Databases are a type of data storage system used to store and retrieve information. These systems are usually referenced by software programs and web pages, and they allow users to quickly access specific pieces of information.
There are multiple types of databases and each has its own unique features. This article will explore some of the most common types and their differences.
Relational
Relational databases organize data points into tables, with columns and rows. Each row contains information about a specific category of data, and each column holds the value for that data type.
Relationships are created between data in different tables by using the primary key and foreign keys, both of which are unique identifiers that link records from two or more tables together. This structure helps ensure the accuracy of the data and minimizes the possibility of duplication.
Another advantage of the relational model is that it provides consistent tagging for data records across multiple applications. This can save application developers time, as repetitive actions like inserting a tuple or querying data need not be repeated every time the application is run.
Relational database systems are also ACID compliant, meaning they follow the four crucial properties of atomicity, consistency, isolation, and durability for data transactions. This standard ensures that a bad query or change request doesn’t corrupt other data points, and that changes are permanent once they’re committed.
Graph
Graph databases are data structures that represent real-world problems using networks of nodes and edges. Graphs are used for real-life examples such as telephone networks, circuit networks, and social networks (like Facebook, LinkedIn, etc).
Unlike relational databases, graphs don’t have strict schemas; they model the relationships between data in the same way as the data itself. This makes them flexible and easy to alter over time without losing functionality.
Another benefit is that graph databases can scale more naturally to large datasets because they don’t depend on a rigid schema like relational database models do. Moreover, they are faster for associative data sets and map more directly to the structure of object-oriented applications.
Graph databases are available from several vendors, including Neo4j, a mature graph database that is open source and offers good performance along with the Cypher query language to make working with your data simple. Neo4j also offers cloud and self-hosted enterprise editions to support big data applications.
OLTP
An OLTP database system is an online transactional database that processes and returns the results of queries in near real-time. OLTP databases administer day-to-day transactions of an organization and are used for business analyses, including planning, budgeting, forecasting, data mining, etc.
OLTP systems focus on fast and effective query processing and maintaining data integrity in multi-access environments. The resulting database performance is measured by the number of transactions processed per second.
A typical OLTP architecture includes a presentation tier, business logic tier, and a data store tier. The business logic tier handles the authentication process and checks that all the information required for the transaction is available.
OLTP databases are optimized for inserting, updating, and deleting small amounts of data that can be easily accessed by the user. They also use a fully normalized schema for data consistency and faster response time. OLTP systems can be scalable for a large number of users. They can be backed up by multiple backups and are often distributed across different machines.
DBMS
A DBMS is system software that supports end users and application programmers to create, protect, read, update and delete information stored in a database. The most common DBMS is the relational model, which normalizes data into tables and joins them using SQL.
Various data structures are available for storing data in a database. These include key-value storage, document-oriented, graph and wide-column stores.
Each of these is a different type of data model and each presents a different external view. These external views may present a conceptual view, in which details of how the data are organized (internal level) are abstracted away.
The internal level (or physical level) deals with the logical and technical aspects of how the data are organized inside the DBMS. These concerns involve scalability, cost, performance and other operational issues.
A DBMS provides a level of abstraction between the conceptual schema, which defines how the data are organized in the database, and the physical schema, which describes the files, indexes and other mechanisms that store the data. This enables a DBA to modify the data structure without breaking the existing system.
