Analytical Databases Explained: Definition, Top Features, and Use Cases

An analytical database is a data storage solution designed to optimize read, retrieval, and analysis of large datasets. The basic function of analytical databases mirrors more traditional transactional databases. However, while a transactional database is designed to optimize write (insert) operations, analytical databases emphasize high-performance read (select) operations that scale effectively to handle large sets of data.

The architecture of analytical databases is uniquely designed to emphasize read operations, but the specifics of this technical design are beyond the scope of this article. The key to understanding analytical databases for most people is not the technical details, but rather their results: You can pull massive volumes of data from analytical databases, and you can do it fast.

Analytical databases are thus well-suited for tasks that put a high demand on data storage solutions to output data. Typically, traditional transactional databases will suffice for most read operations, but analytical databases shine when faced with complex queries such as those used for data mining, predictive analysis, and business intelligence.

Types of analytical databases

Analytical databases can be categorized into various types based on their architecture and the data model they employ:

• Column-oriented databases: Unlike traditional row-oriented databases, these databases store data tables by columns rather than rows, making them ideal for reading large rows of data efficiently.
• Data warehouse: These are centralized repositories for data that integrate data from multiple sources, structured and unstructured, to provide a comprehensive view.
• OLAP (Online Analytical Processing) databases: These databases use a multidimensional data model, allowing for complex analytical and ad-hoc queries with a rapid execution time.
• Time series databases: Optimized for handling time-stamped data like logs, real-time monitoring data, and IoT data.

The different types of analytical databases, including column-oriented, data warehouses, OLAP, and time series databases, all share the primary goal of optimizing data analysis. They facilitate complex queries, large-scale data aggregation, and rapid retrieval speeds, which are essential for data-driven decision-making processes.

Top 7 features of analytical databases

While different types of analytical databases may be optimal in different circumstances, all analytical databases share a set of important features that make them valuable to data-driven businesses:

• Columnar data storage: Columnar storage is one of the defining features of modern analytical databases. It allows for efficient encoding and representation, which reduces the disk I/O requirements and accelerates query performance.
• Data compression: Data compression reduces the storage cost and improves performance by decreasing the amount of data that needs to be read from storage. Analytical databases leverage various compression techniques such as dictionary encoding, run-length encoding, and delta compression.
• In-memory processing: In-memory processing is a key feature of analytical databases that allows data to be stored within the system's main memory, rather than on traditional disk storage. This significantly speeds up data retrieval, analysis, and processing, enabling real-time insights and faster decision-making.
• Parallel processing: Analytical databases often distribute data processing tasks across multiple processors or nodes. Parallel processing enables the handling of large volumes of data more quickly and efficiently than would be possible with a single processor.
• Data aggregation and transformation: These databases provide powerful tools for data aggregation (such as SUM, AVG, COUNT, etc.) and transformation (like SQL's JOINs and WINDOW functions), which are essential for summarizing and making sense of large datasets.
• Horizontal scalability: Horizontal scalability refers to the ability to increase capacity by connecting multiple hardware or software entities so that they work as a single logical unit. When database workloads increase, analytical databases can scale out across additional nodes to maintain high performance.
• Advanced query capabilities: Analytical databases are built to handle complex queries that involve multiple joins and aggregations which are not usually performant in transactional databases. They also support ad-hoc query capabilities for dynamic data analysis needs.

5 use cases for analytical databases

• Financial markets historical data analysis: Analytical databases play a crucial role in the financial sector, primarily due to their ability to handle and analyze extensive amounts of historical market data. Financial institutions use these databases to mine data from various sources, including stock exchanges, economic indicators, and corporate financial reports. The objective is to identify patterns, trends, and potential investment opportunities.
  
  One of the primary uses of analytical databases in finance is for quantitative analysis, where large datasets are processed to calculate statistical measures that inform investment decisions. This might include volatility measures, correlation between different assets, or the performance of a stock relative to its sector. The speed and efficiency of analytical databases allow for real-time analytics, which is critical in a sector where timing and precision are paramount.

• User behavioral data collection, analysis, and aggregation: User behavior involves many data touchpoints that must be collated to provide meaningful insight. When a user interacts with an application or a website, the granularity of each action causes the amount of data required to record these interactions to scale exponentially.
  
  Analytical databases help organizations understand user behavior by providing fast access to these large sets of data from user interactions with applications and websites without causing a performance bottleneck. By analyzing this behavioral data, organizations can better understand and improve their user experience.

• Historical sensor data: In industries such as manufacturing and logistics, analytical databases are used to perform historical analysis on sensor data, helping to predict machine failure or optimize supply chains.
  
  The advanced query capabilities of analytical databases enable complex models and algorithms to run sophisticated simulations and back-testing strategies. These operations require the manipulation of massive datasets over different time frames to forecast future behavior based on historical patterns. For instance, a typical query could involve pulling decades of timestamped data, helping analysts to refine their models with high precision.

• Real-time security and fraud analysis: Security threats and fraud attempts are a high-risk area of data analysis that requires more certainty and statistical precision than other fields. As a result, the data sets underlying this analysis must be larger and thus put more stress on data storage solutions.
  
  Analytical databases play a crucial role in detecting and responding to security threats and fraud in real-time by ensuring access to large volumes of relevant data across various systems and thus the ability to understand and respond in real time.

• Natural language processing data: With the rise of human language processing systems, so also has risen the need for data solutions that can handle a huge volume of text. Natural language processing often depends upon training sets that can exceed terabytes of data.
  
  Analytical databases are crucial for their ability to performantly respond to queries and back-end processes required by systems that analyze, process, and utilize natural language training sets.

Simplify data replication to analytical databases with CData Sync

CData Sync offers a straightforward solution to integrate data from various sources into an analytical database. This tool simplifies the process of data ingestion, replication, and consolidation, enabling businesses to focus more on data analysis rather than data management. With features supporting real-time data access, comprehensive ETL capabilities, and a user-friendly interface, CData Sync ensures that your analytical databases are always up-to-date with minimal effort.

If you want to leverage the power of analytical databases in the modern data-driven world, then migrating your data storage solution is made easier with Sync. No matter where you want to move data from, you can see how simple migration and data replication can be with a free, fully-functional 30-day trial.