What do data warehouse support OLAP or OLTP?

What do data warehouse support OLAP or OLTP?

Oltp examples

Although sometimes used interchangeably, the terms data warehouse and online analytical processing (OLAP) are applied to different components of systems known as decision support systems or business intelligence systems. The components of these types of systems include databases and applications that provide the tools analysts need to make decisions regarding the technical support of the organization.

OLAP technology enables more efficient use of data warehouses for online analysis, providing fast answers to complex and iterative analytical queries. OLAP’s multidimensional data models and data aggregation techniques organize and summarize large amounts of data so that they can be quickly evaluated using online analysis and graphical tools. The response to a query performed on historical data often leads to subsequent queries in which the analyst seeks more specific answers or explores possibilities. OLAP systems provide the speed and flexibility needed to support the analyst in real time.

Oltp ventajas y desventajas

OLTP vs. OLAPhttp://datawarehouse4u.info/OLTP-vs-OLAP.htmlWe puede dividir los sistemas informáticos en transaccionales (OLTP) y analíticos (OLAP). En general, podemos suponer que los sistemas OLTP proporcionan datos de origen a los almacenes de datos, mientras que los sistemas OLAP ayudan a analizarlos.- OLTP (On-line Transaction Processing) se caracteriza por un gran número de transacciones cortas en línea (INSERT, UPDATE, DELETE). El énfasis principal de los sistemas OLTP se pone en el procesamiento muy rápido de las consultas, el mantenimiento de la integridad de los datos en entornos de acceso múltiple y una eficacia medida por el número de transacciones por segundo. En la base de datos OLTP hay datos detallados y actuales, y el esquema utilizado para almacenar las bases de datos transaccionales es el modelo de entidad (normalmente 3NF).- OLAP (On-line Analytical Processing) se caracteriza por un volumen relativamente bajo de transacciones.Las consultas suelen ser muy complejas e implican agregaciones. Para los sistemas OLAP, el tiempo de respuesta es una medida de eficacia. Las aplicaciones OLAP son ampliamente utilizadas por las técnicas de minería de datos. En la base de datos OLAP hay datos agregados e históricos, almacenados en esquemas multidimensionales (normalmente en forma de estrella).

The most commonly used olap databases are:

Multidimensional databases are a variation of the relational model that uses OLAP cubes to organize data and express the relationships between them. The main advantages of this type of databases are the versatility to cross-reference information and the high speed of response. This makes them basic tools for Business Intelligence or Big Data solutions, where data analysis is crucial.

In general, we are used to working with transaction-oriented databases (known as OLTP databases). Below we will see the main differences with multidimensional databases (known as OLAP databases).

OLTP systems are transaction-oriented relational databases (RDBMS). A transaction is a sequence of operations carried out by a database atomically. Transactions can be of four different types: SELECT, INSERT, DELETE and UPDATE. Since it is an atomic process, each transaction has only two possible endings: commit (if all operations have been carried out correctly) or rollback (when an operation in the sequence has failed, in which case the changes produced by the rest of the operations in the transaction must be undone and the error alerted). Transactions are the backbone of virtually any management program or web page in the world. The need for them is very clear, for example, in the banking sector.

Oltp features

A Data Warehouse is an electronic warehouse where a company or organization usually keeps a large amount of information. The data in a data warehouse must be stored securely, reliably, easily retrievable and easy to manage.

A data warehouse is a unified repository for all data collected by a company’s various systems. The repository can be physical or logical and emphasizes the capture of data from various sources primarily for analytical and access purposes.

Typically, a data warehouse is hosted on a corporate server or, increasingly, in the cloud. Data from different Online Transaction Processing (OLTP) applications and other sources is selectively extracted for use by analytical and user query applications.

Data Warehouse is a data warehousing architecture that enables business executives to organize, understand and use their data to make strategic decisions. A data warehouse is a familiar architecture in many modern enterprises.