Data warehouse development life cycle model

A data warehouse is a data management system that was developed mainly to support business intelligence activities, especially analytics. The data warehouses are exclusively designed to perform operations and analysis driven by queries and often contain a large amount of historical data.

What is data warehousing?

Data warehousing is the process of developing, managing, and securing the electronic storage of data by a business or organization in a digital warehouse. The main goal of data warehousing is to create a treasure of historical data that can be used at any time for analysis and to discover useful insights to make a better decision for the organization.

It is also known as enterprise data warehousing. Data warehousing is an electronic method of organizing, managing data and information. For example, data warehousing makes data mining possible, which can assist businesses in looking for data trends and patterns that can lead to greater sales and profits.

• In data warehouses, organizations can store information for and for a long period, which means historical data. The warehouse becomes a library of historical information that can be retrieved and analyzed for better decision-making in business.
• Periodically, new data is updated by the organization in various essential departments, such as marketing and sales.
• The principal factors in building a successful data warehouse include the definition of data that is most significant to the organization and identifying the sources of the information.
• A database is created to store real-time i.e. present data. A data warehouse is designed as a substitution for registers used traditionally to store historical information.

Typically, a data warehouse consists of four components:  

• Data sources
• Data Staging and Processing ETL (Extract, Transform, and Load)
• Data Warehouse
• Data Marts

Data Warehouse architecture

All of these components are put together to increase speed and efficiency. We can analyze data and get results quickly. To get more knowledge about it, refer to this article Data Warehouse Architecture.

Data warehouse development life cycle

Data Warehousing is a flow process used to gather and handle structured and unstructured data from multiple sources into a centralized repository to operate actionable business decisions. With all of your data in one place, it becomes easier to perform analysis, reporting and discover meaningful insights at completely different combination levels. A data warehouse setting includes extraction, transformation, and loading (ELT) resolution, an online analytical processing (OLAP) engine, consumer analysis tools, and different applications that manage the method of gathering data and delivering it to business. The term data warehouse life-cycle is used to indicate the steps a data warehouse system goes through between when it is built. The following is the Life-cycle of Data Warehousing:

Data Warehouse Life Cycle

• Requirement Specification: It is the first step in the development of the Data Warehouse and is done by business analysts. In this step, Business Analysts prepare business requirement specification documents. More than 50% of requirements are collected from the client side and it takes 3-4 months to collect all the requirements. After the requirements are gathered, the data modeler starts recognizing the dimensions, facts & combinations based on the requirements. We can say that this is an overall blueprint of the data warehouse. But, this phase is more about determining business needs and placing them in the data warehouse.
• Data Modelling: This is the second step in the development of the Data Warehouse. Data Modelling is the process of visualizing data distribution and designing databases by fulfilling the requirements to transform the data into a format that can be stored in the data warehouse. For example, whenever we start building a house, we put all the things in the correct position as specified in the blueprint. That’s what data modeling is for data warehouses. Data modelling helps to organize data, creates connections between data sets, and it’s useful for establishing data compliance and its security that line up with data warehousing goals. It is the most complex phase of data warehouse development. And, there are many data modelling techniques that businesses use for warehouse design. Data modelling typically takes place at the data mart level and branches out in a data warehouse. It’s the logic of how the data is stored concerning other data. There are three data models for data warehouses:
  • Star Schema
  • Snowflake Schema
  • Galaxy Schema.
• ELT Design and Development: This is the third step in the development of the Data Warehouse. ETL or Extract, Transfer, Load tool may extract data from various source systems and store it in a data lake. An ETL process can extract the data from the lake, after that transform it and load it into a data warehouse for reporting. For optimal speeds, good visualization, and the ability to build easy, replicable, and consistent data pipelines between all of the existing architecture and the new data warehouse, we need ELT tools. This is where ETL tools like SAS Data Management, IBM Information Server, Hive, etc. come into the picture. A good ETL process can be helpful in constructing a simple yet functional data warehouse that’s valuable throughout every layer of the organization. 
• OLAP Cubes: This is the fourth step in the development of the Data Warehouse. An OLAP cube, also known as a multidimensional cube or hypercube, is a data structure that allows fast analysis of data according to the multiple dimensions that define a business problem. A data warehouse would extract information from multiple data sources and formats like text files, excel sheets, multimedia files, etc. The extracted data is cleaned and transformed and is loaded into an OLAP server (or OLAP cube) where information is pre-processed in advance for further analysis. Usually, data operations and analysis are performed using a simple spreadsheet, where data values are arranged in row and column format. This is ideal for two-dimensional data. However, OLAP contains multidimensional data, with data typically obtained from different and unrelated sources. Employing a spreadsheet isn’t an optimum choice. The cube will store and analyze multidimensional data in a logical and orderly manner. Now, data warehouses are now offered as a fully built product that is configurable and capable of staging multiple types of data. OLAP cubes are becoming outdated as OLAP cubes can’t deliver real-time analysis and reporting, as businesses are now expecting something with high performance.
• UI Development: This is the fifth step in the development of the Data Warehouse. So far, the processes discussed have taken place at the backend. There is a need for a user interface for how the user and a computer system interact, in particular the use of input devices and software, to immediately access the data warehouse for analysis and generating reports. The main aim of a UI is to enable a user to effectively manage a device or machine they’re interacting with. There are plenty of tools in the market that helps with UI development. BI tools like Tableau or PowerBI for those using BigQuery are great choices.
• Maintenance: This is the sixth step in the development of the Data Warehouse. In this phase, we can update or make changes to the schema and data warehouse’s application domain or requirements. Data warehouse maintenance systems must provide means to keep track of schema modifications as well, for instance, modifications. At the schema level, we can perform operations for the Insertion, and change dimensions and categories. Changes are, for example, adding or deleting user-defined attributes.  
• Test and Deployment: This is often the ultimate step in the Data Warehouse development cycle. Businesses and organizations test data warehouses to ensure whether the required business problems are implemented successfully or not. The warehouse testing involves the scrutiny of enormous volumes of data. Data that has to be compared comes from heterogeneous data sources like relational databases, flat files, operational data, etc. The overall data warehouse project testing phases include: Data completeness, Data Transformation, Data is loaded by means of ETL tools, Data integrity, etc. After testing the data warehouse, we deployed it so that users could immediately access the data and perform analysis. Basically, in this phase, the data warehouse is turned on and lets the user take the benefit of it. At the time of data warehouse deployment, most of its functions are implemented. The data warehouses can be deployed at their own data center or on the cloud.

How does a data warehouse work?

A data warehouse contains data from heterogeneous sources. It is a central repository where data is processed, transformed, and loaded into the databases so that users can easily access the data from Data Warehouse for analysis using Business Intelligence  and analytical tools for better decision making. 

All this data may be in one of the following formats:

• Structured
• Unstructured
• Semi-structured

Data Warehouse

The data warehouse works by collecting, managing, and organizing data from different sources into a complete broad database. Once the data is collected, it is processed and stored into various tables depending on the type of data and format.The confidential business details can also be stored within the data warehouse, like employee details, salary information, etc.

Information derived from a data warehouse helps companies to research their customers and anticipate the approaching trends within the competitive market. By having precise information about what their customers want and what they’re expecting from a business, it becomes easier to serve them right and achieve higher customer satisfaction levels.

Advantages:

Structured approach: The DWLC provides a structured approach to building a data warehouse, ensuring that each step is well-defined and understood by the project team.

Helps identify requirements: The DWLC helps to identify and prioritize the business requirements for the data warehouse, ensuring that the data warehouse meets the needs of the organization.

Reduces risk: The DWLC helps to reduce the risk of failure by identifying potential issues early in the development cycle and addressing them before they become major problems.

Improves quality: The DWLC promotes a focus on quality throughout the development process, which helps to ensure that the data warehouse is accurate, reliable, and meets the needs of the business.

Disadvantages:

Lengthy process: The DWLC is a lengthy process that can take several months or even years to complete, depending on the size and complexity of the data warehouse.

Expensive: The DWLC can be expensive, as it requires a dedicated team of skilled professionals to design, build, and deploy the data warehouse.

Requires a high level of expertise: The DWLC requires a high level of expertise in data warehousing and related technologies, which can be a challenge for organizations that do not have the necessary skills in-house.

Limited flexibility: The DWLC can be inflexible, as it is designed to follow a specific process, which may not allow for changes to be made easily or quickly.