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Operational Database

Operational Database Definition

An operational database is a database management system that stores and processes data in real-time.

Diagram showing generic flow chart depicting operational database management system

Operational Database FAQs

What is an Operational Database?

An operational database management system is software that is designed to allow users to easily define, modify, retrieve, and manage data in real-time. While conventional databases rely on batch processing, operational database systems are oriented toward real-time, transactional operations. Operational databases, which can be based on SQL or NoSQL, are the source for data warehouses and are critical to business analytics operations. Popular operational database examples include Apache Cassandra and AWS Dynamo.

Database operations examples in business include the ability to store, modify, manage, and retrieve large quantities of specific information, such as mission-critical business data, payroll records, call data records, customer information, employee data, and sales data. An operational database in big data can be used to monitor activities, audit suspicious transactions, and review custom history. They can also be used to record e-commerce transactions in an On-Line Transaction Processing database (OLTP). Another important characteristic of an operational database system is the ability to share information throughout the enterprise and over the Internet.

What Does an Operational Database Do?

Database requirements for operational data include:

  • Indexing and Cataloging: indexing divides data into primary, secondary and clustered indexes so that it can be easily retrieved; cataloging assigns key attributes to files so that they can be recalled effectively
  • Replication: copies and stores data at multiple locations to lower latency and provide resilience
  • File Storage and Structure: files are sorted and stored at relevant locations to help simplify complex use cases
  • Query Processing: in an instant, queries input by the user are translated into simple instructions, then analyzed, evaluated, and optimized; then relevant data is extracted from the database and presented to the user
  • Transactions Support: a transactional system ensures the operational database is working consistently in a steady-state, in a concurrent state, and maintains recovery services, also ensures the system is ACID-compliant, delivering Atomicity, Consistency, Isolation, and Durability.

Difference Between Operational Database and Analytical
Database

An analytical database is a read-only, specialized system that is specifically designed to support analytics and business intelligence applications. Analytical database configuration includes column-based data storage, in-memory hardware usage, integrated functions such as mining, and typically functions as part of larger data warehouses, enabling faster query performance, better maintenance, and scalability. The main types of analytical databases on the market include columnar databases, data warehouse appliances, in-memory, massively parallel processing (MPP), and online analytical processing (OLAP).

While analytical databases focus on analytics, operational databases are used for transaction processing in the operations of an enterprise. Where transaction processing supports data warehouses and business intelligence applications, analytical databases tend to provide superior performance and scalability than conventional relational database software.

Difference Between Operational Database System and Data Warehouse

Both data warehouses and operational databases can be relational databases, each addresses different requirements.

Operational database system vs data warehouse:

Data Warehouses

  • designed to support high-volume analytical processing
  • typically concerned with historical data
  • once added, data is rarely changed
  • designed for analysis of business operations
  • optimized for complex, unpredictable queries, which might access multiple rows at a time
  • consists of consistent, valid information
  • supports a small number of concurrent clients
  • subject-oriented
  • optimized to perform fast retrievals of high volumes of data
  • designed for On-line Analytical Processing (OLAP)

Operational Databases

  • designed to support high-volume transaction processing
  • typically concerned with current data
  • data added is updated regularly
  • designed for real-time business processes
  • optimized for simple transactions, accessing one row at a time
  • optimized for real-time validation of incoming information
  • supports thousands of concurrent clients
  • process-oriented
  • optimized to perform fast inserts and updates of smaller volumes of data
  • created for On-line Transaction Processing (OLTP)

Data Warehouse Modeling vs Operational Database Modeling

Data warehouse modeling is the initial stage of building a data warehouse wherein the schema is designed. A well-designed schema enables clients to visualize the relationships between warehouse data, allows an effective data warehouse structure to come to fruition, decreases the costs of implementing the data warehouse, and improves efficiency.

The primary function of a data warehouse is to support the Decision Support System (DSS) process, therefore data warehouse modeling is chiefly concerned with creating an environment that supports complex queries on long term information. In contrast, operational database modeling focuses on efficient support of simple transactions in the database, such as retrieving, inputting, deleting, and modifying data.

Does ScyllaDB Offer Solutions for Operational Databases?

NoSQL-based operational database systems can truly harness the power of big data by using technologies such as ScyllaDB, which is a drop-in replacement for Apache Cassandra with built-in schedulers, its own memory allocator, automatic configuration capabilities, high scalability, and support of global and local indexes. As such, ScyllaDB functions as a hybrid transactional and analytics database that provides the benefits of OLTP and OLAP processing. These features increase database performance by orders of magnitude, increased throughput and storage capacity, reduced hardware costs, and simplified usability and maintainability of an operational database.

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