Imagine you have data coming in from various sources and departments, such as marketing, sales, finance databases, and web analytics. How do you make your decisions? Do you manually sit and combine data from each source to make sense of it?
You could do that if you have a few data sources and a small volume of data, but today when you have large volumes of data coming in from left and right, how do you even combine it? This is where data integration and the data warehouse comes in. It centralizes all your data into one place, so you can easily get insights that is based on accurate and holistic data.
Let’s discuss what is a data warehouse, understand its processes, concepts, and benefits, and explore different types of data warehousing.
In this blog, you will learn:
- What is a Data Warehouse
- What are OLAP and OLTP
- History of Data Warehouses
- Types of Data Warehouses
- Data Warehouse Architectures
- Schemas in a Data Warehouse
- Data Warehouse Use Cases
- Benefits for Organizations
- How Astera Data Warehouse Builder Can Help?
What is a Data Warehouse?
A data warehouse is a relational database system businesses use to store data for querying and analytics and managing historical records. It acts as a central repository for data gathered from transactional databases.
It is a technology that combines structured, unstructured, and semi-structured data from single or multiple sources to deliver a unified view of data to analysts and business users for improved BI. Therefore, it is used for analytical and business reporting purposes, which helps keep past records and analyze data to optimize business operations.
A data warehouse is often confused with a database. However, there is a huge difference between the two.
While a database is merely a conventional technique to store data, a data warehouse is especially intended for data analysis. It keeps everything in a single location from numerous external databanks.
|To store historical data in an organized form to facilitate data analysis and reporting.
|To handle large amounts of queries for storing real-time data.
|Stores data from multiple sources.
|Usually designed for a specific business function.
|Tables are denormalized.
|Tables are normalized, i.e., split into many.
|Deepends on the frequency of data loading.
|Real-time. Data is constantly updated.
|Ease of Analysis
|Easier to conduct analytics due to organized data.
|Analysis is complex due to a normalized structure.
What are OLAP and OLTP?
Online Analytical Processing (OLAP) and Online Transaction Processing (OLTP) are two distinct concepts in data warehousing. Each serves a specific purpose and possesses different characteristics.
OLAP is a system that supports multi-dimensional data analysis at high speeds on massive volumes of data. This data is generally from a data warehouse, a data mart, or any other data store. OLAP helps analyze and understand historical data and is useful for performing these functions:
- Complex analytical calculations
- Sales forecasting business intelligence (BI)
- Data mining
- Financial analysis
- Sales forecasting
OLTP, on the other hand, is used for transactional processing and typically involves simple queries and updates on a large amount of data in real time by a large number of users. These transactions generally take place over the internet.
It supports high-concurrency processing and is optimized for swift update and delete operations. OLTP is the main process behind ATM transactions, in-store purchases, and hotel reservations.
|Conducts complex data analysis for smart decision making
|Conducts large transactions by multiple users in real-time
|Designed for use by data scientists and knowledge workers
|Designed for frontline workers such as bank tellers, cashiers, and front desk officials
|Data source has multiple schemas to support complex queries from current and historical data
|Relies on traditional database management systems to accommodate massive data volumes in real-time transactions
|Requires read-intensive workloads and involves large data sets
|Workloads are based on simple read-and-write operations via Structured Query Language (SQL)
History of Data Warehouses
The concept of a data warehouse dates back to the early 1980s. The early data warehouses were dependent on a large amount of redundancy. They were mainly used for data analysis, business intelligence, and decision support systems (DSS).
Companies used to maintain several DSS environments that catered to various users. Even though these DSS systems use the same data, users must replicate the cleaning, gathering, and integration process for each environment.
Since its inception, the concept of data warehouses has evolved from information stores and expanded into broad analytics infrastructures that support performance management and operational analytics. Today, data warehouse processes have progressed into Enterprise Data Warehouse (EDW).
Types of Data Warehouses (DWH)
Typically, enterprise systems use three main types of data warehouses (DWH):
- Enterprise Data Warehouse (EDW): As a centralized data warehouse, EDW provides a holistic approach to organizing and presenting data.
- Operational Data Store (ODS): An Operational Data Store (ODS) is a type of data store that is suitable when neither the OLTP nor a DWH can support a business’s reporting requirements.
- Data Mart: A data mart is designed for departmental data, such as sales, finance, and supply chain.
Data Warehouse Architectures
A data warehouse relies on a three-tier architecture that includes:
- Bottom Tier (Storage Layer): This layer comprises the storage media, meta-repository, data marts, and database server
- Middle Tier (Compute Layer): The middle tier is the online analytical processing (OLAP) system. It processes complex queries and presents the outcomes in a suitable form for data analysis and business intelligence.
- Top Tier (Services Layer): This layer represents the user front end with a visual dashboard to enable analytics and reporting.
Schemas in a Data Warehouse
A schema in a data warehouse defines multiple ways to organize the system with database entities such as dimension table, fact tables, and their logical association. There are three main types of schemas in a data warehouse.
A star schema organizes data into a central fact table and a set of dimension tables. It’s used to denormalize the data and add redundant columns to dimension tables for faster data queries.
The fact table contains the measurements or metrics of the data, while the dimension tables provide the context for the data, such as time, location, or product details. A star schema adapts well to fit OLAP models for better query performance as compared to normalized data.
A snowflake schema is a variation of the star schema, where the fact table is connected with multiple normalized dimension tables. This means that they are split into multiple child tables. Compared with star schema, snowflake benefits from limited data redundancy, which helps improves data integrity but at the cost of reduced query performance.
Fact constellation involves multiple fact tables that share the same dimensions tables. The shared dimension in this schema is called conformed dimensions. A fact constellation is a hybrid of the star and snowflake schema. It uses both normalized and denormalized dimension tables.
Fact constellation schema is also called galaxy schema, as the arrangement of dimensions and fact tables resembles a cluster of stars in a galaxy.
Data Warehouse Use Cases
A data warehouse has numerous applications in the corporate world to facilitate business decisions. Let’s look at a few examples of how they are used across various industries.
For the retail industry, a good example would be a retail data mart that incorporates customer information from cash registers, mailing lists, websites, and feedback cards.
In healthcare, these central data stores are used to record patient information from different units of the medical unit. This would include patient personal information, financial transactions with the hospital, and insurance data. A data warehouse consolidates and connects all this data through the database schema.
Similarly, construction firms require data on every purchase made during the construction timeline. This purchase needs to be attributed to a source to make financial decisions. The same goes for the wages of contractual employees.
Key decision-makers can use the data recorded in a data store for business intelligence to estimate the company’s overall spending on a single construction site.
Banks, insurance firms, trading firms, and others related to the finance sector need accurate data at all times. Proper data validation in databases and appropriate connection with other tables in the database enables the possibility of achieving this.
These are just examples of how data warehouses are used widely in different industries and for various purposes. Since they are just an organized store of raw data, they can serve many purposes for the end-user.
Benefits For Organizations
Now that we know how data warehouses work let’s look at the benefits of data warehouses and how they can help your business grow and scale. Whether you own a digital marketing agency or have a traditional brick-and-mortar setup, data warehousing can yield several benefits for your business.
Below are 7 key benefits of data warehousing for your business:
1. Saves Time
In the modern, fast-paced world of cut-throat competition, your capacity as a business to swiftly make refined decisions is essential to outpace your opponents.
A DWH provides you access to all your required data in minutes, so you and your employees don’t have to dread an approaching deadline. You only need to deploy your data model to acquire data within seconds. Most warehousing solutions allow you to do that without using a complex query or machine learning.
With data warehousing, your business won’t have to rely on the 24/7 availability of a technical expert to troubleshoot problems associated with retrieving information. This way, you can save plenty of time.
2. Improves Data Quality
Maintaining data quality helps guarantee that your company’s policies are based on precise information about your corporate exertions.
You can transform data from multiple sources into a shared arrangement by understanding data warehousing. Consequently, you can ensure the reliability and quality of your corporate data. This way, you can identify and remove replicated, poorly recorded, and any other errors.
Photo taken from intellipaat.com/blog/tutorial/data-warehouse-tutorial/data-warehouse-quality-management/
Implementing a data quality management program and improving data integrity can be costly and laborious for your company. You can easily use a data warehouse to eliminate many of these annoyances while saving money and boosting your organization’s overall efficiency.
After all, poor data quality is a burden for your business and can decline the overall efficiency of your plans.
3. Improves Business Intelligence
You can use a data warehouse to gather, assimilate, and derive data from any source and set up a process to leverage business analytics. As a result, your BI will improve by leaps and bounds, owing to the capability of effortlessly integrating data from distinct sources.
Let’s face it: crosschecking numerous databanks can be challenging and sometimes inconvenient. But, with a data warehouse in place, everyone on your team can have an integrated understanding of all the relevant information on time.
Photo taken from www.cleveroad.com/blog/bi-developer-roles-and-responsibilities
An EDW allows your sales and marketing teams to track and identify which of your targets are dynamic and have accounts on social networking websites. So, if you’re running a promotion that targets females in their mid-twenties working in the beauty industry, your team can fetch profiles of your target audience using your data lake within seconds. They won’t even have to crosscheck worksheets and databanks.
4. Leads to Data Consistency
Another important benefit of using central data stores is the evenness of big data. Your business can benefit from data storage or data mart in a similar arrangement. As data warehousing stores large amounts of data from diverse sources, such as a transactional system, consistently, each source will generate outcomes synchronized with other sources.
This guarantees improved quality and consistency of data. Consequently, you and your team can feel assured that your data is correct, which will result in more mindful corporate decisions.
5. Enhances Return on Investment (ROI)
According to a report by the International Data Corporation (IDC), using a data warehouse generates an average 5-year ROI of 112 percent with an average payback period of 1.6 years.
It empowers you to increase your overall ROI by harnessing the value and insight implanted within numerous databanks. As you increasingly use the information consolidated and organized within the central store, you achieve more from your investment.
Thus, you can elucidate, enumerate, and validate the efficiency of your initiatives to higher management in terms of improved ROI.
6. Stores Historical Data
Storing large volumes of historical data from databases within a data warehouse allows for easy investigation of different time phases and trends, which can be highly impactful for your company. Thus, you can make superior corporate decisions concerning your business strategies with the right and real-time data.
Moreover, predicting the results of your business processes is a significant aspect of being a resourceful business person. Forecasting the future without a tangible understanding of your historical achievements and letdowns can be challenging.
For example, suppose you own a fashion brand. You plan to launch a promotional campaign for your new clothing line. Setting up a central repository enables you to access and analyze historical data from your previous campaigns to identify which approach worked the best and how you might emulate it in upcoming promotions.
You can’t expect to store and analyze such comprehensive past data in any conventional databank. Thus, using EDW gives you an advantage in your business procedures.
7. Increases Data Security
Did you know that complications related to data cost a large number of businesses more than a whopping five million dollars every year?
But, with data warehousing, you can save yourself from the hassle of additional data security.
As a business that deals with customer information regularly, your first and foremost priority is to protect your existing and prospective consumers’ information. Hence, to evade all future nuisances, you take all the necessary actions to escape data breaches. Using a warehousing solution, you can keep all your data sources consolidated and protected. This will significantly decrease the threat of a data breach.
A data warehouse allows improved security by offering cutting-edge safety characteristics erected into its setup. Consumer information is a valuable resource for any company. But once safety becomes a problem, this information becomes your main burden.
These are just a few advantages that data warehousing offers for your business. It provides improved business intelligence, robust decision support, superior business practices, and effective analytics processing.
How Astera Data Warehouse Builder Can Help?
An EDW may seem like a huge investment today. However, it can help you reap maximum profits in the future.
If you are looking for an easier way to implement a data warehouse, then Astera’s Data Warehouse Builder can help easily automate building an enterprise data warehouse. It expedites:
- Transforming and integrating disparate data
- Modeling of schema structure
- Delivers an agile data warehouse
- Through a unified and intuitive platform
Take advantage of this powerful product and create an agile data ecosystem today. Get in touch today or try our product Astera Data Warehouse Builder.