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A Data warehouse can be easily related to a typical warehouse that you are familiar with, it’s a warehouse where historical objects (here data) are stored with the plan for using them later in the time. It is a central repository of long historical data from multiple sources that is to be analyzed to get insights and answers to important business questions and improvisation.
Data comes from multiple databases in a data warehouse like Transactional Databases, operational databases and other sources which is transferred periodically as per the need or architecture of the data warehousing. The Data Warehouse operates on OLAP (Online Analytical Processing) instead of OLTP (Online Transactional Processing) in the databases, making it much more suitable for Business Intelligence. This article will explain OLTP and OLAP later.
Business intelligence is designed to optimize the growth rate of a company to the best possible in the market. In BI, we analyze the trends and convert them into an action plan for the company. BI has become indispensable to compete in the market, which can help you have a breakthrough in the business competition. In BI, we transform the data into information to understand and do analysis for the solution delivery.
Business Intelligence is based on the Data warehousing, where the data is transformed (Clean and Integrated) and processed as per the schemas decided for them. After transforming and processing the data, it is called information. Now, we use respective tools for the data visualization process. The objective of which is to represent that information through graphics, infographics, dashboards, etc., i.e., in such a way, users can easily understand and utilize it.
The need for data warehousing emerged as businesses started relying on computer systems for the creation, storage, and retrieval of important business documents. The concept of data warehousing was first introduced in 1988 by IBM researchers Barry Devlin and Paul Murphy.
Data warehousing aims to facilitate the analysis of historical data. This enables comparison of data consolidated from various heterogeneous sources to gain insights into a company’s performance. Users of a data warehouse can execute queries and analyses on historical data extracted from transactional sources.
Data added into the warehouse remains unaltered and cannot be changed. The warehouse serves as the foundational source to run analytics on past events, with a specific focus on tracking changes over time. Ensuring secure, reliable, and easily retrievable data storage and management is vital for a data warehouse.
Why data warehouse? All the businesses and organizations work on multiple databases (like Oracle, SQL, MySQL, etc.) for their operations and projects in the business. So, it becomes obvious to ask why we need a data warehouses as separate entities to store the data from their databases. This will take an extra step of transferring that data to that entity and required space that is almost 100 times more than a regular database. Here is some food of thought for you.
We need data warehouse concept for multiple reasons. Its understanding goes parallel with the knowledge about importance of a Data Strategy and it’s impact on the business. For a more focused spotlight on the data warehouses, let’s discuss this with the following points-
Using multiple databases is good for having proper efficiency in the respective projects and departments. But to have the overall view of the projects and understanding their collaborative effort & interrelated impacts on each other we need a data warehousing system and necessary BI tools to work on it. Though which we can make better collaborations and have a more effective business roadmap for your company.
When it comes to make some important business decision in the company, it is not that simple and may need a lot of time and risk analysis of it. But having a big data warehouse and proper BI support we can at least have the direction and support of the factual information in the decision making process. Which not only makes the decision faster and reliable but also enthuse the confidence in its implementation, which brings the higher probability of success. This not only have its impacts on the company growth but also builds a strong market reputation.
Query results in a data warehouses is always more reliable and faster as compared to any typical database. This is due to the fact that here we have a long historical data support which is more accurate due to the fact of its high stability data and integrity from all the data bases across your company. Moreover performing a query in a data base will slow it down making it unusable for the faster writes speeds, which are required by the projects and people operating in that database. This is the main reason why we use BI with data warehouses and not with the data bases, by doing so performance of both analytical processing and transactional processing are optimum.
As per the statements given by the Father of Data Warehousing “Bill Linmon” the following properties depicts what is Data Warehousing. Exact data warehouse definition is “A Data Warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management’s decision-making process”. Let’s discuss the four properties which a business data warehouse must have:
There are some important data warehousing concepts or terminologies that you must know and understand before you build data warehouse. Then it will be helpful to understand its Architecture. As these terminologies will be used in explaining data warehouse management and architecture, Lets discuss them one by one:
These are the most common terminologies that are used to describe the functioning of the data bases and data warehouses. This comparison is also between the data bases and data warehouses, as we are comparing the fundamentals on which they are made-
It stands for Extract, Transform and Load. It is the process of extracting the data from various sources, transforming them as per the predefined schema or format (decided as per the requirements like: Star schema, snowflake schema, etc.) and finally loading that up into the warehouses. All this is done using ETL tools/data warehouse tools like Talend, INFORMATICA, etc.
It is pretty similar to a data warehouse but a mini version of it, the data mart aims to extend the functionality of data warehouses. Data stored in a data mart is specific to a single subject or department in the whole organization, like sales data mart would be accessible to sales to team only, marketing data mart would be accessible to the marketing teams only, etc. A data mart aims to have better security and integrity in the whole system.
It refers to the data about your data. To elaborate, it contains the information about source of the data, type of the data, etc. It helps keep track of the data and automate the data ingestion process by defining the source, target, and corresponding data transformation using defined business logic.
To build a data warehouse for your organization it is essential to understand it’s architecture. There are various types of data warehousing as per the purpose or desired output from the warehouse, like enterprise or business data warehousing, operational data store and data mart.
Data warehouse architecture can be divided into three divisions. the first division is front end where the analytics and reports are presented using different data visualization tools. The second division consists of the analytics engine used to access and analyze the data and the third division consists of the data base servers where the data is stored.
Though the phases remain the same in every data warehouse architecture, the functionality and operations performed depend upon the organisation’s use case of the data warehouse. Like many companies, data mining and machine learning are apart from the analytics and reporting in the first division. Now let’s understand the complete architecture with an image:
As shown in the image, the data is extracted from the multiple data sources for your data warehouse for the ETL process. ETL process is done by using the different tools like Apache Nifi (which is open source and free), Informatica, Talend, etc. After the extraction it is temporarily stored in a staging area, which is present in the tool itself. Here it is transformed and processed as per the schemas and format.
After this it is stored into the data warehouse repository, with some additional information about the data. Here the data generally have three divisions: Meta Data (That we have discussed above), Raw Data and Aggregated Data. Due to having more than just the raw data in the repository, makes the data warehouses around 100 times more space demanding in with respect to data bases.
Data warehouse can now be further extended using data marts for each department, projects, or user groups in the organization like sales, marketing, operations, etc. Each uses the data they needed without accessing data that is not related to their workspace. This enhances the data security and accessibility of sensitive data.
This is front end of the data warehouse, here we have values to deliver, things like reporting, analytics, mining, deep leaning are executed using the respective tools like data visualization for making reports, dashboards, analytics presentations, etc. For prediction, we usually use machine learning or AI models on the data.
Data warehouses store the data in two different ways: the data that needs to be regularly accessed is usually stored in very fast storages like SSD drives. The data that is not used on frequent basis is stored in more economical storages like Amazon S3, etc. The data stored in them is automatically shifted to their storage type by the warehouse itself.