Let’s assume you are a data analyst working for a company that has the following three sectors: Marketing, Sales and Finance. Now, let’s assume that each department maintains a separate database.

This could lead to a situation wherein each department has its own version of the facts. For a question such as ‘What is the total revenue of the last month?’, every department might have a different answer. This is because each department draws information from a different database.

This is where a data warehouse can prove to be useful. It can help with creating a single version of the truth and the facts. A data warehouse would thus be the central repository of data of the entire enterprise.

What is Data Warehouse?

A data warehouse is a system used for reporting and data analysis, and is considered a core component of business intelligence. It is a large, centralized repository of data from one or more sources, that is used to support the reporting and analysis of business data.

Data warehouses are designed to support the efficient querying and analysis of data, and typically include a range of tools and technologies for data integration, data management, and reporting. They are commonly used to support decision making in organizations by providing a single source of truth for data.

A data warehouse is like a big library, but instead of books, it stores information about things like sales, customer information, and inventory. Just like in a library, where you can go to find a book you need, in a data warehouse you can go to find information you need to make important decisions.

Like how much money a store made last month or how many blue teddy bears were sold last week. It’s like a big brain for a company to make sure they know what’s happening and make smart choices.

Properties & Characteristics of Data Warehouse

Data warehouses have several key properties that define their characteristics and capabilities:

Subject-oriented: Data in a data warehouse is organized around specific subjects, such as sales or inventory, rather than specific applications or transactions.

Integrated: Data in a data warehouse is integrated from a variety of different sources, such as transactional systems, external data, and legacy systems.

Time-variant: Data in a data warehouse is stored with a timestamp, allowing for the analysis of historical data over time.

Non-volatile: Once data is loaded into a data warehouse, it is not updated or deleted, allowing for accurate reporting and analysis of historical data.

Read-optimized: Data in a data warehouse is optimized for read-heavy workloads, such as reporting and analysis, rather than write-heavy workloads, such as transactional processing.

Schema-on-write: Data in a data warehouse is transformed and structured before it is loaded.

Scalable: Data warehouses are designed to handle large amounts of data and to support a large number of concurrent users.

Multi-dimensional: Data in a data warehouse is organized in a multi-dimensional structure, such as a star or snowflake schema, to support efficient querying and analysis.

Structure of Data Warehouse

Primary methods of designing a data warehouse is dimensional modelling. The two key elements of dimensional modelling include facts and dimensions, which are basically the different types of variables that are used in data warehouse. When this two elements are arranged in a particular manner it is called as “schema design”.

In a data warehouse, “facts” and “dimensions” are two types of data that are used to organize and analyze information.

Facts: They are the numerical data that are being analyzed, such as sales figures, revenue, or inventory levels. They are often stored in tables called “fact tables”.

Dimensions: They are the context in which the facts are being analyzed, such as time, location, or product category. They are also called as “metadata”. They are often stored in tables called “dimension tables”.

For example, a fact table might contain information about sales figures, while a dimension table might contain information about the products that were sold, the time the sales occurred, or the location of the store where the sales took place. Together, the facts and dimensions allow you to slice and dice the data in various ways, to answer different questions and gain insights from the data.

Components of Data Warehouse

The structure of a data warehouse typically includes several components:

Data sources: These are the various systems and databases that provide the data that will be loaded into the data warehouse. Data sources can include transactional systems, external data feeds, and legacy systems.

Staging area: This is an intermediate location where the data is temporarily stored after it is extracted from the data sources, but before it is transformed and loaded into the data warehouse. The staging area is used to perform initial data validation and cleaning, and to resolve any data quality issues.

Data integration: This is the process of integrating data from different sources, and resolving any inconsistencies or conflicts. It also includes data scrubbing, data validation and data cleansing.

Data transformation: This process involves transforming the data into a format that is consistent and can be loaded into the data warehouse. This includes things like data type conversion, data mapping, and data aggregation.

Data loading: This process involves loading the data into the data warehouse, and may also include indexing the data to make it more easily searchable and queryable.

Data mart: A Data mart is a subset of a Data warehouse, it is a small and focused data warehouse that is built to serve a specific business function or department.

Data warehouse schema: This is the structure of the data warehouse, which defines how the data is organized and how it can be queried and analyzed. Common data warehouse schemas include star and snowflake schemas.

Metadata: This is data about the data, such as definitions, descriptions, and relationships. Metadata is used to understand the data and to ensure that it is accurate and consistent.

Business Intelligence (BI) and Analytics Tools: These are the tools used to query, analyze and report on the data in the data warehouse. They can range from simple reporting tools to more advanced analytics and visualization platforms.

Key Points on Data Warehouse

• A data warehouse is a large, centralized repository of data that is specifically designed for reporting and analysis.
• Data is extracted from various sources, transformed to fit the data warehouse schema and then loaded into the data warehouse.
• Data warehousing uses a process called ETL (Extract, Transform, Load) to move data from various sources into a central repository.
• Data is transformed into a format that is easy to query and analyze before it is loaded into the data warehouse
• Data warehouse typically includes several components such as Data sources, Staging area, Data integration, Data transformation, Data loading, Data mart, Data warehouse schema, Metadata and Business Intelligence (BI) and Analytics Tools.
• Data warehouse enables organizations to store and analyze large amounts of data in a way that is efficient, accurate, and easily queryable.

Conclusion

In the next blog we will discuss about Extract, Transfrom & Load (ETL) and see how it is related to Data Warehouse. I hope you are excited about the same.

Stay Tuned !! Happy Learning !!