Database Software - Compilation and Taxonomy

A software company will differentiate its commercial database software in a number of ways:

Database Types

Mainframe Legacy Datastores   Relational Database: Mainframe   Relational Database: Client/Server   Apache Hadoop     NoSQL Database   Cloud Multi-tenant Database   Workstation Database   Mobile Database

Database Tools	Analytical Tools

		File-AID/Db2, File Manager, and Toad		Reporting Transform and Load			Statistical Analysis		Business Intelligence
		Cross Platform Reporting		Hadoop Integration: Third Party			Data Mining Databases		Big Data Ecosystem

Not all databases have the identical capacity and featureset; a database may not work equally well in hybrid software environments with other commercial and open source database software. The design and architecture of a database will make it well suited and efficient within a range of volume, data management, transactions, and administrative functions. In most cases a commercial database will have an open source free counterpart. The major software companies typically will provide financing and support for open source or low cost entry level database software to promote its own strategic interests: hardware, software, and consultancy services. When organizational enterprise selects a database, it is an investment in infrastructure. The primary languages for relational database management systems are: ANSI SQL and ANSI SQL/PSM, IBM SQL and SQL/PL, Oracle SQL and PL/SQL, and Sybase and Microsoft TSQL. They provide an administration, naming, security, and application development framework.

Big Data may become an operational requirement for organizational enterprise; on-line access, analysis, off-line storage, and reporting. Advancements in both hardware and software technology have driven the adoption of cloud computing and social media content distribution and engagement. The result is an immense growth in the amount of new data collected. There are multiple formats and sources: sensors, e-commerce websites, social networks, and weblogs. Data integration has become essential to strategic information technology initiatives: modernization, master data management, and service oriented architecture. IBM, Microsoft, and Oracle increasingly are supporting Big Data - Apache Hadoop and NoSQL. There are other commercial and open source software companies developing tools for connecting and interfacing to components of the Hadoop ecosystem.

Cloud databases are automated multitenant services that present a database capability. They are used to provide on-demand scalability with a minimum of database administration.

Mobile database technology is used in a variety of applications and device environments such as MP3/MP4 players and CRM software on smartphones devices. They are distinguished by a small footprint for minimal memory and CPU efficiency. In-memory, on-disk, and hybrid data storage is used for optimizing the mobile device for speed and persistence. There is developer flexibility for managing the form factor presentation and cross operating system coding.

Document-oriented databases are different from relational databases. From a developer's perspective, schemaless document data is simpler and more flexible to manage. Rather than storing data into a rigid schema of tables, rows, and columns joined by relationships, documents are written individually, containing whatever data is required. Schemaless datastores for the most part do not support fully ACID: atomicity, consistency, isolation, and durability. It can present a challenge where reliability and consistency are important. Schemaless datastores tend to scale more easily than relational ones, making document-oriented storage a good option for web based applications. MongoDB and CouchDB are the leading documented-oriented databases.