A data warehouse is a modern solution that allows you to collect, store, and analyze all data types quickly and easily. With a data warehouse, you’ll have everything you need to make informed decisions about your business.
Please read this post for more information about the data warehouses.
A data warehouse is a data repository structured for reporting and analysis. It usually contains historical data that has been cleansed and transformed to meet the needs of the business.
A data warehouse is often used in conjunction with Business Intelligence (BI) tools to allow users to perform complex data analyses. BI tools can include reporting tools, OLAP cubes, and dashboards.
Few important aspects
- The data warehouse is a system that stores “big data.”
- It’s used to store and manage large amounts of information about business operations.
- The goal of a data warehouse is to provide fast access to the most relevant information for decision-making.
- A data warehouse can be created using a relational database management system (RDBMS) or an online analytical processing (OLAP) tool.
- RDBMS includes MySQL, Oracle Database, and Microsoft SQL Server; examples of OLAP tools include Cognos TM1 and Hyperion Essbase.
- Data warehouses are sometimes called enterprise data warehouses because they help companies make decisions at all levels of the organization.
Where is it used?
You know that a data warehouse is a core component of Business intelligence. It is also called an Enterprise data warehouse (EDW).
It is used for reporting and analysis. It stores old data and also uses real-time data to generate business reports.
Below are the familiar sectors where the data warehouse is used.
- Public region: In this area, the data warehouse collects intelligence in government offices. It is also used to monitor and analyze each individual’s health records and tax records in government offices.
- Bank sector: It helps the banking sector control and investigates available resources on desks.
- Hospitality Industries like hotels and restaurants: In this sector, data warehouse helps promote themselves and attract target customers.
- Health care: In this area, the warehouse helps to generate patient treatment reports.
- Airlines: Here, the warehouse is used for analyzing the works assigned to the airline crew.
- Insurance: In this sector, the warehouse helps trace market fluctuations.
How can a data warehouse benefit an organization?
A specific business purpose can be analyzed with the data collected here.
Suppose the business wants to understand the machine downtime and how it can reduce. In that case, data can be collected from the data warehouse to understand the various times or situations during which the machines stopped working, the reasons behind the same, and how this can be reduced.
Data from different sources are integrated to provide cooperative data. For instance, if a company wants to do budgeting for the next quarter, a data warehouse will have all the information required.
The entire data set is available in one source, from incurred to depreciation costs.
The company utilizes the historical data stored in the system to extract relevant reports and understand the overall organization’s health.
But data such as the employee database, which includes addresses and phone numbers, must not be included as they are subject to change.
Once data is entered, it remains the same. Therefore, the firm must ensure that information is highly protected and that there is no alteration.
If any modifications are made, it will affect the reports and analysis.
5. Improved data quality
Helps to improve data quality by providing consistent, accurate data and fixing insufficient data.
Disadvantages of data warehouse
Cost v/s Benefit
A data warehouse is an IT project, and it consumes more person-hours and more money from the budget. Moreover, its implementation and maintenance are costly.
Hence the cost to benefit ratio is meager. However, if the organization is small or medium, it may affect its revenue.
We know that data warehouses are software applications for service. Its primary concern of it is the security of data.
You have to be more sure that the people who handle and analyze the customer data are the employees that your company trusts.
Because leaking the customer’s data within the organization may cause problems for executives and affect the relationship between the company and the customer.
The data imported into the data warehouse is often static data set that have less flexibility. They have less ability to generate a particular solution.
Warehouses are subjected to ad hoc queries that are highly difficult due to their most minor processing and query speed.
Miscalculation of ETL processing time
The entire data warehouse development process is the extraction, cleaning, and loading of consolidated data into the warehouse takes more time.
But usually, organizations do not guess the time required for the ETL process. As a result, it leads to a backlog of work.
Levels of data warehouse architecture
It comprises several levels. A few of them are as mentioned below:
- Data Source Layer
- Data Extraction Layer
- Staging Area
- ETL Layer
- Data Storage Layer
- Data Logic Layer
- Data Presentation Layer
- Metadata Layer
- System Operations Layer
Types of data warehouse architecture
There are three types of architecture in it.
Single tier architecture: It is rarely used architecture. It reduces the amount of data stored by avoiding repetition.
In this type of architecture, only the source layer is available. Thus, the single-tier consists of the source, data warehouse, and analysis layers.
The two-tier architecture consists of a data staging area or ETL (extraction, transformation, and loading) and the source layer.
This layer helps to merge diversified data into one standard schema. This type of architecture consists of the source layer, data staging layer, data warehouse layer, and analysis layer.
The three-tier architecture contains a reconciled layer and the data staging and source layer.
The source layer contains multiple sources in this architecture, and the data warehouse layer has data warehouses and data marts.
The role of a reconciled layer is to generate a standard data model for the entire enterprise. This reconciled layer can also use to do some operational works like reporting.
This architecture consists of the source, data staging, reconciled, data warehouse, and analysis layers.
Types of data warehouse
The following three are the main types of data warehouses.
1. Enterprise Data Warehouse (EDW): It helps to provide decision support service throughout the enterprise and also helps to classify data according to the subject.
2. Operational Data Store: It helps to store records of employees.
3. Data Mart: It helps to collect data directly from sources.
Data warehouse tools
Following are the few popular tools for data warehouse
- Amazon Redshift
- Microsoft Azure
- CData Sync
- SAP HANA
- Amazon RDS
- Amazon S3
- Maria DB
Difference between database(DB) and data warehouse(DW)
Many people get confused between these two concepts. So here are the main differences
- DW transfers and stores accumulated data for analytical purposes. At the same time, DB collects data for multiple transactions.
- DW was developed for the accumulation and recapture of large data sets. But DB was developed for write or read access.
- DW made for a more straightforward analysis of data collected and stored from multiple databases. DB made for quick record and recapture of data.
Data warehouse history
In the 1950s American government and businesses started using punch cards to store computer-generated data. They were being used till the 1980s.
In the 1960s, slowly disk storage systems came into the picture, and in 1964 the systems became popular, called ‘magnetic storage’ for data.
IBM was the first company that designed and started using the floppy disk drive. Later is called the hard disk drive.
In 1966, IBM designed its DBMS(database management system) called ‘information management system’. It contained the following features.
- Ability to find out the exact location of data
- Ability to solve the problem of locating more than one unit of data in the same place
- Ability to delete data
- Ability to access the data rapidly
- Ability to allocate the place when data stored cannot fit in the specified location.
In 1970, online applications came into the picture. People know that data can be directly accessible and shared between computers.
After that, people started using their personal computers. It changed the way of doing work. At the same time, 4GL technology was invented.
The combination of personal computers and 4GL technology gave complete freedom to the end-user. It allows end-users to access their data efficiently and rapidly by controlling the computer system. But they found the following problems.
- They got misled by incorrect data.
- Old data is not at all useful
- Confusion because of duplicated data
As a solution to these problems, the rational database was used in the 1980s. It used SQL (structured query language)as its language.
Businesses started assigning personal computers to the employees and widely used office applications( ms word, ms excel, ms office).
In the year 1990, significant changes took place. That is the usage of the internet. Internet became very popular, and conflict started because of globalization, computerization, and networking.
In 2000, businesses needed good integration between systems and consistent data to get the accurate business information required for proper decision-making.
Because of expanded databases and application systems, getting consistent data became difficult. Hence a data warehouse is developed by businesses.
The data warehouse is a term that has been around for more than two decades. It is one of the most essential and powerful tools in modern business intelligence, but many people don’t know what it is or how to use it. Please read this blog post to learn about data warehousing and its benefits!