Data Warehouse Development Life cycle Model
warehouse development model

What is data warehouse?

A data warehouse is a data management system designed to support business intelligence activities, particularly analytics. Data warehousing is the process of building, managing, and securing a company’s or organization’s electronic data storage in a digital warehouse. For example, Data warehousing enables data mining. Which can help firms find data trends and patterns that could result in higher sales and profitability.

It  typically consists of four components.

  • Data Sources
  • Processing and data Staging
  • Data warehouse
  • Data Mart


components of data warehouse

Development lifecycle

Data Warehousing is a flow process that gathers and manages structured and unstructured data from numerous sources into a Centralized repository. It makes actionable business decisions. With all of your data in one location, you can perform analysis, reporting, and discover important insights at a variety of combination levels. The phases (and their relationships) that a data warehouse system goes through from the time it is conceptualized until it is no longer usable are referred to as the data warehouse life-cycle.

Requirement Gathering

  • Firstly Business analysts, the local technical lead, and the customer complete it.
  • A business analyst creates a business requirement specification (BRS) document during this stage.
  • Finally after gathering the requirements, the data modeler begins to identify the dimensions, facts, and combinations depending on the needs.
  • We can describe this as the data warehouse’s general design.

Data Modeling

  • This is the second step.
  • This is the process of designing databases and visualizing data distribution.
  • Star Schema, Snowflake Schema and Galaxy Schema are the three data models of data warehouse.
  • It is the reasoning behind the storage of data in relation to other data.

ELT Design and development

  • Thirdly a data lake can include the data that an ETL (Extract, transfer, load) tool has extracted from different source systems.
  • ETL process extract the data from the lake. After that it transforms and load it into a data warehouse for reporting.
  • A solid ETL procedure can be useful in creating a straightforward yet useful data warehouse that is beneficial at all organizational levels.
  • ELT application design to fulfill  specifications documents created during the analytical phase

OLAP Cubes

  • This is the Fourth step.
  • Also called hypercube or multidimensional cube.
  • It is a data format that enables quick data analysis in accordance with the several aspects that characterize a business problem.
  • From various data sources and file types, such as text files, excel sheets, multimedia files, etc., a data warehouse would extract information.
  • The retrieved data is modified and cleaned before being placed into an OLAP server (or OLAP cube) for preliminary processing in preparation for additional analysis.

UI Development

  • This is the Fifth step.
  • A user interface is required for the communication between user and a computer system.
  • A user interface’s primary goal is to give users the ability to efficiently control the equipment or device they’re using.
  • There are several tools available on the market to aid in UI development. For those using BigQuery, excellent options include BI tools like Tableau or PowerBI.


  • This is the Sixth step.
  • In this step we can update or change the schema and the application domain or requirement of it.
  • Systems for maintaining data warehouses must include tools for tracking changes to the schema, such as updates.

Test and Deployment

  • This is the ultimate step.
  • Finally, Business and organization evaluate data warehouses to determine implementation of necessary business problems.
  • The warehouse testing requires examining vast amounts of data.
  • Similarly, different types of data sources, including relational databases, flat files, operational data, etc., provide the data that must be compared.
  • At the time of implementation the majority of its capabilities are implemented. Additionally both on-premises and cloud deployment options are available for the data warehouses.

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Data Warehouse Development Life cycle Model

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