What Is Database Software?

• Database Software Definition

  Database Software Definition

  Database software helps streamline database management by ensuring seamless data storage, monitoring, backup, recovery, and reporting. It enables users to store data in a structured format for easy search and quick access. It can also help ensure the accuracy, consistency, and security of data and information stored in the database.
• How does a database server work?

  How does a database server work?

  A database server is a processor in a local area network (LAN) that hosts single or multiple databases to ensure data storage and retrieval by providing data accessibility to clients in a client-server environment. Alternatively, database software is a comprehensive software solution that acts as an interface between the database and its end users or applications. It allows users to edit, modify, retrieve, and manage data to perform operations. A database management system empowers users to control databases by facilitating various administrative functions, including performance monitoring, data backup, and recovery.

  A database server can hold and run the database software to simplify database management and help streamline performance monitoring to enhance the overall database performance. By using a database management system (DBMS), a database administrator can improve availability, reliability, and security by ensuring easy scalability, higher uptime, automatic backup and recovery in real-time, and data security.
• Types of database software

  Types of database software

  There are different types of database software, and every enterprise may need a specific database management system that meets its requirements. Here’s a list of common types of database software:

  • Analytical Database Software: Helps store data extracted from an array of databases for the purpose of data analysis and quantitative performance assessment. With this type of database software, users can edit and filter data stored in the analytical database software to analyze an organization’s or its employees’ performance.
  • Operational Database Software: Commonly known as online transaction processing (OLTP) database software, it allows real-time management and modification of dynamic data. It enables enterprises to easily add, alter, or delete large volumes of data in their database continuously. Customer database software for customer relationship management is a typical example. Some other business use cases for implementing operational database software include financial transaction processing and online purchase process.
  • Distributed Database Software: This type of database software implements a centralized DBMS that connects and controls data residing on an array of storage devices distributed across multiple locations. A distributed database may exist over LAN, WAN, or network servers.
  • External Database Software: It allows users to access data often via the internet.
  • Data Warehouse Database Software: This type of database software pulls the required data from various databases and stores it centrally for helping users in reporting and deriving insights.
  • End-User Database Software: In simple terms, end-user database software allows specific users to create, edit, store, and manage data.
• What do relational database and non-relational database mean?

  What do relational database and non-relational database mean?

  Relational (SQL) databases and non-relational (NoSQL) databases are two of the most common types of databases.

  1. A relational database stores information in a tabular format with corresponding rows and columns. It allows you to organize data in a structured way to ensure quick and easy access to the information. With a highly integrated structure and clear dependencies, relational database software is the optimal choice to build or support complex applications. Since relational databases adhere to SQL essential requirements such as ACID compliance (Atomicity, Consistency, Isolation, Durability), they’re a good option to build applications where database integrity is paramount. Some key relational database examples include Oracle, PostgreSQL, and MariaDB, among others. Here’s a quick list of relational database software capabilities:
     • Operates with structured data
     • Ensures a high level of data integrity due to strict data structure
     • Provides integrity
     • Improves data analysis and reporting by supporting complex SQL queries
     • Supports high-performance requirements of transactional type applications
  2. Unlike relational database software systems, a non-relational database stores semi-structured or unstructured data. These are also known as NoSQL databases since they aren’t restricted to table structures. NoSQL databases are document-oriented and store non-structured data, including photos, videos, and articles collated in a single document. Non-relational databases gained widespread popularity with the growing need and complexity of web applications. Some common NoSQL database software examples include MongoDB, Redis, Cassandra, and more. Following are the common capabilities of a non-relational database:
     • Stores vast volumes of data with minimum structure
     • Supports changing business requirements with ease of scalability and flexibility
     • Captures all types of data
     • The optimal option for flexible data storage
     • Provides a flexible data model to eliminate the need for schema modification

  Since both types of databases differ in terms of data processing, retrieval, distribution, and use cases, it’s critical to determine which database type is best for the organization and applications.
• On-premises vs. hosted database differences in administration

  On-premises vs. hosted database differences in administration

  With data taking center stage for modern businesses, it’s becoming increasingly critical for companies to plan their data management needs strategically. It’s significant for enterprises to intelligently build, store, and manage data to support insightful business decisions and meet rising customer demands.

  There are two broad options available for database hosting: on-premises databases and hosted databases. This section explores the basic concepts of these databases and outlines the differences between the two approaches.

  Let’s explore different aspects that clearly differentiate between the two database management approaches.

  • Scalability: A database hosted on the cloud is easy to scale up and down depending on your needs. Enterprises can automatically align the database capacity with the growing data volume and variety in real-time. On the other hand, enterprises with on-premises databases may face a lengthy procurement process that will cost the business time and money. Therefore, it’s not suitable for industries that witness varying seasonal demands and need on-demand scalability capabilities.
  • Database Speed and Reliability: Enterprises with an on-premises database directly depend on their underlying infrastructure to support high-performance needs. Since the data remains within the office premises, data latency issues arising from poor network connectivity aren’t a concern. Alternatively, enterprises using hosted databases rely on the uptime and availability SLAs agreed upon with the service provider.
  • Capacity Planning and Resource Management: An on-premises database requires multiple layers of approval, which usually delays the process and becomes a performance bottleneck. Additionally, resource management is generally static in the case of an on-premises database. In contrast, web-based database software helps ensure dynamic and fast capacity planning and resource management to support overall database performance.
  • Maintenance Activities: An on-premises database results in more overheads for an enterprise since it’s fully managed internally. In comparison, hosted databases implemented using the IaaS or PaaS model result in fewer overheads as the service provider manages maintenance tasks.
  • Performance Monitoring: Generally, web-based database software provides built-in centralized monitoring for proactively tracking performance in real time from unified dashboards and reports. However, on-premises databases lack integrated central monitoring capabilities, leading to escalations and broken maintenance processes that eventually hamper performance.
• Database software performance monitoring

  Database software performance monitoring

  Monitoring database performance is significant to ensure whether the database effectively supports the business applications without getting bogged down by slowdowns or lags in promptly responding to queries. The right database software helps identify the effects of performance issues and pinpoint bottlenecks, analyze root cause, and improve database performance.

  Database monitoring is essential to fine-tune database performance at every level. For example, monitoring a customer database software helps determine inefficient SQL statements at the SQL level that lead to latency and errors while retrieving customer data. Choosing an effective database performance management software helps to ensure:

  • Real-time and historical data analysis
  • Intuitive root-cause identification
  • Query level performance analysis
  • Advanced database anomaly detection
  • Cross-platform database support management

  The essence of database monitoring lies in consistently tracking relevant database monitoring metrics to proactively spot anomalies and troubleshoot issues before they hamper database performance. Here’s a list of commonly used database monitoring metrics.

  • Database Throughput: Measures the number of incoming queries a database server processes over a certain unit of time (per second or per hour). If it’s less than the number of incoming queries, then the server may face overload and result in a longer waiting time.
  • Database Response: Measures the average response time per query. It reflects the latency in the database server’s response to provide a query result.
  • Number of Errors: Helps monitor the number of queries for each error response code to easily identify the most frequently occurring errors. This helps you fix recurring errors and optimize queries.
  • Resource Usage: Determines how resources are used for specific database operations and helps in analyzing performance problems pertaining to resource availability. Analysis of resource usage metrics over time can facilitate efficient capacity planning considering the cyclical changes in workload.
  • Database File I/O: Determines the amount of data written to and read from a specific file in the database. It helps check whether I/O is suitable for the size of the file.