Data Warehousing Tutorial Last Updated : 23 Jul, 2025 Comments Improve Suggest changes Like Article Like Report Data warehousing refers to the process of collecting, storing, and managing data from different sources in a centralized repository. It allows businesses to analyze historical data and make informed decisions. The data is structured in a way that makes it easy to query and generate reports.A data warehouse consolidates data from multiple sources.It helps businesses track historical trends and performance.Facilitates complex queries and analysis for decision-making.Enables efficient reporting and business intelligence.Data WarehousingIntroduction to Data WarehousingData WarehousingCharacteristics of Data WarehousingData Warehouse vs DBMSComparison of Operational and Informational SystemsData Warehouse ArchitectureData Warehouse ArchitectureThree - Tier ArchitectureData MartsData LakeDifference between Data Mart, Data Lake, and Data WarehouseOLAP TechnologyOLAP in Data WarehousingOLAP vs OLTPETL ProcessETL vs ELTOLAP OperationsTypes of OLAP SystemsMOLAP (Multidimensional OLAP)ROLAP (Relational OLAP)HOLAP (Hybrid OLAP)ROLAP vs MOLAPDifference between ROLAP, MOLAP and HOLAPOLAP ImplementationData Warehouse ModelingIntroduction to Data Warehouse ModelingMultidimensional Data ModelFact TableDimension TableFact Tables vs Dimension TablesData Warehouse Schema ModelsStar SchemaSnowflake SchemaStar Schema vs Snowflake SchemaConcept HierarchyData TransformationIntroduction to Data TransformationTypes of Data TransformationData NormalizationAggregationDiscretizationData SamplingHandling Missing ValuesHandling OutliersFeature SelectionFeature ExtractionDifference between Feature Selection and Feature ExtractionDimensionality ReductionMiscellaneous TopicsMeasures - Categorization and ComputationData Warehouse ImplementationPerformance Optimization in Data WarehousingData Warehouse Tools and Technologies Comment More infoAdvertise with us Next Article Data Warehousing R realravipal27 Follow Improve Article Tags : DBMS Data Warehouse GATE Computer Science and Engineering GATE DA +1 More Similar Reads Data Warehousing Tutorial Data warehousing refers to the process of collecting, storing, and managing data from different sources in a centralized repository. It allows businesses to analyze historical data and make informed decisions. The data is structured in a way that makes it easy to query and generate reports.A data wa 2 min read Basics of Data WarehousingData WarehousingA data warehouse is a centralized system used for storing and managing large volumes of data from various sources. It is designed to help businesses analyze historical data and make informed decisions. Data from different operational systems is collected, cleaned, and stored in a structured way, ena 7 min read History of Data WarehousingThe data warehouse is a core repository that performs aggregation to collect and group data from various sources into a central integrated unit. The data from the warehouse can be retrieved and analyzed to generate reports or relations between the datasets of the database which enhances the growth o 7 min read Data Warehouse ArchitectureA Data Warehouse is a system that combine data from multiple sources, organizes it under a single architecture, and helps organizations make better decisions. It simplifies data handling, storage, and reporting, making analysis more efficient. Data Warehouse Architecture uses a structured framework 10 min read Difference between Data Mart, Data Lake, and Data WarehouseA Data Mart, Data Lake, and Data Warehouse are all used for storing and analyzing data, but they serve different purposes. A Data Warehouse stores structured, processed data for reporting, a Data Lake holds raw, unstructured data for flexible analysis, and a Data Mart is a smaller, focused version o 5 min read Data Loading in Data warehouseThe data warehouse is structured by the integration of data from different sources. Several factors separate the data warehouse from the operational database. Since the two systems provide vastly different functionality and require different types of data, it is necessary to keep the data database s 5 min read OLAP TechnologyOLAP ServersOnline Analytical Processing(OLAP) refers to a set of software tools used for data analysis in order to make business decisions. OLAP provides a platform for gaining insights from databases retrieved from multiple database systems at the same time. It is based on a multidimensional data model, which 4 min read Difference Between OLAP and OLTP in DatabasesOLAP (Online Analytical Processing) and OLTP (Online Transaction Processing) are both integral parts of data management, but they have different functionalities.OLTP focuses on handling large numbers of transactional operations in real time, ensuring data consistency and reliability for daily busine 6 min read Difference between ELT and ETLIn managing and analyzing data, two primary approaches i.e. ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform), are commonly used to move data from various sources into a data warehouse. Understanding the differences between these methods is crucial for selecting the right approach ba 5 min read Types of OLAP Systems in DBMSOLAP is considered (Online Analytical Processing) which is a type of software that helps in analyzing information from multiple databases at a particular time. OLAP is simply a multidimensional data model and also applies querying to it.Types of OLAP ServersRelational OLAPMulti-Dimensional OLAPHybri 4 min read Data Warehousing ModelData Modeling Techniques For Data WarehouseData warehouse schemas structure data into fact tables (numeric metrics) and dimension tables (descriptive attributes). The three core models are: star schema (denormalized for speed), snowflake schema (normalized for storage efficiency), and galaxy schema (multiple interconnected facts). Star schem 3 min read Difference between Fact Table and Dimension TableIn data warehousing, fact tables and dimension tables are key components of star or snowflake schemas. Fact tables store numeric data like sales or order amounts and include foreign keys linking to dimension tables. Dimension tables provide context with descriptive details like product names or cust 4 min read Data Modeling Techniques For Data WarehouseData warehouse schemas structure data into fact tables (numeric metrics) and dimension tables (descriptive attributes). The three core models are: star schema (denormalized for speed), snowflake schema (normalized for storage efficiency), and galaxy schema (multiple interconnected facts). Star schem 3 min read Concept Hierarchy in Data MiningPrerequisites: Data Mining, Data Warehousing Data mining refers to the process of discovering insights, patterns, and knowledge from large data. It involves using techniques from fields such as statistics, machine learning, and artificial intelligence to extract insights and knowledge from data. Dat 7 min read Data TransformationWhat is Data Transformation?Data transformation is an important step in data analysis process that involves the conversion, cleaning, and organizing of data into accessible formats. It ensures that the information is accessible, consistent, secure, and finally recognized by the intended business users. This process is undertak 4 min read Data Normalization in Data MiningData normalization is a technique used in data mining to transform the values of a dataset into a common scale. This is important because many machine learning algorithms are sensitive to the scale of the input features and can produce better results when the data is normalized. Normalization is use 5 min read Aggregation in Data MiningData Aggregation is used when raw datasets are too detailed for analysis. It summarizes data into meaningful metrics like sum, count, or average to improve insights and user experience. Aggregated data aids in understanding customer behavior, creating reports, and tracing data errors (data lineage). 4 min read DiscretizationDiscretization is the process of converting continuous data or numerical values into discrete categories or bins. This technique is often used in data analysis and machine learning to simplify complex data and make it easier to analyze and work with. Instead of dealing with exact values, discretizat 3 min read What is Data Sampling - Types, Importance, Best PracticesData sampling is a statistical method that selects a representative subset (sample) from a large dataset. Analysts then study this sample to make inferences and draw conclusions about the entire dataset. It's a powerful tool for handling large volumes of data efficientlyData Sampling ProcessThe proc 5 min read Difference Between Feature Selection and Feature ExtractionFeature selection and feature extraction are two key techniques used in machine learning to improve model performance by handling irrelevant or redundant features. While both works on data preprocessing, feature selection uses a subset of existing features whereas feature extraction transforms data 2 min read Introduction to Dimensionality ReductionWhen working with machine learning models, datasets with too many features can cause issues like slow computation and overfitting. Dimensionality reduction helps to reduce the number of features while retaining key information. Techniques like principal component analysis (PCA), singular value decom 4 min read Advanced Data WarehousingMeasures in Data Mining - Categorization and ComputationIn data mining, Measures are quantitative tools used to extract meaningful information from large sets of data. They help in summarizing, describing, and analyzing data to facilitate decision-making and predictive analytics. Measures assess various aspects of data, such as central tendency, variabil 5 min read Rules For Data Warehouse ImplementationA data warehouse is a central system where businesses store and organize data from various sources, making it easier to analyze and extract valuable insights. It plays a vital role in business intelligence, helping companies make informed decisions based on accurate, historical data. Proper implemen 5 min read How To Maximize Data Warehouse PerformanceData warehouse performance plays a crucial role in ensuring that businesses can efficiently store, manage and analyze large volumes of data. Optimizing the performance of a data warehouse is essential for enhancing business intelligence (BI) capabilities, enabling faster decision-making and providin 6 min read Top 15 Popular Data Warehouse ToolsA data warehouse is a data management system that is used for storing, reporting and data analysis. It is the primary component of business intelligence and is also known as an enterprise data warehouse. Data Warehouses are central repositories that store data from one or more heterogeneous sources. 11 min read Data Warehousing SecurityData warehousing is the act of gathering, compiling, and analyzing massive volumes of data from multiple sources to assist commercial decision-making processes is known as data warehousing. The data warehouse acts as a central store for data, giving decision-makers access to real-time data analysis 7 min read PracticeLast Minute Notes (LMNs) - Data WarehousingA Data Warehouse (DW) is a centralized system that stores large amounts of structured data from various sources, optimized for analysis, reporting, and decision-making. Unlike transactional databases, which handle daily operations, a data warehouse focuses on analytical processing. This article cove 15+ min read Like