Last few decades have seen a revolution in terms of cloud-based technologies. These technologies allow organisations to seamlessly store and retrieve data about their customers, products, and employees. This data can then be used to gather actionable insights and take the organisation up the ladder.
While Big Data and Analytics deals with the actions performed on data AFTER it’s retrieved, the concept of Data Warehousing focuses on how that data is stored in the cloud. Many global organisations have embraced the concept of Data Warehousing to organise their data that streams from operational centers and corporate branches around the world.
The concept of data warehousing was absent till the Big Data boom happened. Before that, all the organisations used OLTP (Operational databases), which are suitable for managing, tracking, and analyzing day to day activities, but miserably fail when it comes to dealing with historical datasets that might span terabytes in size. An OLTP system is merely a relational database model that works on Entity-Relationship. While still used, OLTPs are slowly fading away owing to the colossal amounts of data with organisations today.
Enter: Data Warehouse!
What is a Data Warehouse?
The concept of Data Warehousing allows organisations to collect, store, and deliver decision-support data. The concept of data warehousing is broad, and a data warehouse is one of the artifacts created during the process of warehousing.
The term “Data Warehouse” was coined by William (Bill) H. Inmon back in 1990. According to Inmon, a data warehouse is merely a subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s decision-making process.
The OLTP we talked about earlier undergoes frequent changes (almost daily). So much so that it’s impossible for a business executive to analyse previous product feedbacks or complaints due to a lack of historical data.
A data warehouse, on the other hand, provides a consolidated data in a multidimensional view. It also provides OLAP (Online Analytical Processing) tools – which are of tremendous aid when you get down to analyzing the data you’ve stored. A Data Warehouse, unlike an OLTP, also supports operations such as Data mining, classification, clustering, and predictive analysis. Due to all these reasons and more, the concept of Data Warehousing has become an integral part of any organisation.
What is a data warehouse not?
People relatively newer to the concept of Data Warehousing often confuse a “data warehouse” for a “database”. However, let’s clarify this point before we move any further – a data warehouse is not just a database but more than that. It includes a copy of operational data which is collected from multiple data sources and comes handy during strategic decision making.
Some also believe that a data warehouse contains ONLY historical data. However, it’s far from the truth. A data warehouse can be made to include historical data, and also analytics and reporting data, too. The transactional data that is managed in data stores is, however, not stored in a warehouse. The purpose of using Data Warehouse is to analyse historical data and gain actionable insights seamlessly.
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Importance of Data Warehousing
By now we are on the same page regarding the concept of Data Warehousing, the need of it, and saw the significant differences between a Data Warehouse and an OLTP. Now, let us look at the importance of the concept of Data Warehousing:
Ensures data consistency
Data warehouses store data from various sources, and that data is in multiple formats. Hence, they are programmed to apply ETL methods to ensure that the data is overall consistent. Consistency is what makes data warehousing a perfect tool for corporate decision-makers to analyse and share data insights with their colleagues around the globe. Standardizing and formatting the data also reduces the risk of errors while data analysis; thereby providing overall better accuracy.
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Facilitate better decisions
“First comes data, then theories.” A data warehouse allows organisations to store and retrieve data with ease thereby ensuring better theories and strategies around that data. Data warehousing is also a lot faster regarding accessing different data sets and makes it easier to derive actionable insights.
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Improve their bottom line
A data warehouse helps in the improvement of overall operations of any organisation by allowing the stakeholders to dive into their historical data. This, eventually, enables business leaders to quickly track their organisation’s past activities and evaluate successful (or unsuccessful) strategies. This allows executives to see where they can adjust their approach to decrease costs, maximise efficiency, and increase sales to improve their bottom line.
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Some crucial terminologies in and around the concept of Data Warehousing:
Metadata is essentially just data about data. For example, if we talk about a book, its index can serve as metadata for the content of the book. In other words, metadata can be understood as the summary for the complete data.
In terms of data warehouse, we can define metadata as −
- A road-map to the data warehouse.
- A directory which helps the decision support system to locate the contents of a data warehouse.
A data cube is defined by dimensions and facts and helps us represent data in more than one dimensions. The dimensions are nothing but entities concerning which an organisation preserves the records. It is mostly used for storing data for reporting purposes. Each dimension of the cube represents a certain characteristic of the database, for example, daily, monthly, or yearly sales. The data included in a data cube makes it possible to analyse almost all the figures for virtually any of the customers, sales agents, products, and much more. Thus, a data cube can ideally help establish trends and analyse performance.
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A data mart can be understood as a repository of data built to serve a particular section of the organisation. A data mart contains one subset of the entire organisation data that is valuable to a specific group of people. For example, a data mart specifically designed for the marketing team might just contain only data related to items, customers, and sales. Data marts are confined to subjects in question.
of data warehousing along with the important terms and technologies. If you find it interesting, we recommend you go through this topic in depth by fiddling with the concepts of data mining, data analytics, and more. The journey is long, and data warehouse is just the starting point.
If you have any doubts or questions, do let us know in the comments below!
Why should a company leverage Data Warehousing?
Modern data warehousing systems simplify the time-consuming tasks of designing, building, and deploying a data warehouse to meet rapidly changing company needs. As a result, many companies use data warehousing solutions to acquire information. Improved data analytics, greater revenue, and the capacity to compete more strategically in the marketplace are all advantages of having a data warehouse. Data warehouse tools make use of a variety of related technologies, such as structured and unstructured data, ETL software, and data mining, to achieve these benefits.
Some key benefits of a Data warehouse include:
1. Allows for Historical Insight
2. Improves Data Quality and Consistency
3. Increases Productivity
4. Data Analytics Can Be Made More Powerful and Faster
5. Increases Revenue
6. Interacts with both on-premise and cloud-based systems
What is the best data warehouse tool to use?
Many businesses today rely on data warehousing tools. Choosing the right solution for managing and maintaining the data warehouse, as well as finding one that exactly suits the business goals and restrictions, can be difficult.
Here is an overview of some Data Warehousing Tools that businesses may use to extract useful data from their data warehouse:
1. Amazon Redshift : Amazon Redshift is a Data Warehousing tool that makes it feasible to examine data with existing Business Intelligence tools using simple SQL queries. It uses high-performance computation, parallel execution, uniform query optimization, and columnar storage to run sophisticated analytical queries. By default, Amazon Redshift encrypts its data at rest.
2. Google BigQuery : Google BigQuery is a serverless, cost-effective, and highly scalable data warehousing tool that includes machine learning and leverages the Business Intelligence Engine. It analyses petabytes of data at fast speed using the ANSI SQL language, provides insights and solutions from data across clouds through a flexible architecture, and can store and query enormous data sets in a cost-effective and efficient manner.
3. Microsoft Azure : Microsoft Azure is a Data Warehousing Tool that combines more than 200 products and cloud services that helps to design, run, and manage highly scalable applications across different cloud networks. It aids in the deployment of Windows and Linux virtual machines across a variety of cloud and hybrid environments.