Multidimensional vs Multi Relational OLAP: A Comparison of Two Approaches for Data Analysis
Online analytical processing (OLAP) is a technique for performing complex queries and calculations on large amounts of data. OLAP enables users to explore and analyze data from multiple perspectives and dimensions, such as time, geography, product, customer, etc. OLAP systems can be classified into two main types: multidimensional OLAP (MOLAP) and multi relational OLAP (MROLAP).
MOLAP is based on a multidimensional data model, where data is stored in a cube or a hypercube structure. Each dimension of the cube represents an attribute or a hierarchy of attributes, and each cell of the cube contains a measure or an aggregated value. MOLAP systems use pre-computed summaries and indexes to speed up query processing and analysis. MOLAP systems are suitable for applications that require fast response time, consistent data quality, and high data compression.
MROLAP is based on a relational data model, where data is stored in tables in a relational database. MROLAP systems use SQL queries to access and manipulate data on demand. MROLAP systems can handle more complex and ad hoc queries than MOLAP systems, as they do not rely on predefined summaries or structures. MROLAP systems are suitable for applications that require flexibility, scalability, and integration with other data sources.
Both MOLAP and MROLAP have their advantages and disadvantages, depending on the nature and requirements of the application. Some of the factors that can influence the choice between MOLAP and MROLAP are:
Data volume: MOLAP systems can handle large amounts of data efficiently, as they use compression and aggregation techniques to reduce storage space and improve performance. However, MOLAP systems may suffer from data sparsity issues, as not all combinations of dimensions and measures may have meaningful values. MROLAP systems can handle sparse data better, as they do not store empty cells or null values.
Data complexity: MOLAP systems can handle simple and structured data well, as they use predefined schemas and hierarchies to organize data. However, MOLAP systems may have difficulties with complex and unstructured data, such as text, images, videos, etc. MROLAP systems can handle complex and unstructured data better, as they can use various data types and formats.
Data freshness: MOLAP systems can provide consistent and accurate data analysis, as they use batch processes to load and refresh data periodically. However, MOLAP systems may have issues with data currency and timeliness, as they do not reflect the latest changes or updates in the data source. MROLAP systems can provide more current and dynamic data analysis, as they access and query data directly from the data source.
Data diversity: MOLAP systems can provide a unified and integrated view of data from multiple sources, as they use a common dimensional model to map and consolidate data. However, MOLAP systems may have limitations with data variety and heterogeneity, as they may not be able to capture all the nuances and differences among different data sources. MROLAP systems can provide more diverse and heterogeneous views of data from multiple sources, as they can use different relational models and schemas to represent data.
In conclusion, multidimensional OLAP and multi relational OLAP are two different approaches for data analysis that have their own strengths and weaknesses. The choice between them depends on the characteristics and needs of the application domain. In some cases, a hybrid or a mixed OLAP system that combines both approaches may be the best solution. 061ffe29dd