“The study of Data Warehousing, like the Nile, begins in Inmon and ends in magnificence.” –Charles Caleb Colton, English Writer
In simple terms, a data warehouse is described as a system that is responsible for aggregating data from various sources to a single and consistent data store. Modern businesses are utilizing data warehouses to prepare data for machine learning, artificial intelligence, business intelligence, and data analytics initiatives.
Much contrary to the traditional databases, a data warehouse optimizes real-time operations for aggregating, reading, and analyzing the data. One of the most important functions of data warehousing is to support business intelligence efforts and ensure data quality over time. This blog will discuss ‘data warehouse’ concepts, structure, types, and applications in very simple terms for beginners to understand without any difficulty.
What are Data Warehouses?
Data warehouses, also known as central repositories, are used to store and analyze information to assist in business-related decisions. This data is typically collected from different sources such as relational databases, transactional systems, etc. In many scenarios, this technology is used to facilitate searches that involve huge amounts of data.
Types of Data Warehouses
Typically, data warehouses follow a layered architecture that ensures effective data storage. The crucial components of a data warehouse are:
- Data Source – There are both internal and external systems from where data is collected. Some of them are external APIs, CRM systems, transactional databases, etc.
- ETL – This process is also known as Extract, Transform, Load, which is responsible for extracting data from sources and storing it in the data warehouse.
- Data Storage – This is the third layer of the data warehouse that is optimized for query performance with the help of columnar storage and relational databases.
- Data Marts – It is a subset of the data warehouse, which is customized as per requirements such as business units, specific departments, that help to provide accurate data views.
- Business Intelligence Analytics – These interfaces and tools help users to analyze and visualize data to make better decisions in business.
The Architecture of Data Warehouses
The architecture is quite simple as it comprises only three layers: the bottom tier, the middle tier, and the top tier.
Bottom Tier
This usually represents the relational database system. It comprises the back-end tools that are used to clean, transform, and store data in the layer.
Middle Tier
This represents an OLAP server, which is typically incorporated in two ways:
- The extended relational database management system termed ROLAP or Relational OLAP model that maps multidimensional data processes into standard relational processes.
- Another database management system called MOLAP or multidimensional OLAP that acts directly on the multidimensional data and operations.
Top Tier
The top tier is a front-end client interface that helps the user to perform ad hoc data analysis and generate reports. This layer comprises analysis tools, query tools, data mining tools, and reporting tools.
Database vs. Data Warehouse
Even though both databases and data warehouses are mainly used to store data, there are some significant differences as well.
One major difference is that data is collected mainly for various transactional purposes in the case of a database, whereas for a data warehouse, data is collected for analysis. Another big difference is that databases offer real-time data for analysis, whereas data warehouses store data that can be accessed by the user later for solving big analytical queries.
For instance, a data warehouse is a prime example of an OLAP system (online database query system) ideal for large-scale data analysis. Databases, on the other hand, prioritize CRUD operations.
Benefits of Data Warehouses
There are many benefits of data warehouses:
- Better decision-making – With a data warehouse, you can perform data management and share accurate insights with stakeholders, leading to data-driven decision making.
- Improved scalability – Modern warehouses have the ability to scale and accommodate increasing data volumes and meet user demands.
- Data Analysis – Data warehouses facilitate the long-term storage of data, which assists with trend analysis and forecasting.
- Enhanced Performance – Many reports have proved that optimized storage helps enhance query speed for various analytical tasks.
- Data Consistency – Standardized cleaning processes and data formats are useful in improving both reliability and overall accuracy.
How to Learn Data Warehousing?
As the field of data science is evolving and the dependency on this technology is growing rapidly, this is the right time to build a future in data science. If you are eager to learn data science or specifically any tool of data science, then it is best to opt for a full-time certification from a reputed institution.
Here is a brief guide to help you achieve the goal step-by-step:
- Learn the basics – It all starts with the basics! Get used to mathematical and statistical concepts as these are widely used in data science fields to solve complex queries. Also, learning SQL, relational databases, and data modeling is recommended.
- Learn the tools – Be it data warehousing or data analytics, a lot of tools are used to conduct these programs. You can study tools like Apache Nifi, Tableau, Informatica, etc. to get a better idea.
- Pursue data science courses – Having a certification from globally recognized institutes like MIT, United States Data Science Institute, Princeton University, etc. not only enhances your resume but also gives you industry-relevant skills as it offers opportunities to work on various data science projects.
Conclusion
Data warehouses are crucial for modern businesses to perform effective data analysis. Hence, more and more sectors are implementing this technology to gain strategic advantages. With a centralized platform for data storage, integration and analysis, business leaders can make informed decisions and improve efficiency to maintain a competitive edge. Hence, learning the concepts of data warehousing can pave a way to excel in any data-driven roles for professionals.
