An analytical database is a read-only system that stores historical data on business metrics such as sales performance and inventory levels. Business analysts, executives, and managers access this data through business intelligence (BI) tools, SQL queries, and other analytical applications to support their decision-making tasks.
An analytical database is designed to support business intelligence (BI) and analytical applications, typically as part of a data warehouse or data mart. This type of database contains historical data derived from transaction data but can include data from other sources. It separates analysis workload from transaction workload and enables an organization to consolidate data from several sources.
In addition to relational databases, analytical databases can also be non-relational or NoSQL databases, and they can be built on top of Hadoop or other big data systems. They are optimized for read-intensive operations (SELECT statements in SQL) and are rarely updated because they are used as a data source for reporting and data analysis rather than transaction processing.
The primary purpose of an analytical database is to support business decision-making processes. It provides a way to consolidate, store, and retrieve large volumes of data in a way that's optimized for reporting and analysis.
Analytical databases are used to create reports, generate business insights, and support decision-making tasks. They are used in various business applications such as customer relationship management (CRM), enterprise resource planning (ERP), and business intelligence (BI) tools.
Analytical databases play a crucial role in data analysis. They provide a platform for data analysts and data scientists to query and retrieve data efficiently. The data stored in these databases is already cleaned, transformed, and optimized for analysis. This saves a significant amount of time and resources that would otherwise be spent on data preparation.
Moreover, analytical databases are designed to handle complex queries on large data sets. They have features like columnar storage, data compression, and advanced indexing to speed up data retrieval. This makes them an excellent choice for data-intensive tasks such as data mining, predictive analytics, and machine learning.
In conclusion, an analytical database is an essential tool for any organization that relies on data to drive decision-making. It provides a robust and efficient platform for storing and analyzing large volumes of data, enabling businesses to gain valuable insights and make informed decisions.