Analytical Database Definition
An analytical database stores and manages big data, including business, market and customer data for business intelligence (BI) analysis. Analytical databases are specially optimized for faster queries and scalability.
What Is an Analytical Database?
Analytical database software specializes in big data management for business applications and services. Analytical databases are optimized to provide quick query response times and advanced analytics. They are also more scalable than traditional databases and often times are columnar databases that can efficiently write and read data to and from hard disk storage in order to speed up the time it takes to return a query. Analytical database features include column-based storage, in-memory loading of compressed data and the ability to search data through multiple attributes.
How Are Analytical Databases Used?
Analytic database software is designed to quickly analyze massive amounts of data, performing up to 1,000 times faster than an operational database for demanding analytical workloads. Business analysts, researchers, financial market analysts, big data analysts, geospatial analysts and data scientists rely on the high availability of analytical databases that can handle high data volumes.
The historical data in an analytical database is compared with operational data. Historical data refers to data that is not in real-time but could only be a few hours old. Contrasting analytical and operational data helps determine the best processes for transactions and other business or research decisions.
Examples of Analytical Databases
- Market data — Historical price and volume data for financial markets for testing trading strategies.
- Transactional data — Historical transactions that can include purchasing patterns for improved marketing.
- Sensor data — Historical data from sensors that monitor situations like the weather.
- Natural language data — Study of social media posts for research purposes.
- Process data — Study of processes to better understand logistics and find bottlenecks.
- Machine data — Software and hardware-generated data from products to improve efficiency.
Difference Between Analytical Database and Operational Database
An analytical database is also known as OLAP (OnLine Analytical Processing). It is used for fast processing of massive amounts of data with few or no filters.
An operational database is also known as OLTP (OnLine Transaction Processing). It is used for looking up single rows of information for quick updates of a group’s daily operations.
Operational data records business happenings. But the complexity of analytical data helps determine business strategy and decisions.
Operational databases contain transactional data while analytical databases are designed for efficient analysis.
Analytical Database Benefits
Interest in analytical databases has risen in the last 15 years with the increased demand for tools that enable data processing in real-time from sources such as: IOT connected devices, mobile devices, remote sensors, biometrics devices and streaming video and media software. Here as some high level benefits of using an analytical database:
- Columnar data storage — A column versus row-based design which allows for very fast analysis of large sets of data point within a column. Traditional row-based designs cannot scale for massive amounts of data the way columns can.
- Efficient data compression — The columnar design of analytical databases allow for the most efficient version of data compression, which is how database space and speed is maximized.
- Distributed workloads — Data is stored on a cluster of servers also called “nodes.” When data is stored across many different parallel servers, queries can be processed across the board. This allows for very efficient processing of large volumes of data.
Other analytical database benefits include:
- Horizontal scalability
- SQL compatibility
- Advanced math and statistical functionality
Does OmniSci Offer an Analytical Database?
Yes. OmniSci offers an open source analytical database using the improved query and visualization speed of graphic processing units (GPUs). OmniSciDB is the foundation of the OmniSci Accelerated Analytics Platform. OmniSciDB is SQL-based, relational, columnar and specifically developed to harness the parallel processing power of GPUs. OmniSciDB can query up to billions of rows in milliseconds, and is capable of unprecedented ingestion speeds, making it the ideal SQL engine for the era of big, high-velocity data.