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22 Online Database Definitions You Should Know

  • Written By: admin
Online Database Definitions

Effectively managing and leveraging your company’s information is more important than ever. And if you’re like a lot of business professionals, you may still be handling data in spreadsheets. But as your business grows and your data complexity increases, you need a more robust and flexible solution to maintain data integrity – and improve the efficiency of your data management. That’s where online databases come in, offering a powerful alternative to traditional Microsoft spreadsheets for your data storage needs.

Navigating the world of online databases can seem daunting at first, particularly if you’re not familiar with the terminology. But no worries, here are clear definitions for the most important terms you’ll encounter when exploring online databases. Consider it your go-to guide, so that you can make informed decisions on how best store data and manage your business data. 

Here are the top terms and definitions to help you navigate the language of online databases and database software, and learn more about the types of database structure:

  1. Online Database: A database is an organized collection of information typically arranged in a data structure to model important aspects of a business (for example, the availability of rooms in hotels), in a way that supports processes requiring the information (for example, finding a hotel with vacancies). In the context of an online database, this collection of data is stored in the cloud, managed, and accessed via the internet, allowing real-time, end-user access from anywhere with an internet connection.
  2. Database Management System (DBMS): This is specialized database software designed to interact with the user, the database itself, and other applications to capture and analyze data. A DBMS allows for the definition, creation, querying, update, and administration of databases.
  3. Relational Database: This is a type of database that stores and provides access to data types related to one another. Relational database models are an intuitive, straightforward way of representing data in tables. In a relational database, each row in the table is a record with a unique ID called the key. The columns of the table hold attributes of the data, and each record usually has a value for each attribute, making it easy to establish the relationships among data points.
  4. SQL (Structured Query Language): SQL is a standardized programming language that is used to manage relational databases and perform various operations on the data in them. SQL is used to query, insert, update and modify data. Most relational databases support SQL servers, which is an added benefit of using a relational database.
  5. NoSQL: NoSQL databases are a type of database that can handle and sort all kinds of unstructured data – the kind of messy and unpredictable data that our modern life produces like rich media files, full-text documents, invoices, and social media. They are designed to overcome the limitations of traditional relational databases involving volume, variety, and velocity of data. They are especially useful when an organization must analyze large chunks of unorganized data or data that’s stored across multiple virtual servers in the cloud.
  6. Cloud Database: A cloud database is a database service built and accessed through a cloud platform. It serves many of the same functions as a traditional database with the added flexibility of cloud computing. Users can run databases on the cloud independently, using a virtual machine image, or they can purchase access to a database service maintained by a cloud database provider.
  7. Database-as-a-Service (DBaaS): DBaaS is a cloud computing service model that provides users with some form of access to database apps without the need for setting up physical hardware, installing software or configuring for performance. All of the administrative tasks and maintenance are taken care of by the service provider so that all the user or application owner needs to do is use the database.
  8. Schema: In a relational database, the schema is like a blueprint. It describes the way data is organized and how the relationships among them are associated It outlines how data is stored in the database in terms of tables, views, indexes, keys, etc. A schema can be seen as a read-only, logical group of objects.
  9. Data Warehousing: A data warehouse is a system used for reporting and data analysis, and is considered a core component of business intelligence. Data warehousing involves consolidating data from different sources to provide meaningful business insights. It involves unifying data from disparate sources into one comprehensive database to allow for sophisticated analysis and reporting.
  10. Big Data: This is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters.
  11. Data Mining: Data mining is the process of discovering meaningful correlations, patterns, and trends by sifting through large amounts of data stored in online database repositories. Data mining employs pattern recognition technologies, as well as statistical and mathematical techniques.
  12. Normalization: This is a process in database design that organizes data to minimize redundancy and prevent certain types of anomalies during data operations.Data normalization involves dividing a database into two or more tables and defining relationships between the tables to achieve a structure that allows for efficient data operations and ensures the accuracy and integrity of the data.
  13. Indexing: Indexing is a way to optimize the performance of a database by minimizing the number of disk accesses required when a query is processed. It’s a data structure technique that helps efficiently retrieve records from the database files based on some attributes on which the indexing has been done. An index creates an entry for each value, making it faster to retrieve data.
  14. Database Security: This involves a wide range of security measures to protect the database against compromises of their confidentiality, integrity, and availability. It includes various types or categories of controls, such as technical, administrative, and physical. Database security can protect data from unauthorized access, data corruption, and theft.
  15. Database Backup and Recovery: The process of backing up involves making a copy of the data stored on a database, in case the original data is lost or damaged. The complementary process of recovery restores the database from a backup following a data loss event. These processes can help to prevent data loss and system downtime in the event of an error or system failure.
  16. Real-time Database: A real-time database is a database system which uses real-time processing to handle workloads whose state is constantly changing. This is in contrast to traditional databases containing persistent data, mostly unaffected by time. In a real-time database, data has a limited time span to be recorded and stored before it loses its relevance.
  17. Distributed Database: A distributed database is a database in which not all storage devices are attached to a common processor. It may be stored in multiple computers located in the same physical location on a local network, or may be dispersed over a network of interconnected computers. The main advantages of a distributed database are scalability, performance, and reliability.
  18. ACID Properties (Atomicity, Consistency, Isolation, Durability): These are a set of properties that guarantee database transactions are processed reliably. Atomicity requires that each transaction is “all or nothing”; if one part of the transaction fails, the entire transaction fails. Consistency ensures that a transaction brings the database from one valid state to another. Isolation ensures that concurrent execution of transactions results in a system state that would be obtained if transactions were executed serially. Durability ensures that once a transaction has been committed, it will remain committed even in the case of a system failure.
  19. Data Replication: This is the process of storing data in more than one site or node. It enables all the users who are connected to a network to access the same data simultaneously. Data replication contributes to the efficiency and reliability of data.
  20. Data Migration: This is the process of transferring data from one system to another, which can be required for many reasons, including server or storage equipment replacements, maintenance or upgrades, and application migration or consolidation. Data migration is a key consideration for any system implementation, upgrade, or consolidation, and it is typically performed in such a way as to be as automated as possible, freeing up human resources from tedious tasks.
  21. Object-oriented Databases: An object-oriented database (OODB) is a database management system that represents information in the form of objects. OODBs store data in objects which are instances of classes. The class to which the object belongs defines its attributes. This allows database systems to have flexibility, ease of use, and dynamic behavior, which can enhance productivity in database design and make systems more comprehensible.
  22. MySQL: An open-source relational database management system (RDBMS) base on SQL (Structured Query Language). It’s widely used for web databases, being integral to the LAMP (Linux, Apache, MySQL and PHP) open-source web application software. It’s known for its speed, reliability and ease of use.

Unlocking the Potential of Your Data

Navigating the world of online databases may initially seem like you’re trying to decipher a foreign language, but once you understand the key terms, you’ll be on your way to making informed decisions for your business.

Transitioning from spreadsheets to an online database to store information doesn’t have to be a complex process. It’s about finding the right solution that offers flexibility, efficiency, and a scalable structure for your growing data needs.