Data Warehouse is very popular and widely used. It is used by many organizations for handling or storing data for making important decisions for their business. In this article, we'll get to know what are data warehouses, how a data warehouse works, data warehouse architecture, the types of data warehouses, the advantages of the data warehouse, the disadvantages of the data warehouse, and the top data warehouse tools.
What is a Data Warehouse
The term Data Warehouse was coined in the year 1990 by Bill Inmon. Data Warehouse is a central repository used to store information collected from various sources, and the collected information is stored under a unified schema. The stored information provides business insights to organizations to have a competitive edge over their competitors in the market. The Construction of a data warehouse involves data cleaning, integration, and transformation. Data Warehouse provides powerful tools that are used for organizing, understanding, and utilizing the data to make strategic decisions. We perform analysis and business intelligence activities (like data mining, data visualization, etc) in a data warehouse. A data warehouse works on historical data rather than present data for analytical purposes. Data Warehouses are mainly used for online analytical processing (OLAP). A data warehouse is a powerful tool that can store data in multiple dimensions. Now we'll look into the types of data warehouses.
Data Warehouse Architecture
A typical data warehouse generally consists of 3 tiers bottom tier (warehouse database server), middle tier (OLAP servers), and top tier (front-end client layer). In the bottom tier, back-end utilities and tools are used to provide data to this tier from various operational databases and other sources and perform ETL (extract, transform, and load) operations. The data is extracted using API (Application Program Interface) known as gateways. In the middle tier, We make use of OLAP (online analytical processing) servers like ROLAP (Relational online analytical processing), and MOLAP (Multidimensional online analytical processing) for analysis to make business decisions. In the top tier, there are many tools present that are mainly used for analytics, reporting, predictions, etc.
How does a Data Warehouse work?
Data Warehouse is a central repository that collects data from multiple sources. The data received by the data warehouse can be structured, semi-structured, or unstructured and data can be obtained from customers, social media, and many more sources. The data is then transformed, and processed into a form that is convenient for users to access the data that is further used for decision-making. So, whenever an organization has to make a decision it performs an analysis of the data warehouse to check if the decision made leads to the gain or loss of the organization. So, one can say that the use of a data warehouse is very crucial for an organization to function.
Types of Data Warehouses
- Enterprise Data Warehouse
- It is a type of central repository that stores the historical business data of an enterprise. These types of warehouses are mainly used by organizations. These types of warehouses are very complex to build take stake years to develop them.
- There are further classified into 2 types:- 1) on-premise and 2) virtual data warehouse.
- Data Marts
- It is a subset of the data warehouse. It is mainly used for a specific group of users or departments in a business line.
- These are further classified into 2 types:- 1) independent data mart and 2) dependent data mart.
- Operational Datastores
- These are popularly known as ODS. They are complementary elements to enterprise data warehouses These are used for operational purposes like storing employee details or records and other routine organizational activities.
Advantages of Data Warehouse- Data Warehouses are very helpful for providing business trends and forecasting.
- A relational database stores data or records up to GBs, whereas a data warehouse store can store data up to TBs.
- Data Warehouses store historical data that can be used to make long-term decisions.
- Data Warehouses are mainly designed for Online Analytical Processing (OLAP).
- Data Warehouses can store multi-dimensional data, which is not possible with a traditional database model.
- Data Warehouses can store both structured and unstructured data, whereas a traditional database can store only structured data.
- Using a data warehouse we can access millions of data records at a time.
- Data Warehouses provide a competitive advantage over an organization's competitors
Disadvantages of Data Warehouse- Though using a data warehouse is not a time taking process, the data which is fed to a data warehouse should be manually prepared by users. This is a time taking process.
- The cost required to maintain a data warehouse is expensive, as a data warehouse needs to be updated frequently to prevent security breaches and have newer or advanced safety measures.
- A data warehouse use is limited, if the data fed to the data warehouse is confidential then it is shared with a particular group of people or a department, thus leading to the limited use of the data warehouse.
Top Data Warehouse tools- Snowflake
- It is a cloud computing-based data warehousing tool that runs on top of AWS or Microsoft Azure cloud infrastructure. It is appropriate for enterprise-related applications. Snowflake offers dynamic and scalable computing power where we only pay for what we use.
- Amazon Redshift
- It is a cloud-based data warehouse tool that is managed by Amazon. It is cheap and easy to use. It can handle large datasets and provide analysis or insights to the user.
- Google BigQuery
- It is a data warehousing tool that is managed by Google. It is a high-end data warehousing solution. It is a serverless data warehouse that allows scalable analysis over massive amounts of data. It also provides machine learning features. Mainly used for handling large amounts of data.
- PostgreSQL
- It is open-source software that can be easily downloaded. It is an extremely stable database management system. It is mainly used for analytical purposes. This type of data warehouse is compatible with all types of programming languages like python, java, etc.
- Amazon RDS
- It is a cloud data storage service provided by Amazon. It is used to operate and scale a relational database within the AWS cloud. It is cost-effective and provides resizable hardware capabilities. It is considered a Platform as a Service (PaaS) as it provides tools to manage database instances.
- IBM db2 Warehouse
- It is a data management tool provided by IBM. It is an elastic cloud data warehouse that provides scaling of data storage and computation. It is mainly used for storing, retrieving, and analyzing data with efficiency.
- Oracle Autonomous Warehouse
- It is a cloud-based data warehousing tool that is provided by Oracle. This type of warehouse automates the processes of scaling, securing, and other processes to improve the productivity of data analysts and data scientists.
- It is a type of central repository that stores the historical business data of an enterprise. These types of warehouses are mainly used by organizations. These types of warehouses are very complex to build take stake years to develop them.
- There are further classified into 2 types:- 1) on-premise and 2) virtual data warehouse.
- It is a subset of the data warehouse. It is mainly used for a specific group of users or departments in a business line.
- These are further classified into 2 types:- 1) independent data mart and 2) dependent data mart.
- These are popularly known as ODS. They are complementary elements to enterprise data warehouses These are used for operational purposes like storing employee details or records and other routine organizational activities.
- Data Warehouses are very helpful for providing business trends and forecasting.
- A relational database stores data or records up to GBs, whereas a data warehouse store can store data up to TBs.
- Data Warehouses store historical data that can be used to make long-term decisions.
- Data Warehouses are mainly designed for Online Analytical Processing (OLAP).
- Data Warehouses can store multi-dimensional data, which is not possible with a traditional database model.
- Data Warehouses can store both structured and unstructured data, whereas a traditional database can store only structured data.
- Using a data warehouse we can access millions of data records at a time.
- Data Warehouses provide a competitive advantage over an organization's competitors
- Though using a data warehouse is not a time taking process, the data which is fed to a data warehouse should be manually prepared by users. This is a time taking process.
- The cost required to maintain a data warehouse is expensive, as a data warehouse needs to be updated frequently to prevent security breaches and have newer or advanced safety measures.
- A data warehouse use is limited, if the data fed to the data warehouse is confidential then it is shared with a particular group of people or a department, thus leading to the limited use of the data warehouse.
- Snowflake
- It is a cloud computing-based data warehousing tool that runs on top of AWS or Microsoft Azure cloud infrastructure. It is appropriate for enterprise-related applications. Snowflake offers dynamic and scalable computing power where we only pay for what we use.
- Amazon Redshift
- It is a cloud-based data warehouse tool that is managed by Amazon. It is cheap and easy to use. It can handle large datasets and provide analysis or insights to the user.
- Google BigQuery
- It is a data warehousing tool that is managed by Google. It is a high-end data warehousing solution. It is a serverless data warehouse that allows scalable analysis over massive amounts of data. It also provides machine learning features. Mainly used for handling large amounts of data.
- PostgreSQL
- It is open-source software that can be easily downloaded. It is an extremely stable database management system. It is mainly used for analytical purposes. This type of data warehouse is compatible with all types of programming languages like python, java, etc.
- Amazon RDS
- It is a cloud data storage service provided by Amazon. It is used to operate and scale a relational database within the AWS cloud. It is cost-effective and provides resizable hardware capabilities. It is considered a Platform as a Service (PaaS) as it provides tools to manage database instances.
- IBM db2 Warehouse
- It is a data management tool provided by IBM. It is an elastic cloud data warehouse that provides scaling of data storage and computation. It is mainly used for storing, retrieving, and analyzing data with efficiency.
- Oracle Autonomous Warehouse
- It is a cloud-based data warehousing tool that is provided by Oracle. This type of warehouse automates the processes of scaling, securing, and other processes to improve the productivity of data analysts and data scientists.