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What is MOLAP?Multidimensional OLAP (MOLAP) is a classical OLAP that facilitates data analysis by using a multidimensional data cube. Data is pre-computed,pre-summarized, and stored in a MOLAP (a major difference from ROLAP). Using a MOLAP, a user can use multidimensional view data with different facets. Multidimensional data analysis is also possible if a relational database is used. By that would require querying data from multiple tables. On the contrary, MOLAP has all possible combinations of data already stored in a multidimensional array. MOLAP can access this data directly. Hence, MOLAP is faster compared to Relational Online Analytical Processing (ROLAP). In this tutorial, you will learn- What is MOLAP? MOLAP Architecture Implementation considerations is MOLAP Molap Advantages Molap Disadvantages MOLAP Tools Key Points In MOLAP, operations are called processing.MOLAP tools process information with the same amount of response time irrespective of the level of summarizing.MOLAP tools remove complexities of designing a relational database to store data for analysis.MOLAP server implements two level of storage representation to manage dense and sparse data sets. The storage utilization can be low if the data set is sparse. Facts are stored in multi-dimensional array and dimensions used to query them.MOLAP Architecture MOLAP Architecture includes the following components − Database server.MOLAP server.Front-end tool. Above given MOLAPArchitectures, shown in given figure The user request reports through the interfaceThe application logic layer of the MDDB retrieves the stored data from DatabaseThe application logic layer forwards the result to the client/user. MOLAP architecture mainly reads the precompiled data. MOLAP architecture has limited capabilities to dynamically create aggregations or to calculate results that have not been pre-calculated and stored. For example, an accounting head can run a report showing the corporate P/L account or P/L account for a specific subsidiary. The MDDB would retrieve precompiled Profit & Loss figures and display that result to the user. Implementation considerations is MOLAPIn MOLAP it’s essential to consider both maintenance and storage implications to creating strategy for building cubes. Proprietary languages used to query MOLAP. However, it involves extensive click and drag support for example MDX by Microsoft. Difficult to scale because the number and size of cubes required when dimensions increase. API’s should provide for probing the cubes. Data structure to support multiple subject areas of data analyses which data can be navigated and analyzed. When the navigation changes, the data structure needs to be physically reorganized.Need different skill set and tools for Database administrator to build, maintain the database.MOLAP AdvantagesMOLAP can manage, analyze and store considerable amounts of multidimensional data.Fast Query Performance due to optimized storage, indexing, and caching.Smaller sizes of data as compared to the relational database.Automated computation of higher level of aggregates data.Help users to analyze larger, less-defined data. MOLAP is easier to the user that’s why It is a suitable model for inexperienced users. MOLAP cubes are built for fast data retrieval and are optimal for slicing and dicing operations.All calculations are pre-generated when the cube is created. MOLAP DisadvantagesOne major weakness of MOLAP is that it is less scalable than ROLAP as it handles only a limited amount of data.The MOLAP also introduces data redundancy as it is resource intensiveMOLAP Solutions may be lengthy, particularly on large data volumes. MOLAP products may face issues while updating and querying models when dimensions are more than ten. MOLAP is not capable of containing detailed data. The storage utilization can be low if the data set is highly scattered. It can handle the only limited amount of data therefore, it’s impossible to include a large amount of data in the cube itself. MOLAP ToolsEssbase – Tools from Oracle that has a multidimensional database. Express Server – Web-based environment that runs on Oracle database. Yellowfin – Business analytics tools for creating reports and dashboards.Clear Analytics – Clear analytics is an Excel-based business solution. SAP Business Intelligence – Business analytics solutions from SAPSummary: Multidimensional OLAP (MOLAP) is a classical OLAP that facilitates data analysis by using a multidimensional data cube.MOLAP tools process information with the same amount of response time irrespective of the level of summarizing.MOLAP server implements two level of storage to manage dense and sparse data sets.MOLAP can manage, analyze, and store considerable amounts of multidimensional data.It helps to automate computation of higher level of aggregates dataIt is less scalable than ROLAP as it handles only a limited amount of data.  

Difference between Database and Data Warehouse

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What is Database?A database is a collection of related data which represents some elements of the real world. It is designed to be built and populated with data for a specific task. It is also a building block of your data solution. In this tutorial, you will learn What is Database? What is a Data Warehouse? Why use a Database? Why Use Data Warehouse? Characteristics of Database Characteristics of Data Warehouse Difference between Database and Data Warehouse Applications of Database Applications of Data Warehousing Disadvantages of Database Disadvantages of Data Warehouse What is a Data Warehouse?A data warehouse is an information system which stores historical and commutative data from single or multiple sources. It is designed to analyze, report, integrate transaction data from different sources. Data Warehouse eases the analysis and reporting process of an organization. It is also a single version of truth for the organization for decision making and forecasting process. Why use a Database?Here, are prime reasons for using Database system: It offers the security of data and its access A database offers a variety of techniques to store and retrieve data. Database act as an efficient handler to balance the requirement of multiple applications using the same data A DBMS offers integrity constraints to get a high level of protection to prevent access to prohibited data. A database allows you to access concurrent data in such a way that only a single user can access the same data at a time. Why Use Data Warehouse?Here, are Important reasons for using Data Warehouse: Data warehouse helps business users to access critical data from some sources all in one place. It provides consistent information on various cross-functional activities Helps you to integrate many sources of data to reduce stress on the production system. Data warehouse helps you to reduce TAT (total turnaround time) for analysis and reporting. Data warehouse helps users to access critical data from different sources in a single place so, it saves user’s time of retrieving data information from multiple sources. You can also access data from the cloud easily. Data warehouse allows you to stores a large amount of historical data to analyze different periods and trends to make future predictions. Enhances the value of operational business applications and customer relationship management systems Separates analytics processing from transactional databases, improving the performance of both systems Stakeholders and users may be overestimating the quality of data in the source systems. Data warehouse provides more accurate reports. Characteristics of DatabaseOffers security and removes redundancy Allow multiple views of the data Database system follows the ACID compliance ( Atomicity, Consistency, Isolation, and Durability). Allows insulation between programs and data Sharing of data and multiuser transaction processing Relational Database support multi-user environment Characteristics of Data WarehouseA data warehouse is subject oriented as it offers information related to theme instead of companies’ ongoing operations. The data also needs to be stored in the Datawarehouse in common and unanimously acceptable manner. The time horizon for the data warehouse is relatively extensive compared with other operational systems. A data warehouse is non-volatile which means the previous data is not erased when new information is entered in it.Difference between Database and Data Warehouse Parameter Database Data Warehouse Purpose Is designed to record Is designed to analyze Processing Method The database uses the Online Transactional Processing (OLTP) Data warehouse uses Online Analytical Processing (OLAP). Usage The database helps to perform fundamental operations for your business Data warehouse allows you to analyze your business. Tables and Joins Tables and joins of a database are complex as they are normalized. Table and joins are simple in a data warehouse because they are denormalized. Orientation Is an application-oriented collection of data It is a subject-oriented collection of data Storage limit Generally limited to a single application Stores data from any number of applications Availability Data is available real-time Data is refreshed from source systems as and when needed Usage ER modeling techniques are used for designing. Data modeling techniques are used for designing. Technique Capture data Analyze data Data Type Data stored in the Database is up to date. Current and Historical Data is stored in Data Warehouse. May not be up to date. Storage of data Flat Relational Approach method is used for data storage. Data Ware House uses dimensional and normalized approach for the data structure. Example: Star and snowflake schema. Query Type Simple transaction queries are used. Complex queries are used for analysis purpose. Data Summary Detailed Data is stored in a database. It stores highly summarized data. Applications of DatabaseSector Usage Banking Use in the banking sector for customer information, account-related activities, payments, deposits, loans, credit cards, etc. Airlines Use for reservations and schedule information. Universities To store student information, course registrations, colleges, and results. Telecommunication It helps to store call records, monthly bills, balance maintenance, etc. Finance Helps you to store information related stock, sales, and purchases of stocks and bonds. Sales & Production Use for storing customer, product and sales details. Manufacturing It is used for the data management of the supply chain and for tracking production of items, inventories status. HR Management Detail about employee’s salaries, deduction, generation of paychecks, etc. Applications of Data WarehousingSector Usage Airline It is used for airline system management operations like crew assignment, analyzes of route, frequent flyer program discount schemes for passenger, etc. Banking It is used in the banking sector to manage the resources available on the desk effectively. Healthcare sector Data warehouse used to strategize and predict outcomes, create patient’s treatment reports, etc. Advanced machine learning, big data enable datawarehouse systems can predict ailments. Insurance sector Data warehouses are widely used to analyze data patterns, customer trends, and to track market movements quickly. Retain chain It helps you to track items, identify the buying pattern of the customer, promotions and also used for determining pricing policy. Telecommunication In this sector, data warehouse used for product promotions, sales decisions and to make distribution decisions. Disadvantages of DatabaseCost of Hardware and Software of an implementing Database system is high which can increase the budget of your organization. Many DBMS systems are often complex systems, so the training for users to use the DBMS is required. DBMS can’t perform sophisticated calculations Issues regarding compatibility with systems which is already in place Data owners may lose control over their data, raising security, ownership, and privacy issues. Disadvantages of Data WarehouseAdding new data sources takes time, and it is associated with high cost. Sometimes problems associated with the data warehouse may be undetected for many years. Data warehouses are high maintenance systems. Extracting, loading, and cleaning data could be time-consuming. The data warehouse may look simple, but actually, it is too complicated for the average users. You need to provide training to end-users, who end up not using the data mining and warehouse. Despite best efforts at project management, the scope of data warehousing will always increase. What Works Best for You?To sum up, we can say that the database helps to perform the fundamental operation of business while the data warehouse helps you to analyze your business. You choose either one of them based on your business goals.