If you’re looking to understand the most important concepts of what is data warehouse, then this post is definitely for you!
Companies are collecting more data than in previous decades. Those with a competitive edge are leveraging it to their advantage. It is a separate environment from the operational database and can run a high volume of analytical queries.
Enterprises or businesses of today depend on the efficient gathering, storing, and integrating of data. This is done from various sources for analysis and understanding. These data analytics tasks now form the core of profit maximization, controlling costs, 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 efficiently store all the data.
Benefits of a Data Warehouse
In the above section, we talked about what is 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 better utilize all their data. This includes sales data, financial information, and marketing data. It makes it easy to retrieve and analyze this information. It also ensures that the data is clean and standard. A data warehouse also helps businesses make more informed decisions. Users of a data warehouse 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 standardized, higher-quality data that translates into significant revenue gains. Better business intelligence also helps companies make better decisions, which in turn results in a greater return on investment and a stronger business over time.
Ability to make accurate predictions
Another benefit of a data warehouse is its ability to make accurate predictions. Predictive analytics enables organizations 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 in a short period of time and without a large initial investment.
Manage all kinds of data easily
A data warehouse can combine all types of data and create a holistic view of customers and prospects. Companies can make more informed decisions and refine their products and messaging by analyzing data from multiple sources, ultimately reducing customer churn. So with the help of a data warehouse, you can easily store all kinds of crucial data.
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 & expense. Business customers won’t have to wait for IT to provide the reports as a result, and diligent IT analysts can concentrate on keeping the company functioning.
In addition to creating a comprehensive view of business data, a data warehouse can be customized to address specific departmental needs. In the healthcare industry, a data mart is used to store patient information, including financial and insurance data. Healthcare data marts can also store personal information as well as feedback.
Provide a secure & regulatory system of data
A data warehouse can also help your organization with security and regulatory compliance. Cloud-based solutions use strong encryption, including AES256, and they provide features for access control, user authentication, and identity management. However, a data warehouse built in the cloud will be far more flexible than a data warehouse 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 regularly optimize the data warehouse.
Data Warehouse Architecture
After knowing what a data warehouse is, now it’s time to understand its architecture and many other things. Data warehouse architecture relies on computing resources provisioned on-premise. However, Data warehouse also has 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. This allows data to be retrieved much faster.
While data warehouse architecture is highly customizable, best practices and frameworks are common. Some popular architecture standards include 3NF, Data Vault modeling, and star schema. The architecture is designed to ensure that there is 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.
In addition to the data in an EDW, metadata is also a critical component. This helps make it easier for users to access and analyze the data. Different metadata types describe different aspects of the data. The metadata describes the origin of the data, its contents, and its purpose. It also describes the lifecycle of the data.
Features of Data Warehouse Architecture
Data warehouses have many different components, but the primary purpose is to serve as a central repository for data from all departments and lines of business. It enables companies to process data for business intelligence, reporting, and analysis. They also help organizations reduce costs and streamline data access. So, the design of a data warehouse depends on its purpose and function.
The data warehouse architecture should be flexible and scalable. It should also allow for the addition of new integrations into the data warehouse. Moreover, the architecture should support the development of analytical reports and should also be extensible. The architecture should be based on standard best practices and should support future growth.
Different data warehouse 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
What is a data warehouse: Data warehousing is not a new concept it just evolves 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 used for data storage. After some years of technological advances, magnetic tape technology started to emerge.
- Magnetic tapes can be used to write as well as write data, however, they are not a reliable way to store data. Disk storage can help in storing and accessing large volumes of data.
- Then DBMS storage, 4th generation (4GL), and personal computer.
What is a Cloud Data Warehouse?
To help the company operate its operations, it is possible to access and analyze all the data gathered from numerous sources and stores in a data warehouse. The internet of things, relational databases, data systems, and numerous data streams are possible sources of this information.
Cloud-based data warehouses benefit from the key 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?
Modern data warehouses follow an extract-load-transformation (ETL) architecture, where the transformation of data takes place within the data warehouse system. The database performs most of the ETL operations. The speed of data transformation is a key element of a data warehouse. In real-time applications, data warehouses may incorporate streaming data to make them more responsive.
Designing a Data Warehouse
Before building a data warehouse, consider your business needs and goals. Create a data model, document your data sources, and develop business rules. A data warehouse should be flexible enough to accommodate future expansion. It will also be easy to maintain and expand as your business evolves.
The logical and physical structure of a data warehouse 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 to determine what data it needs to hold. In most cases, the end user wants aggregated data. However, it’s often difficult to know what they’ll need until a problem arises. That’s why a thorough exploration of the needs of end users is so important. The data warehouse architecture should also be flexible and expandable, allowing for future growth.
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 data warehouse functionality is maintained and security requirements are met. After completing the development and testing phases, the Data Warehouse will be ready to go into production.
Designing a data warehouse can help your organization to 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 a data warehouse should contain also depends on the needs of decision-makers in different business stages.
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 carefully choose the right ETL tools for your project.
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 some standard reports, dashboards, scorecards, and ad hoc analytics. Data Warehouse should be implemented in a sandpit environment for its first iteration. However, the expectations should be low.
A traditional design approach suggests mapping high-level entities to “loosely-normalized” tables. Loose normalization improves query performance and population performance. Additionally, it 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 the way it handles data, it might be time to create a data lake. Machine learning and analytics use data from a data lake as well.
There are several benefits of having a data lake –
- A data lake stores all your data – including data that is not currently being used and is permanently stored. A data lake differs from a data warehouse because it retains data at any time. Its hardware will be different from that of a data warehouse.
- Data Democratization refers to Data lakes that can make data available to the entire organization.
- Top executives can ask for reports from different departments. Middle management, however, cannot ask for data from other departments. Additionally, requesting data from different departments can be time-consuming. Hence, data lakes are essential for democratizing 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 be used to store raw, semi-structured, and structured data. A data lake can store data from all stages of the refinement process.
- Data lakes are often referred to as a “big data” solution, they do not meet all of 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.
Why Not Run Analytics Against Your OLTP Environment?
You can extract data for intricate analyses using OLTP systems. The searches frequently involve enormous volumes of records in order to influence business choices. OLTP systems, on the other hand, are perfect for doing straightforward database updates, insertions, and deletions. Most of the time, the queries only return one or two records.
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. There is no need for software installation, hardware configuration or management, or database administration in a cloud-based autonomous data warehouse.
Constructing the data warehouse, updating, extending, as well as back up the database, and changing the database’s size are all carried out automatically. It works with the same adaptability, scalability, speed, and cost savings that cloud platforms offer. The autonomous data warehouse streamlines deployment, reduces complexity, and frees up resources so businesses can concentrate on tasks that improve the company.
Q 1. What is a data warehouse?
A data warehouse is a comprehensive system that stores and manipulates data. It can be used to store, analyze, and interpret data. Data warehouses are often recommended for businesses that need to track customer data, analyze its performance, or plan marketing campaigns.
Q 2. How important is data warehousing?
A data warehouse implementation could assist a business in avoiding different issues. It is insufficient to make decisions on your own in a time of fierce competition. It must be taken promptly because if you miss it, you will see your rivals overtake you in the race.
Q 3. How is the future of data warehousing?
Data warehouses are becoming increasingly durable because of simplified support and architecture. Several systems make it simple for businesses to move their data to the cloud and offer the flexibility to conduct analytics from any location.
Q 4. What is the difference between data mining and data warehouse?
Data mining is the process of automatically extracting data from databases, whereas data warehouse is the method of gathering as well as organizing data into a single shared database.
Q 5. What is the difference between a Big Data engineer, a Data engineer, and a Data-warehouse engineer?
Data warehouse engineers tackle data processing as well as storage tasks related to constructing a data warehouse, whereas data engineers tackle data transformation as well as storage tasks for any purpose.
Q 6. What is the difference between a database and a data warehouse?
databases are arranged groups of data that have been saved. Data warehouses are systems for storing and analyzing data that are constructed from many data sources.
Q 7. Why should I have a separate database and data warehouse?
To effectively implement full data analysis one should definitely have separate databases and data warehouses.
Q 8. When does one need a data warehouse?
If you want to analyze large volumes of information you definitely need data warehouses.
Q 9. What qualities make an effective data warehouse?
Relational databases, flat files, organized and semi-structured data, metadata, and master data are a few examples of these. When the sources are merged in a way that is reliable, relevant, and ideally certifiable, a firm can be confident in the accuracy of the data.
Data warehouse architecture is a design for storing and retrieving data. Its tiers are made up of 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 details regarding what is data warehouse is discussed above in the article.