Database
Database is a server which store data is called as database.
Databases were first created in the 1960s. These early databases were network models where each record is related to many primary and secondary records
databases are used for storing, maintaining and accessing any sort of data. They collect information on people, places or things. That information is gathered in one place so that it can be observed and analyzed. Databases can be thought of as an organized collection of information.
Most modern applications use huge volumes of data of both kinds. The first kind of data is non-relational data or database examples of data stored in files, where such data is not uniquely related to the other data and typically has a string value. Relational data on the other hand is always related to the other data elements.
Types of Databases
There are many types of databases
Distributed databases:
a database that is not limited to one system, it is spread over different sites, on multiple computers or over a network of computers.
it use both the information locally captured and the common database. Such database systems do not store all data in one place. Rather it is distributed over several organizations and hence the name.
Relational databases:
Relational databases like the RDBMS- Relational Data Base Management System use and define the database relationships using a table form. Very popular for its storage, zero data redundancy and ease of retrieval, there are several RDBMS like MySQL, Microsoft’s SQL Server database, Oracle’s Oracle DB etc in use today.
All modern database management systems like SQL, MS SQL Server, IBM DB2, ORACLE, My-SQL and Microsoft Access are based on RDBMS.
Object-oriented databases:
The object-oriented database model (OODBM) is an alternative implementation to that of a relational model. An object-oriented database is similar in principle to an object-oriented programming language.
Such objects in the database have methods, defined rules and attributes instructing them on the query meaning in the database and what to do with the provided data. For Ex: PostgreSQL is an object-oriented RDBMS.
Centralized database:
A centralized database is a collection of information at a single location accessible from numerous points, in contrast with a distributed database where the information is spread out across multiple sites.
This multiple user database uses a centralized location to store data that can be accessed by users with varied backgrounds.
Open-source databases:
Open source databases store vital information in software which the organization can control. An open source database allows users to create a system based on their unique requirements and business needs. It is free and can also be shared.
For Ex: Applications in fields like marketing (SalesForce), HR applications etc.
Cloud databases:
The database itself can be offered as a SaaS (Software-as-a-Service) application or simply be hosted in a cloud-based virtual machine. Applications can then access all the data stored in a cloud database over a network from any device.
They provide many advantages like easy availability, paid bandwidth, storage capacity etc and are scalable on-demand. Ex: Security applications from Imperva.
Columnar databases:
A columnar database stores data by columns rather than by rows, which makes it suitable for analytical query processing, and thus for data warehouses.
hese types of databases are often used in data warehouses because they’re great at handling analytical queries. When you’re querying a columnar database, it essentially ignores all of the data that doesn’t apply to the query, because you can retrieve the information from only the columns you want.
Wide column databases:
Wide column databases, or column family databases, refers to a category of NoSQL databases that works well for storing enormous amounts of data that can be collected. Its architecture uses persistent, sparse matrix, multi-dimensional mapping.
Highly scalable, wide column databases can handle petabytes of data, making them ideal for supporting real-time big data applications.
Key-value databases:
Key-value pair stores are not a new concept and were already with us for the last few decades. One of the known stores is the old Windows Registry allowing the system/applications to store data in a “key-value” structure, where a key can be represented as a unique identifier or a unique path to the value.
Examples: Amazon DynamoDB, Redis.
Hierarchical databases:
This model structure allows the one-to-one and a one-to-many relationship between two/ various types of data. This structure is very helpful in describing many relationships in the real world; table of contents, any nested and sorted information.
The one-to-many format is rigid, so child records can’t have more than one parent record. Originally developed by IBM in the early 1960s, hierarchical databases are commonly used to support high-performance and high availability applications.
Document databases:
Document databases make it easier for developers to store and query data in a database by using the same document-model format they use in their application code.
Document databases are simple and scalable, making them useful for mobile apps that need fast iterations.
Examples: MongoDB, Amazon DocumentDB, Apache CouchDB
Graph databases:
Graph databases are a type of NoSQL database that are based on graph theory.
A graph database is defined as a specialized, single-purpose platform for creating and manipulating graphs. Graphs contain nodes, edges, and properties, all of which are used to represent and store data in a way that relational databases are not equipped to do.
Time series databases:
A time series database (TSDB) is a database optimized for time-stamped or time series data. Time series data are simply measurements or events that are tracked, monitored, downsampled, and aggregated over time.
Things sensors that are getting attached to everything put out a constant stream of time series data.
Examples: Druid, eXtremeDB, InfluxDB
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