing and faster data transformations.

Speed is a critical factor for modern data warehouses. They are optimised to handle high-speed data transformations, ensuring data is processed and available for analysis as quickly as possible. This rapid transformation capability supports complex querying and analysis, which is essential for deriving actionable insights.

Moreover, modern data warehouses are increasingly incorporating real-time data streaming capabilities. By integrating streaming data, these systems become more responsive and capable of handling continuous data inputs. This enhancement allows businesses to make timely decisions based on current data, significantly improving their operational agility and competitive edge.

Designing a Data Warehouse

Before building a warehouse, consider your business needs and goals. Create a data model, document your data sources, and develop business rules. It should be flexible enough to accommodate future expansion and easy to maintain and expand as your business evolves.

A data warehouse’s logical and physical structure must support business goals and key performance indicators. A good design must also account for the limitations of source systems, challenges in joining data from different sources, and the possibility of future changes in business needs or source system structures. 

Future posts will discuss specific considerations and the ideal structure.

Determine the requirement

The first step in designing a data warehouse is determining what data it needs to hold. In most cases, the end user wants aggregated data. However, knowing what they’ll need is often difficult until a problem arises. 

That’s why it’s so important to thoroughly explore their needs. The architecture should also be flexible and expandable, allowing future growth.

Development

The second step in designing a data warehouse is the development environment. This environment contains test cases that use data from the data warehouse. This helps identify errors that propagate from development to testing. 

Further, it ensures that its functionality is maintained and security requirements are met. After completing the development and testing phases, the Warehouse will be ready for production.

Designing a data warehouse can help your organisation conduct logical queries, create accurate forecasting models, and identify impactful trends. However, building a data warehouse is a lengthy and error-prone process. In addition, the information that it should contain also depends on decision-makers needs in different business stages.

Modelling

A data model provides a framework that guides the overall data architecture of the data warehouse. It impacts how the data warehouse should be structured and what ETL tools should be used to extract and load the data. 

ETL is a process that pulls data from different sources, including existing storage solutions. It’s crucial to choose the right ETL tools for your project carefully.

The first iteration of the Data Warehouse is to provide the business with an initial view of the data warehouse. It helps them better articulate their requirements. 

This step is a learning process for the team. It should showcase standard reports, dashboards, scorecards, and ad hoc analytics. For its first iteration, the Data Warehouse should be implemented in a sandpit environment. However, the expectations should be low.

Mapping entities

A traditional design approach suggests mapping high-level entities to “loosely normalised” tables. Loose normalisation improves query and population performance and provides functionally neutral data.

Do I Need a Data Lake?

You may be wondering if you need a data lake. If your company has bottlenecks in data handling, it might be time to create one. Machine Learning and analytics also use data from a data lake. 

There are several benefits of having a data lake –

• A data lake stores all your data – including data not currently being used and permanently stored. A data lake differs from a data warehouse because it retains data simultaneously. Its hardware will be different from that of a data warehouse.
•  Data Democratisation refers to Data lakes that can make data available to the entire organisation. 
•  Top executives can request reports from different departments. Middle management, however, cannot request data from other departments. Additionally, requesting data from various departments can be time-consuming. Hence, data lakes are essential for democratising information.
•  Another advantage of a data lake is that it allows data scientists and engineers to do experiments and analyses of the data.
•  A data lake can store raw, semi-structured, and structured data from all stages of the refinement process.
•  Data lakes are often referred to as a “big data” solution, they do not meet all your business needs. 
•  Unifying the data into a single data lake will simplify your architecture and give you the power to leverage data analytics and Machine Learning.

Must Explore: Data Lakes Vs. Data Warehouse: Its significance and relevance in the data world.

Why Not Run Analytics Against Your OLTP Environment?

Running complex analytics against an OLTP (Online Transaction Processing) environment is generally not advisable. OLTP systems excel at handling high-speed transactions involving frequent updates, insertions, and deletions. 

They are designed for operational efficiency, focusing on processing individual transactions quickly and accurately. Queries in OLTP systems typically retrieve a few records, which is optimal for tasks like real-time order processing or customer record management.

However, conducting detailed analytics in an OLTP environment can be problematic. The nature of OLTP systems means they are not optimised for handling large-scale analytical queries that require scanning extensive datasets. 

Such queries can be resource-intensive and may slow down the system, affecting the performance of real-time transactional operations. OLTP systems often lack the indexing and optimisation features necessary to execute complex analytical queries efficiently.

A dedicated OLAP (Online Analytical Processing) environment or data warehouse is more suitable for intricate analyses. These systems are designed to handle large volumes of data and complex queries, allowing for comprehensive analysis without impacting the performance of transactional systems. Organisations can ensure operational efficiency and robust data insights by separating transactional and analytical workloads.

Zero-Complexity Deployment: The Autonomous Data Warehouse

The autonomous data warehouse is the most recent generation of the data warehouse. It uses Machine Learning and artificial intelligence to do away with manual processes. It also uses ML to streamline setup, deployment, and data administration. 

A cloud-based autonomous data warehouse does not require software installation, hardware configuration or management, or database administration.

Constructing the data warehouse, updating, extending, backing up the database, and changing the database’s size are all carried out automatically. The autonomous data warehouse works with the same adaptability, scalability, speed, and cost savings that cloud platforms offer. 

It streamlines deployment, reduces complexity, and frees up resources so businesses can concentrate on tasks that improve the company.

Frequently Asked Questions

What is a data warehouse?

A data warehouse is a centralised system that stores and manages data from various sources. It supports complex queries and analysis, allowing businesses to consolidate and integrate data efficiently. It enables effective decision-making, reporting, and data-driven insights across different departments.

What are the benefits of using a data warehouse?

Data warehouses provide numerous benefits, including improved decision-making through consolidated and clean data, the ability to make accurate predictions using historical data, and efficient management of diverse data types. They save time by centralising data access and supporting secure, customisable, scalable data management solutions.

How does a modern data warehouse differ from traditional ones?

Modern data warehouses use cloud-based infrastructure for faster processing and real-time data integration. Unlike traditional methods, they perform data transformations within the warehouse system. This setup offers enhanced speed, scalability, and flexibility, supporting complex queries and high-speed analytics more effectively than conventional data warehouses.

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Conclusion

Data warehouse architecture is a design for storing and retrieving data. Its tiers comprise a data layer, a storage area, and a data mart or data lake. A semantic layer then restructures the data for analytics. The rest of the data warehouse details are discussed in the article.