What is a Data Warehouse: Overview, Concepts, and how it Works?

Data Warehouse

In today’s rapidly changing corporate environment, organizations are turning to cloud-based technologies for convenient data collection, reporting, and analysis. This is where Data Warehousing comes in as a core component of business intelligence that enables businesses to enhance their performance. It is important to understand what is a data warehouse and why it is evolving in the global marketplace.

What is a Data Warehouse?

A data warehouse is a large, centralized repository of data that is specifically designed to support business intelligence (BI) activities such as reporting, analysis, and data mining. Data warehouses typically store historical data from multiple sources, such as transactional systems and external data sources, and are optimized for read-intensive access patterns. They are often used to support decision-making processes by providing a single source of truth for an organization’s data.

Key Characteristics of Data Warehouse:

1. Subject-oriented: Data is organized around specific subjects, such as customers, sales, or products.
2. Integrated: Data is consolidated from multiple sources into a single, consistent format.
3. Non-volatile: Data in a data warehouse is not updated or deleted, only new data is added.
4. Time-variant: Data is stored with a time dimension, allowing for analysis of historical trends.
5. Read-optimized: Data is optimized for read-intensive access patterns, rather than write-intensive access patterns.
6. Scalable: Data warehouses are designed to handle large amounts of data and can be easily scaled to accommodate growing data volumes.
7. Data Governance: Data in the data warehouse is governed by rules, policies, and procedures to ensure data quality, security, and compliance.
8. Business Intelligence: The data warehouse serves as a single source of truth for reporting, analysis, and data mining activities to support business decision-making.
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Database Vs. Data Warehouse

A database and a data warehouse are both types of data storage systems, but they have some key differences:

1. Purpose: A database is primarily used for transactional processing, while a data warehouse is used for analytical and reporting purposes.
2. Data Structure: A database is typically structured in a normalized format, while a data warehouse is typically structured in a denormalized format to support reporting and analysis.
3. Data Integrity: A database enforces data integrity constraints, such as primary keys and foreign keys, to maintain the consistency of data, while a data warehouse does not typically enforce these constraints.
4. Data Freshness: A database stores current data, while a data warehouse stores historical data and is typically updated on a regular basis with new data.
5. Performance: A database is optimized for write-intensive access patterns, while a data warehouse is optimized for read-intensive access patterns.
6. Data Governance: A Database is used by applications and developers to perform CRUD operations. A data warehouse is governed by rules, policies, and procedures to ensure data quality, security, and compliance.

In summary, a database is used for transactional processing, while a data warehouse is used for analytical and reporting purposes.

Data Warehouse Architecture:

Data warehouse architecture refers to the way the different components of a data warehouse system are arranged and how they interact with one another.

There are several different architectural models, including:

1. Single-tier architecture: In this model, all components are on a single server. The data source, ETL process, and data warehouse are all on the same physical machine. This model is the simplest, but it is also the least scalable and may not be able to handle large amounts of data.

1. Two-tier architecture: In this model, the data source, ETL process, and data warehouse are on separate servers. The ETL process is responsible for extracting data from the data source and loading it into the data warehouse. This model is more scalable than single-tier architecture but still may not be suitable for large data sets.

1. Three-tier architecture: This is the most common model for data warehouse architecture. It includes an additional tier, the client-server, between the data source and data warehouse. This tier acts as an intermediary between the data source and the data warehouse, and it is responsible for performing the ETL process. This model is highly scalable and can handle large amounts of data.

In summary, Single-tier architecture is the simplest but less scalable, Two-tier architecture is more scalable but still may not be suitable for large data sets, and Three-tier architecture is the most common and highly scalable model that can handle large amounts of data.

How Data Warehouse Works?

A data warehouse works by consolidating data from multiple sources into a single, centralized repository. The data is then organized and optimized for read-intensive access patterns, making it easy to perform reporting, analysis, and data mining activities. The process of creating and maintaining a data warehouse typically involves the following steps:

1. Data extraction: Data is extracted from various sources, such as transactional systems, external data sources, and other databases.
2. Data transformation: The extracted data is then transformed into a format that is compatible with the data warehouse. This may include cleaning and normalizing the data, as well as resolving any inconsistencies.
3. Data loading: The transformed data is then loaded into the data warehouse. This may involve loading the data into a staging area first, and then into the actual data warehouse.
4. Data indexing: The data in the data warehouse is indexed to make it easy to find and retrieve specific data.
5. Data quality assurance: Data Governance process is applied to ensure data quality, security, and compliance, typically through the use of rules, policies, and procedures.
6. Data access: Once the data is loaded into the data warehouse, it can be accessed and analyzed using business intelligence tools, such as OLAP (Online Analytical Processing) systems, data mining tools, and reporting tools.

In summary, A data warehouse works by consolidating data from multiple sources, cleaning, normalizing, and resolving inconsistencies, loading, indexing, and ensuring data quality before it can be accessed and analyzed using business intelligence tools.

Benefits of Data Warehouse:

Data warehouses provide several benefits to organizations, including:

1. Improved decision-making: A data warehouse provides a single source of truth for an organization’s data, making it easier to access the information needed to make informed decisions.
2. Increased efficiency: By consolidating data from multiple sources into a single, centralized repository, data warehouses can help reduce the time and resources needed to access and analyze data.
3. Better data quality: Data Governance process is applied to ensure data quality, security, and compliance, which helps to reduce errors and inconsistencies in the data.
4. Scalability: Data warehouses are designed to handle large amounts of data and can be easily scaled to accommodate growing data volumes.
5. Flexibility: Data warehouse allows for customization of data marts for different departments and business units with the ability to add new data sources as the business evolves.
6. Historical data analysis: Data is stored with a time dimension, allowing for analysis of historical trends and patterns, which can be used to make better predictions and forecasting.
7. Data security: Data warehouse provides a secure environment to store and access the data, with the ability to apply various security measures such as user access controls, data encryption, and firewalls.

In summary, a Data warehouse provides benefits such as improved decision-making, increased efficiency, better data quality, scalability, flexibility, historical data analysis, and data security.

Types of Data Warehouse:

There are several different tools that are commonly used in data warehousing, including:

1. Enterprise Data Warehouse (EDW): An enterprise data warehouse (EDW) is a large, centralized repository of data that is designed to support business intelligence activities for an entire organization. An EDW consolidates data from multiple sources, such as transactional systems and external data sources, and is optimized for read-intensive access patterns. It serves as a single source of truth for an organization’s data, providing a holistic view of the business.

1. Operational Data Store (ODS): An operational data store (ODS) is a type of data warehouse that stores current data and is used to support operational processes, such as order processing and inventory management. An ODS is typically updated in real-time or near real-time and is used to provide immediate access to current data for operational activities. It is designed to be a source of data for operational systems and is optimized for write-intensive access patterns.

1. Data Mart: A data mart is a subset of a data warehouse that is focused on a specific subject area, such as sales or marketing. It is typically smaller in scope and serves a specific department or business unit within an organization. Data marts are designed to provide a more specific and detailed view of the data and are optimized for the specific needs of a particular department or business unit. They can be created and maintained independently, but they are usually connected to the EDW.

Enterprise Data Warehouse (EDW) is a large, centralized repository of data that is designed to support business intelligence activities for an entire organization, Operational Data Store (ODS) is a type of data warehouse that stores current data and is used to support operational processes, and Data Mart is a subset of a data warehouse that is focused on a specific subject area, such as sales or marketing.

There are several other different tools that are commonly used in data warehousing, including:

1. Extract, Transform, and Load (ETL) tools: These tools are used to extract data from various sources, transform it into a format that is compatible with the data warehouse, and load it into the data warehouse. Examples include Informatica PowerCenter, IBM DataStage, and Talend.
2. Data integration tools: These tools are used to integrate data from multiple sources and ensure that it is consistent and accurate. Examples include Informatica Data Quality and IBM InfoSphere DataStage.
3. Data modeling tools: These tools are used to design the structure and schema of the data warehouse. Examples include ER/Studio and CA ERwin Data Modeler.
4. Data governance tools: These tools are used to ensure data quality, security, and compliance, typically through the use of rules, policies, and procedures. Examples include Informatica Data Governance, SAP Data Governance, and Collibra Data Governance.
5. Business Intelligence (BI) tools: These tools are used to analyze and gain insights from the data in the data warehouse. Examples include Tableau, QlikView, and Microsoft Power BI.
6. OLAP (Online Analytical Processing) systems: These systems are optimized for complex analytical queries, and are used to perform multidimensional analysis of data in the data warehouse. Examples include IBM Cognos and Microsoft SQL Server Analysis Services.
7. Data visualization tools: These tools are used to create an interactive and visual representation of data, making it easier to understand and communicate insights. Examples include Tableau, QlikView, and Microsoft Power BI.

In summary, Data Warehousing tools include ETL tools, Data integration tools, Data modeling tools, Data governance tools, Business Intelligence tools, OLAP systems, and Data visualization tools. These tools are used to extract, integrate, model, govern, analyze and visualize the data in a data warehouse.

Data Mining:

In this process, data is extracted and analyzed to fetch useful information. In data mining, hidden patterns are researched from the dataset to predict future behavior. Data mining is used to indicate and discover relationships through the data. Data mining uses statistics, artificial intelligence, machine learning systems, and some databases to find hidden patterns in the data. It supports business-related queries that are time-consuming to resolve.

Data mining is the process of discovering patterns, relationships, and insights in large sets of data. It involves the use of statistical, machine learning, and other techniques to extract useful information from data. Data mining can be used to identify patterns and trends in data, classify and predict future events, and discover relationships among variables.

Data mining can be applied to a wide range of data types, including structured data, semi-structured data, and unstructured data. It is used in a variety of industries, such as finance, healthcare, marketing, and retail, to gain insights and make better decisions.

There are several common data mining techniques, such as:

1. Clustering: This technique is used to group similar data points together based on their attributes.
2. Association rule mining: This technique is used to discover relationships among variables, such as which products are often purchased together.
3. Classification: This technique is used to predict the class of a data point based on its attributes.
4. Regression: This technique is used to predict a numerical value based on a set of input variables.
5. Anomaly detection: This technique is used to identify unusual or abnormal data points, which may indicate outliers or errors.
6. Sequential pattern mining: This technique is used to discover patterns in data over time.

In summary, Data mining is the process of discovering patterns, relationships, and insights in large sets of data by applying statistical, machine learning, and other techniques. It is widely used in various industries to gain insights and make better decisions, and there are several common data mining techniques such as Clustering, Association rule mining, Classification, Regression, Anomaly detection, and Sequential pattern mining.

Data Warehouse Example

An example of a data warehouse would be a retail company that uses a data warehouse to consolidate data from multiple sources, such as point-of-sale systems, customer relationship management systems, and inventory management systems. The data warehouse is designed to support business intelligence activities, such as reporting on sales by product, customer demographics, and store performance.

The data is extracted from various sources, transformed into a format that is compatible with the data warehouse, and loaded into the data warehouse on a regular basis. The data is then indexed and organized around specific subjects, such as customers, products, and sales. This allows the retail company to easily access and analyze the data to gain insights into customer behavior, sales trends, and inventory levels.

The retail company could also use Data Marts to provide specific departments such as marketing or finance with a detailed view of their area of concern. The marketing department, for instance, could use a Data Mart to analyze customer demographics, purchase history, and website behavior to create targeted marketing campaigns.

In summary, An example of a data warehouse would be a retail company that uses a data warehouse to consolidate data from multiple sources, such as point-of-sale systems, customer relationship management systems, and inventory management systems, to support business intelligence activities, such as reporting on sales by product, customer demographics, and store performance. Additionally, Data Marts can be created and maintained independently but connected to the EDW.

Build Your Career in Data Warehousing 

If you are looking to work as a Business Intelligence (BI) professional or learn data warehousing, you have many exciting career options available. Data architects, database administrators, coders, and analysts are some of the most sought-after BI professionals. Prepare yourself for a job interview with our data warehouse interview questions.  

With data sources growing larger, businesses of the future need to devise better data insights and data analysis. Prepare for the future with Data Science and position yourself as an asset for top organizations.