Database Development Overview
Every business needs are different. Need to develop customized databases that exactly fit your requirements is a tough task. Storage of data and information can be the most important assets of an enterprise. In a model database development solution, the key importance is how the way data integrity, security and extensibility together with cost-effectiveness and maximum utility are approach for complete solutions. The database design should be centered on the business logic defined.
We have some of best database experts at Leonsoft, our team consist of enterprise level professionals who have years experience and depth that the best database companies choose us for their consulting assignments. Our personnel are highly specialized combined with rich functional expertise to offer clients a single window catering to all their needs with a view to offer clients a more focused delivery channel for Business Intelligence.
Leonsoft Technologies offers database solutions for all types of web , distributed , desktop applications. As database designer we have the expertise to undertake large scale database development, remote database administration, database maintenance, database hosting and various kinds of Database Solutions that drives software applications. If you have a logical business concept, then we can transform it into a database driven distributed or web application.
We have established a good network of good experienced in-house developers to assist you. Regardless of the size of your project, company, or budget, we are committed to achieving your complete satisfaction. Our team has built a solid track record and reputation for providing database development services effectively and efficiently. We specialize in database design, data conversion, integration and web development, distributed development, desktop development, data mining, so you won't lose your existing data
Our development team is geared to build software for every level of complexity. Be it a small project suited for a application to complex custom-made software packages used by various industries, nationals and international organizations.
All databases employed by us are rapidly scalable. Our Software engineers and programmers have extensive experience in database software development.Using well established and stable database-capable technologies such as Spring , Spring Mvc & Webflow , Java Server Faces , Tapestry , Web Services , Service Oriented Architectures Servlets, Jsp, Struts, Java Portlets, C++ Qt, RMI, EJB, JMS, CORBA, PHP, ASP, ASP.net, Visual Basic, VB.net, Jdbc, JDO , Hibernate, Oracle 7, 8, 8i, 9i & 10g , Microsoft SQL , MySQL , Sybase, DB2 , Ingres 2006, Postgre SQL 8.2.3, Firebird 2.0.1, SQLite, DaffodilDB, HSQLDB 184.108.40.206, Apache Derby, SmallSQL. our developers can create and integrate a database solution seamlessly with your applications.
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- Database :
A database is an organized collection of data. The data are typically organized to model relevant aspects of reality in a way that supports processes requiring this information. For example, modeling the availability of rooms in hotels in a way that supports finding a hotel with vacancies.
Database management systems (DBMSs) are specially designed applications that interact with the user, other applications, and the database itself to capture and analyze data. A general-purpose database management system (DBMS) is a software system designed to allow the definition, creation, querying, update, and administration of databases. Well-known DBMSs include MySQL, PostgreSQL, SQLite, Microsoft SQL Server, Microsoft Access, Oracle, SAP, dBASE, FoxPro, IBM DB2, LibreOffice Base and FileMaker Pro. A database is not generally portable across different DBMS, but different DBMSs can inter-operate by using standards such as SQL and ODBC or JDBC to allow a single application to work with more than one database.
Terminology and overview
Formally, the term "database" refers to the data itself and supporting data structures. Databases are created to operate large quantities of information by inputting, storing, retrieving, and managing that information. Databases are set up so that one set of software programs provides all users with access to all the data.
A "database management system" (DBMS) is a suite of computer software providing the interface between users and a database or databases. Because they are so closely related, the term "database" when used casually often refers to both a DBMS and the data it manipulates. Outside the world of professional information technology, the term database is sometimes used casually to refer to any collection of data (perhaps a spreadsheet, maybe even a card index). This article is concerned only with databases where the size and usage requirements necessitate use of a database management system.
The interactions catered for by most existing DBMS fall into four main groups:br/> Data definition. Defining new data structures for a database, removing data structures from the database, modifying the structure of existing data.
Update. Inserting, modifying, and deleting data.
Retrieval. Obtaining information either for end-user queries and reports or for processing by applications. Administration. Registering and monitoring users, enforcing data security, monitoring performance, maintaining data integrity, dealing with concurrency control, and recovering information if the system fails.
A DBMS is responsible for maintaining the integrity and security of stored data, and for recovering information if the system fails. Both a database and its DBMS conform to the principles of a particular database model. "Database system" refers collectively to the database model, database management system, and database.
Physically, database servers are dedicated computers that hold the actual databases and run only the DBMS and related software. Database servers are usually multiprocessor computers, with generous memory and RAID disk arrays used for stable storage. RAID is used for recovery of data if any of the disks fails. Hardware database accelerators, connected to one or more servers via a high-speed channel, are also used in large volume transaction processing environments. DBMSs are found at the heart of most database applications. DBMSs may be built around a custom multitasking kernel with built-in networking support, but modern DBMSs typically rely on a standard operating system to provide these functions. Since DBMSs comprise a significant economical market, computer and storage vendors often take into account DBMS requirements in their own development plans.
Databases and DBMSs can be categorized according to the database model(s) that they support (such as relational or XML), the type(s) of computer they run on (from a server cluster to a mobile phone), the query language(s) used to access the database (such as SQL or XQuery), and their internal engineering, which affects performance, scalability, resilience, and security.
Applications and roles
Most organizations in developed countries today depend on databases for their business operations. Increasingly, databases are not only used to support the internal operations of the organization, but also to underpin its online interactions with customers and suppliers (see Enterprise software). Databases are not used only to hold administrative information, but are often embedded within applications to hold more specialized data: for example engineering data or economic models. Examples of database applications include computerized library systems, flight reservation systems, and computerized parts inventory systems.
Client-server or transactional DBMSs are often complex to maintain high performance, availability and security when many users are querying and updating the database at the same time. Personal, desktop-based database systems tend to be less complex. For example, FileMaker and Microsoft Access come with built-in graphical user interfaces.
General-purpose and special-purpose DBMSs
A DBMS has evolved into a complex software system and its development typically requires thousands of person-years of development effort. Some general-purpose DBMSs such as Adabas, Oracle and DB2 have been undergoing upgrades since the 1970s. General-purpose DBMSs aim to meet the needs of as many applications as possible, which adds to the complexity. However, the fact that their development cost can be spread over a large number of users means that they are often the most cost-effective approach. However, a general-purpose DBMS is not always the optimal solution: in some cases a general-purpose DBMS may introduce unnecessary overhead. Therefore, there are many examples of systems that use special-purpose databases. A common example is an email system: email systems are designed to optimize the handling of email messages, and do not need significant portions of a general-purpose DBMS functionality.
Many databases have application software that accesses the database on behalf of end-users, without exposing the DBMS interface directly. Application programmers may use a wire protocol directly, or more likely through an application programming interface. Database designers and database administrators interact with the DBMS through dedicated interfaces to build and maintain the applications' databases, and thus need some more knowledge and understanding about how DBMSs operate and the DBMSs' external interfaces and tuning parameters.
General-purpose databases are usually developed by one organization or community of programmers, while a different group builds the applications that use it. In many companies, specialized database administrators maintain databases, run reports, and may work on code that runs on the databases themselves (rather than in the client application).
With the progress in technology in the areas of processors, computer memory, computer storage and computer networks, the sizes, capabilities, and performance of databases and their respective DBMSs have grown in orders of magnitudes.
The development of database technology can be divided into three eras based on data model or structure: navigational, SQL/relational, and post-relational. The two main early navigational data models were the hierarchical model, epitomized by IBM's IMS system, and the Codasyl model (Network model), implemented in a number of products such as IDMS.
The relational model, first proposed in 1970 by Edgar F. Codd, departed from this tradition by insisting that applications should search for data by content, rather than by following links. The relational model is made up of ledger-style tables, each used for a different type of entity. It was not until the mid-1980s that computing hardware became powerful enough to allow relational systems (DBMSs plus applications) to be widely deployed. By the early 1990s, however, relational systems were dominant for all large-scale data processing applications, and they remain dominant today (2013) except in niche areas. The dominant database language is the standard SQL for the relational model, which has influenced database languages for other data models.
Object databases were invented in the 1980s to overcome the inconvenience of object-relational impedance mismatch, which led to the coining of the term "post-relational" but also development of hybrid object-relational databases.
The next generation of post-relational databases in the 2000s became known as NoSQL databases, introducing fast key-value stores and document-oriented databases. A competing "next generation" known as NewSQL databases attempted new implementations that retained the relational/SQL model while aiming to match the high performance of NoSQL compared to commercially available relational DBMSs.
1960s Navigational DBMS
Basic structure of navigational CODASYL database model.
The introduction of the term database coincided with the availability of direct-access storage (disks and drums) from the mid-1960s onwards. The term represented a contrast with the tape-based systems of the past, allowing shared interactive use rather than daily batch processing. The Oxford English dictionary cites a 1962 report by the System Development Corporation of California as the first to use the term "data-base" in a specific technical sense.
As computers grew in speed and capability, a number of general-purpose database systems emerged; by the mid-1960s there were a number of such systems in commercial use. Interest in a standard began to grow, and Charles Bachman, author of one such product, the Integrated Data Store (IDS), founded the "Database Task Group" within CODASYL, the group responsible for the creation and standardization of COBOL. In 1971 they delivered their standard, which generally became known as the "Codasyl approach", and soon a number of commercial products based on this approach were made available.
The Codasyl approach was based on the "manual" navigation of a linked data set which was formed into a large network. Records could be found either by use of a primary key (known as a CALC key, typically implemented by hashing), by navigating relationships (called sets) from one record to another, or by scanning all the records in sequential order. Later systems added B-Trees to provide alternate access paths. Many Codasyl databases also added a query language that was very straightforward. However, in the final tally, CODASYL was very complex and required significant training and effort to produce useful applications.
IBM also had their own DBMS system in 1968, known as IMS. IMS was a development of software written for the Apollo program on the System/360. IMS was generally similar in concept to Codasyl, but used a strict hierarchy for its model of data navigation instead of Codasyl's network model. Both concepts later became known as navigational databases due to the way data was accessed, and Bachman's 1973 Turing Award presentation was The Programmer as Navigator. IMS is classified as a hierarchical database. IDMS and Cincom Systems' TOTAL database are classified as network databases.
1970s relational DBMS
Edgar Codd worked at IBM in San Jose, California, in one of their offshoot offices that was primarily involved in the development of hard disk systems. He was unhappy with the navigational model of the Codasyl approach, notably the lack of a "search" facility. In 1970, he wrote a number of papers that outlined a new approach to database construction that eventually culminated in the groundbreaking A Relational Model of Data for Large Shared Data Banks.
In this paper, he described a new system for storing and working with large databases. Instead of records being stored in some sort of linked list of free-form records as in Codasyl, Codd's idea was to use a "table" of fixed-length records, with each table used for a different type of entity. A linked-list system would be very inefficient when storing "sparse" databases where some of the data for any one record could be left empty. The relational model solved this by splitting the data into a series of normalized tables (or relations), with optional elements being moved out of the main table to where they would take up room only if needed. Data may be freely inserted, deleted and edited in these tables, with the DBMS doing whatever maintenance needed to present a table view to the application/user.
In the relational model, related records are linked together with a "key"
The relational model also allowed the content of the database to evolve without constant rewriting of links and pointers. The relational part comes from entities referencing other entities in what is known as one-to-many relationship, like a traditional hierarchical model, and many-to-many relationship, like a navigational (network) model. Thus, a relational model can express both hierarchical and navigational models, as well as its native tabular model, allowing for pure or combined modeling in terms of these three models, as the application requires.
For instance, a common use of a database system is to track information about users, their name, login information, various addresses and phone numbers. In the navigational approach all of these data would be placed in a single record, and unused items would simply not be placed in the database. In the relational approach, the data would be normalized into a user table, an address table and a phone number table (for instance). Records would be created in these optional tables only if the address or phone numbers were actually provided.
Linking the information back together is the key to this system. In the relational model, some bit of information was used as a "key", uniquely defining a particular record. When information was being collected about a user, information stored in the optional tables would be found by searching for this key. For instance, if the login name of a user is unique, addresses and phone numbers for that user would be recorded with the login name as its key. This simple "re-linking" of related data back into a single collection is something that traditional computer languages are not designed for.
Just as the navigational approach would require programs to loop in order to collect records, the relational approach would require loops to collect information about any one record. Codd's solution to the necessary looping was a set-oriented language, a suggestion that would later spawn the ubiquitous SQL. Using a branch of mathematics known as tuple calculus, he demonstrated that such a system could support all the operations of normal databases (inserting, updating etc.) as well as providing a simple system for finding and returning sets of data in a single operation.
Codd's paper was picked up by two people at Berkeley, Eugene Wong and Michael Stonebraker. They started a project known as INGRES using funding that had already been allocated for a geographical database project and student programmers to produce code. Beginning in 1973, INGRES delivered its first test products which were generally ready for widespread use in 1979. INGRES was similar to System R in a number of ways, including the use of a "language" for data access, known as QUEL. Over time, INGRES moved to the emerging SQL standard.
IBM itself did one test implementation of the relational model, PRTV, and a production one, Business System 12, both now discontinued. Honeywell wrote MRDS for Multics, and now there are two new implementations: Alphora Dataphor and Rel. Most other DBMS implementations usually called relational are actually SQL DBMSs.
In 1970, the University of Michigan began development of the MICRO Information Management System based on D.L. Childs' Set-Theoretic Data model. Micro was used to manage very large data sets by the US Department of Labor, the U.S. Environmental Protection Agency, and researchers from the University of Alberta, the University of Michigan, and Wayne State University. It ran on IBM mainframe computers using the Michigan Terminal System. The system remained in production until 1998.
Database machines and appliances
In the 1970s and 1980s attempts were made to build database systems with integrated hardware and software. The underlying philosophy was that such integration would provide higher performance at lower cost. Examples were IBM System/38, the early offering of Teradata, and the Britton Lee, Inc. database machine.
Another approach to hardware support for database management was ICL's CAFS accelerator, a hardware disk controller with programmable search capabilities. In the long term, these efforts were generally unsuccessful because specialized database machines could not keep pace with the rapid development and progress of general-purpose computers. Thus most database systems nowadays are software systems running on general-purpose hardware, using general-purpose computer data storage. However this idea is still pursued for certain applications by some companies like Netezza and Oracle (Exadata).
Late-1970s SQL DBMS
IBM started working on a prototype system loosely based on Codd's concepts as System R in the early 1970s. The first version was ready in 1974/5, and work then started on multi-table systems in which the data could be split so that all of the data for a record (some of which is optional) did not have to be stored in a single large "chunk". Subsequent multi-user versions were tested by customers in 1978 and 1979, by which time a standardized query language - SQL - had been added. Codd's ideas were establishing themselves as both workable and superior to Codasyl, pushing IBM to develop a true production version of System R, known as SQL/DS, and, later, Database 2 (DB2).
Larry Ellison's Oracle started from a different chain, based on IBM's papers on System R, and beat IBM to market when the first version was released in 1978.
Stonebraker went on to apply the lessons from INGRES to develop a new database, Postgres, which is now known as PostgreSQL. PostgreSQL is often used for global mission critical applications (the .org and .info domain name registries use it as their primary data store, as do many large companies and financial institutions).
In Sweden, Codd's paper was also read and Mimer SQL was developed from the mid-70s at Uppsala University. In 1984, this project was consolidated into an independent enterprise. In the early 1980s, Mimer introduced transaction handling for high robustness in applications, an idea that was subsequently implemented on most other DBMS.
Another data model, the entity-relationship model, emerged in 1976 and gained popularity for database design as it emphasized a more familiar description than the earlier relational model. Later on, entity-relationship constructs were retrofitted as a data modeling construct for the relational model, and the difference between the two have become irrelevant.
1980s desktop databases
The 1980s ushered in the age of desktop computing. The new computers empowered their users with spreadsheets like Lotus 1,2,3 and database software like dBASE. The dBASE product was lightweight and easy for any computer user to understand out of the box. C. Wayne Ratliff the creator of dBASE stated: dBASE was different from programs like BASIC, C, FORTRAN, and COBOL in that a lot of the dirty work had already been done. The data manipulation is done by dBASE instead of by the user, so the user can concentrate on what he is doing, rather than having to mess with the dirty details of opening, reading, and closing files, and managing space allocation. dBASE was one of the top selling software titles in the 1980s and early 1990�s.
1980s object-oriented databases
The 1980s, along with a rise in object oriented programming, saw a growth in how data in various databases were handled. Programmers and designers began to treat the data in their databases as objects. That is to say that if a person's data were in a database, that person's attributes, such as their address, phone number, and age, were now considered to belong to that person instead of being extraneous data. This allows for relations between data to be relations to objects and their attributes and not to individual fields. The term "object-relational impedance mismatch" described the inconvenience of translating between programmed objects and database tables. Object databases and object-relational databases attempt to solve this problem by providing an object-oriented language (sometimes as extensions to SQL) that programmers can use as alternative to purely relational SQL. On the programming side, libraries known as object-relational mappings (ORMs) attempt to solve the same problem.
2000s NoSQL and NewSQL databases
The next generation of post-relational databases in the 2000s became known as NoSQL databases, including fast key-value stores and document-oriented databases. XML databases are a type of structured document-oriented database that allows querying based on XML document attributes.
NoSQL databases are often very fast, do not require fixed table schemas, avoid join operations by storing denormalized data, and are designed to scale horizontally.
In recent years there was a high demand for massively distributed databases with high partition tolerance but according to the CAP theorem it is impossible for a distributed system to simultaneously provide consistency, availability and partition tolerance guarantees. A distributed system can satisfy any two of these guarantees at the same time, but not all three. For that reason many NoSQL databases are using what is called eventual consistency to provide both availability and partition tolerance guarantees with a maximum level of data consistency.
The most popular NoSQL systems include: MongoDB, Riak, Oracle NoSQL Database, memcached, Redis, CouchDB, Hazelcast, Apache Cassandra and HBase, note that all are open-source software products.
A number of new relational databases continuing use of SQL but aiming for performance comparable to NoSQL are known as NewSQL.
Database technology has been an active research topic since the 1960s, both in academia and in the research and development groups of companies (for example IBM Research). Research activity includes theory and development of prototypes. Notable research topics have included models, the atomic transaction concept and related concurrency control techniques, query languages and query optimization methods, RAID, and more.
The database research area has several dedicated academic journals (for example, ACM Transactions on Database Systems-TODS, Data and Knowledge Engineering-DKE) and annual conferences (e.g., ACM SIGMOD, ACM PODS, VLDB, IEEE ICDE).
Database type examples
One way to classify databases involves the type of their contents, for example: bibliographic, document-text, statistical, or multimedia objects. Another way is by their application area, for example: accounting, music compositions, movies, banking, manufacturing, or insurance. A third way is by some technical aspect, such as the database structure or interface type. This section lists a few of the adjectives used to characterize different kinds of databases.
An in-memory database is a database that primarily resides in main memory, but is typically backed-up by non-volatile computer data storage. Main memory databases are faster than disk databases, and so are often used where response time is critical, such as in telecommunications network equipment.SAP HANA platform is a very hot topic for in-memory database. By May 2012, HANA was able to run on servers with 100TB main memory powered by IBM. The co founder of the company claimed that the system was big enough to run the 8 largest SAP customers.
An active database includes an event-driven architecture which can respond to conditions both inside and outside the database. Possible uses include security monitoring, alerting, statistics gathering and authorization. Many databases provide active database features in the form of database triggers.
A cloud database relies on cloud technology. Both the database and most of its DBMS reside remotely, "in the cloud," while its applications are both developed by programmers and later maintained and utilized by (application's) end-users through a web browser and Open APIs.
Data warehouses archive data from operational databases and often from external sources such as market research firms. The warehouse becomes the central source of data for use by managers and other end-users who may not have access to operational data. For example, sales data might be aggregated to weekly totals and converted from internal product codes to use UPCs so that they can be compared with ACNielsen data. Some basic and essential components of data warehousing include retrieving, analyzing, and mining data, transforming, loading and managing data so as to make them available for further use.
- A deductive database combines logic programming with a relational database, for example by using the Datalog language.
- A distributed database is one in which both the data and the DBMS span multiple computers.
- A document-oriented database is designed for storing, retrieving, and managing document-oriented, or semi structured data, information. Document-oriented databases are one of the main categories of NoSQL databases.
- An embedded database system is a DBMS which is tightly integrated with an application software that requires access to stored data in such a way that the DBMS is hidden from the application�s end-users and requires little or no ongoing maintenance.
- End-user databases consist of data developed by individual end-users. Examples of these are collections of documents, spreadsheets, presentations, multimedia, and other files. Several products exist to support such databases. Some of them are much simpler than full fledged DBMSs, with more elementary DBMS functionality.
- A federated database system comprises several distinct databases, each with its own DBMS. It is handled as a single database by a federated database management system (FDBMS), which transparently integrates multiple autonomous DBMSs, possibly of different types (in which case it would also be a heterogeneous database system), and provides them with an integrated conceptual view.
- Sometimes the term multi-database is used as a synonym to federated database, though it may refer to a less integrated (e.g., without an FDBMS and a managed integrated schema) group of databases that cooperate in a single application. In this case typically middleware is used for distribution, which typically includes an atomic commit protocol (ACP), e.g., the two-phase commit protocol, to allow distributed (global) transactions across the participating databases.
- A graph database is a kind of NoSQL database that uses graph structures with nodes, edges, and properties to represent and store information. General graph databases that can store any graph are distinct from specialized graph databases such as triplestores and network databases. In a hypertext or hypermedia database, any word or a piece of text representing an object, e.g., another piece of text, an article, a picture, or a film, can be hyperlinked to that object. Hypertext databases are particularly useful for organizing large amounts of disparate information. For example, they are useful for organizing online encyclopedias, where users can conveniently jump around the text. The World Wide Web is thus a large distributed hypertext database.
- A knowledge base (abbreviated KB, kb or ?) is a special kind of database for knowledge management, providing the means for the computerized collection, organization, and retrieval of knowledge. Also a collection of data representing problems with their solutions and related experiences.
- A mobile database can be carried on or synchronized from a mobile computing device.
Operational databases store detailed data about the operations of an organization. They typically process relatively high volumes of updates using transactions. Examples include customer databases that record contact, credit, and demographic information about a business' customers, personnel databases that hold information such as salary, benefits, skills data about employees, enterprise resource planning systems that record details about product components, parts inventory, and financial databases that keep track of the organization's money, accounting and financial dealings.
A parallel database seeks to improve performance through parallelization for tasks such as loading data, building indexes and evaluating queries.
The major parallel DBMS architectures which are induced by the underlying hardware architecture are:
Shared memory architecture, where multiple processors share the main memory space, as well as other data storage.
Shared disk architecture, where each processing unit (typically consisting of multiple processors) has its own main memory, but all units share the other storage.
Shared nothing architecture, where each processing unit has its own main memory and other storage. Probabilistic databases employ fuzzy logic to draw inferences from imprecise data.
Real-time databases process transactions fast enough for the result to come back and be acted on right away. A spatial database can store the data with multidimensional features. The queries on such data include location based queries, like "Where is the closest hotel in my area?".
A temporal database has built-in time aspects, for example a temporal data model and a temporal version of SQL. More specifically the temporal aspects usually include valid-time and transaction-time.
A terminology-oriented database builds upon an object-oriented database, often customized for a specific field.
An unstructured data database is intended to store in a manageable and protected way diverse objects that do not fit naturally and conveniently in common databases. It may include email messages, documents, journals, multimedia objects, etc. The name may be misleading since some objects can be highly structured. However, the entire possible object collection does not fit into a predefined structured framework. Most established DBMSs now support unstructured data in various ways, and new dedicated DBMSs are emerging.
a simple example of DBMS is the banking system which can understand by everyone easily.
Database design and modeling
The first task of a database designer is to produce a conceptual data model that reflects the structure of the information to be held in the database. A common approach to this is to develop an entity-relationship model, often with the aid of drawing tools. Another popular approach is the Unified Modeling Language. A successful data model will accurately reflect the possible state of the external world being modeled: for example, if people can have more than one phone number, it will allow this information to be captured. Designing a good conceptual data model requires a good understanding of the application domain; it typically involves asking deep questions about the things of interest to an organisation, like "can a customer also be a supplier?", or "if a product is sold with two different forms of packaging, are those the same product or different products?", or "if a plane flies from New York to Dubai via Frankfurt, is that one flight or two (or maybe even three)?". The answers to these questions establish definitions of the terminology used for entities (customers, products, flights, flight segments) and their relationships and attributes.
Producing the conceptual data model sometimes involves input from business processes, or the analysis of workflow in the organization. This can help to establish what information is needed in the database, and what can be left out. For example, it can help when deciding whether the database needs to hold historic data as well as current data.
Having produced a conceptual data model that users are happy with, the next stage is to translate this into a schema that implements the relevant data structures within the database. This process is often called logical database design, and the output is a logical data model expressed in the form of a schema. Whereas the conceptual data model is (in theory at least) independent of the choice of database technology, the logical data model will be expressed in terms of a particular database model supported by the chosen DBMS. (The terms data model and database model are often used interchangeably, but in this article we use data model for the design of a specific database, and database model for the modelling notation used to express that design.)
The most popular database model for general-purpose databases is the relational model, or more precisely, the relational model as represented by the SQL language. The process of creating a logical database design using this model uses a methodical approach known as normalization. The goal of normalization is to ensure that each elementary "fact" is only recorded in one place, so that insertions, updates, and deletions automatically maintain consistency.
The final stage of database design is to make the decisions that affect performance, scalability, recovery, security, and the like. This is often called physical database design. A key goal during this stage is data independence, meaning that the decisions made for performance optimization purposes should be invisible to end-users and applications. Physical design is driven mainly by performance requirements, and requires a good knowledge of the expected workload and access patterns, and a deep understanding of the features offered by the chosen DBMS.
Another aspect of physical database design is security. It involves both defining access control to database objects as well as defining security levels and methods for the data itself.
Collage of five types of database models.
A database model is a type of data model that determines the logical structure of a database and fundamentally determines in which manner data can be stored, organized, and manipulated. The most popular example of a database model is the relational model (or the SQL approximation of relational), which uses a table-based format.
- Common logical data models for databases include:
- Hierarchical database model
- Network model
- Relational model
- Entity - relationship model
- Enhanced entity - relationship model
- Object model
- Document model
- Entity - attribute - value model
- Star schema
- An object-relational database combines the two related structures.
- Physical data models include:
- Inverted index
- Flat file
- Other models include:
- Associative model
- Multidimensional model
- Multivalue model
- Semantic model
- XML database
- Named graph
External, conceptual, and internal views
A database management system provides three views of the database data:
The external level defines how each group of end-users sees the organization of data in the database. A single database can have any number of views at the external level.
The conceptual level unifies the various external views into a coherent global view. It provides the synthesis of all the external views. It is out of the scope of the various database end-users, and is rather of interest to database application developers and database administrators.
The internal level (or physical level) is the internal organization of data inside a DBMS (see Implementation section below). It is concerned with cost, performance, scalability and other operational matters. It deals with storage layout of the data, using storage structures such as indexes to enhance performance. Occasionally it stores data of individual views (materialized views), computed from generic data, if performance justification exists for such redundancy. It balances all the external views' performance requirements, possibly conflicting, in an attempt to optimize overall performance across all activities.
While there is typically only one conceptual (or logical) and physical (or internal) view of the data, there can be any number of different external views. This allows users to see database information in a more business-related way rather than from a technical, processing viewpoint. For example, a financial department of a company needs the payment details of all employees as part of the company's expenses, but does not need details about employees that are the interest of the human resources department. Thus different departments need different views of the company's database.
The three-level database architecture relates to the concept of data independence which was one of the major initial driving forces of the relational model. The idea is that changes made at a certain level do not affect the view at a higher level. For example, changes in the internal level do not affect application programs written using conceptual level interfaces, which reduces the impact of making physical changes to improve performance.
The conceptual view provides a level of indirection between internal and external. On one hand it provides a common view of the database, independent of different external view structures, and on the other hand it abstracts away details of how the data is stored or managed (internal level). In principle every level, and even every external view, can be presented by a different data model. In practice usually a given DBMS uses the same data model for both the external and the conceptual levels (e.g., relational model). The internal level, which is hidden inside the DBMS and depends on its implementation (see Implementation section below), requires a different level of detail and uses its own types of data structure types.
Separating the external, conceptual and internal levels was a major feature of the relational database model implementations that dominate 21st century databases.
- Database languages are special-purpose languages, which do one or more of the following:
- Data definition language - defines data types and the relationships among them
- Data manipulation language - performs tasks such as inserting, updating, or deleting data occurrences
- Query language - allows searching for information and computing derived information
- Database languages are specific to a particular data model. Notable examples include:
- SQL combines the roles of data definition, data manipulation, and query in a single language. It was one of the first commercial languages for the relational model, although it departs in some respects from the relational model as described by Codd (for example, the rows and columns of a table can be ordered). SQL became a standard of the American National Standards Institute (ANSI) in 1986, and of the International Organization for Standards (ISO) in 1987. The standards have been regularly enhanced since and is supported (with varying degrees of conformance) by all mainstream commercial relational DBMSs.
- OQL is an object model language standard (from the Object Data Management Group). It has influenced the design of some of the newer query languages like JDOQL and EJB QL.
- XQuery is a standard XML query language implemented by XML database systems such as MarkLogic and eXist, by relational databases with XML capability such as Oracle and DB2, and also by in-memory XML processors such as Saxon.
- SQL/XML combines XQuery with SQL.
- A database language may also incorporate features like:
- DBMS-specific Configuration and storage engine management
- Computations to modify query results, like counting, summing, averaging, sorting, grouping, and cross-referencing
- Constraint enforcement (e.g. in an automotive database, only allowing one engine type per car)
- Application programming interface version of the query language, for programmer convenience
Performance, security, and availability
Because of the critical importance of database technology to the smooth running of an enterprise, database systems include complex mechanisms to deliver the required performance, security, and availability, and allow database administrators to control the use of these features.
Database storage is the container of the physical materialization of a database. It comprises the internal (physical) level in the database architecture. It also contains all the information needed (e.g., metadata, "data about the data", and internal data structures) to reconstruct the conceptual level and external level from the internal level when needed. Putting data into permanent storage is generally the responsibility of the database engine a.k.a. "storage engine". Though typically accessed by a DBMS through the underlying operating system (and often utilizing the operating systems' file systems as intermediates for storage layout), storage properties and configuration setting are extremely important for the efficient operation of the DBMS, and thus are closely maintained by database administrators. A DBMS, while in operation, always has its database residing in several types of storage (e.g., memory and external storage). The database data and the additional needed information, possibly in very large amounts, are coded into bits. Data typically reside in the storage in structures that look completely different from the way the data look in the conceptual and external levels, but in ways that attempt to optimize (the best possible) these levels' reconstruction when needed by users and programs, as well as for computing additional types of needed information from the data (e.g., when querying the database).
Some DBMS support specifying which character encoding was used to store data, so multiple encodings can be used in the same database. Various low-level database storage structures are used by the storage engine to serialize the data model so it can be written to the medium of choice. Techniques such as indexing may be used to improve performance. Conventional storage is row-oriented, but there are also column-oriented and correlation databases.
Database materialized views
Often storage redundancy is employed to increase performance. A common example is storing materialized views, which consist of frequently needed external views or query results. Storing such views saves the expensive computing of them each time they are needed. The downsides of materialized views are the overhead incurred when updating them to keep them synchronized with their original updated database data, and the cost of storage redundancy.
Database and database object replication
Occasionally a database employs storage redundancy by database objects replication (with one or more copies) to increase data availability (both to improve performance of simultaneous multiple end-user accesses to a same database object, and to provide resiliency in a case of partial failure of a distributed database). Updates of a replicated object need to be synchronized across the object copies. In many cases the entire database is replicated.
The following text needs to be harmonized with text in Database security.
Database security deals with all various aspects of protecting the database content, its owners, and its users. It ranges from protection from intentional unauthorized database uses to unintentional database accesses by unauthorized entities (e.g., a person or a computer program).
Database access control deals with controlling who (a person or a certain computer program) is allowed to access what information in the database. The information may comprise specific database objects (e.g., record types, specific records, data structures), certain computations over certain objects (e.g., query types, or specific queries), or utilizing specific access paths to the former (e.g., using specific indexes or other data structures to access information). Database access controls are set by special authorized (by the database owner) personnel that uses dedicated protected security DBMS interfaces.
This may be managed directly on an individual basis, or by the assignment of individuals and privileges to groups, or (in the most elaborate models) through the assignment of individuals and groups to roles which are then granted entitlements. Data security prevents unauthorized users from viewing or updating the database. Using passwords, users are allowed access to the entire database or subsets of it called "subschemas". For example, an employee database can contain all the data about an individual employee, but one group of users may be authorized to view only payroll data, while others are allowed access to only work history and medical data. If the DBMS provides a way to interactively enter and update the database, as well as interrogate it, this capability allows for managing personal databases.
Data security in general deals with protecting specific chunks of data, both physically (i.e., from corruption, or destruction, or removal; e.g., see physical security), or the interpretation of them, or parts of them to meaningful information (e.g., by looking at the strings of bits that they comprise, concluding specific valid credit-card numbers; e.g., see data encryption).
Change and access logging records who accessed which attributes, what was changed, and when it was changed. Logging services allow for a forensic database audit later by keeping a record of access occurrences and changes. Sometimes application-level code is used to record changes rather than leaving this to the database. Monitoring can be set up to attempt to detect security breaches.
Transactions and concurrency
Database transactions can be used to introduce some level of fault tolerance and data integrity after recovery from a crash. A database transaction is a unit of work, typically encapsulating a number of operations over a database (e.g., reading a database object, writing, acquiring lock, etc.), an abstraction supported in database and also other systems. Each transaction has well defined boundaries in terms of which program/code executions are included in that transaction (determined by the transaction's programmer via special transaction commands).
The acronym ACID describes some ideal properties of a database transaction: Atomicity, Consistency, Isolation, and Durability. Further information: Concurrency control
A database built with one DBMS is not portable to another DBMS (i.e., the other DBMS cannot run it). However, in some situations it is desirable to move, migrate a database from one DBMS to another. The reasons are primarily economical (different DBMSs may have different total costs of ownership or TCOs), functional, and operational (different DBMSs may have different capabilities). The migration involves the database's transformation from one DBMS type to another. The transformation should maintain (if possible) the database related application (i.e., all related application programs) intact. Thus, the database's conceptual and external architectural levels should be maintained in the transformation. It may be desired that also some aspects of the architecture internal level are maintained. A complex or large database migration may be a complicated and costly (one-time) project by itself, which should be factored into the decision to migrate. This in spite of the fact that tools may exist to help migration between specific DBMS. Typically a DBMS vendor provides tools to help importing databases from other popular DBMSs.
Database building, maintaining, and tuning
After designing a database for an application arrives the stage of building the database. Typically an appropriate general-purpose DBMS can be selected to be utilized for this purpose. A DBMS provides the needed user interfaces to be utilized by database administrators to define the needed application's data structures within the DBMS's respective data model. Other user interfaces are used to select needed DBMS parameters (like security related, storage allocation parameters, etc.).
When the database is ready (all its data structures and other needed components are defined) it is typically populated with initial application's data (database initialization, which is typically a distinct project; in many cases using specialized DBMS interfaces that support bulk insertion) before making it operational. In some cases the database becomes operational while empty from application's data, and data are accumulated along its operation.
After completing building the database and making it operational arrives the database maintenance stage: Various database parameters may need changes and tuning for better performance, application's data structures may be changed or added, new related application programs may be written to add to the application's functionality, etc. Contribution by Malebye Joyce as adapted from informations systems for businesses from chapter 5 - storing ad organizing data. Databases are often confused with spread sheet such as Microsoft excel which is different from Microsoft access. Both can be used to store information,however a database serves a better function at this. Below is a comparison of spreadsheets and databases. Spread sheets strengths -1. Very simple data storage 2. Relatively easy to use 3. Require less planning Weaknesses- 1. Data integrity problems, include inaccurate,inconsistent and out of date version and out of date data. 2. Formulas could be incorrect Databases strengths 1. Methods for keeping data up to date and consistent 2. Data is of higher quality than data stored in spreadsheets 3. Good for storing and organizing information. Weakness 1. Require more planning and designing
Backup and restore
Sometimes it is desired to bring a database back to a previous state (for many reasons, e.g., cases when the database is found corrupted due to a software error, or if it has been updated with erroneous data). To achieve this a backup operation is done occasionally or continuously, where each desired database state (i.e., the values of its data and their embedding in database's data structures) is kept within dedicated backup files (many techniques exist to do this effectively). When this state is needed, i.e., when it is decided by a database administrator to bring the database back to this state (e.g., by specifying this state by a desired point in time when the database was in this state), these files are utilized to restore that state.
Other DBMS features might include:
Graphics component for producing graphs and charts, especially in a data warehouse system
Query optimizer - Performs query optimization on every query to choose for it the most efficient query plan (a partial order (tree) of operations) to be executed to compute the query result. May be specific to a particular storage engine.
Tools or hooks for database design, application programming, application program maintenance, database performance analysis and monitoring, database configuration monitoring, DBMS hardware configuration (a DBMS and related database may span computers, networks, and storage units) and related database mapping (especially for a distributed DBMS), storage allocation and database layout monitoring, storage migration, etc.
- In-memory database :
An in-memory database (IMDB; also main memory database system or MMDB or memory resident database) is a database management system that primarily relies on main memory for computer data storage. It is contrasted with database management systems which employ a disk storage mechanism. Main memory databases are faster than disk-optimized databases since the internal optimization algorithms are simpler and execute fewer CPU instructions. Accessing data in memory eliminates seek time when querying the data, which provides faster and more predictable performance than disk.
In applications where response time is critical, such as telecommunications network equipment and mobile advertising networks, main memory databases are often used. IMDBs have gained a lot of traction, especially in the data analytics space, starting mid-2000s mainly due to cheaper RAM.
With the introduction of NVDIMM technology , in-memory databases will now be able to run at full speed and maintain data in the event of power failure.
In their simplest form, main memory databases store data on volatile memory devices. These devices lose all stored information when the device loses power or is reset. In this case, MMDBs can be said to lack support for the durability portion of the ACID (atomicity, consistency, isolation, durability) properties. Volatile memory-based MMDBs can, and often do, support the other three ACID properties of atomicity, consistency and isolation. However, since Non-Volatile DIMMs (NVDIMM) have become available, most future IMDB or MMDB installations will indeed have the durability to meet full ACID support.
Many MMDBs have added durability via the following mechanisms:
Snapshot files, or, checkpoint images, which record the state of the database at a given moment in time. These are typically generated periodically, or, at least when the MMDB does a controlled shut-down. While they give a measure of persistence to the data (in that not everything is lost in the case of a system crash) they only offer partial durability (as 'recent' changes will be lost). For full durability, they will need to be supplemented by one of the following:
Transaction logging, which records changes to the database in a journal file and facilitates automatic recovery of an in-memory database. Non-Volatile DIMM (NVDIMM), a memory module that has a DRAM interface, often combined with NAND flash for the Non-Volatile data security. The first NVDIMM solutions were designed with supercapacitors instead of batteries for the backup power source. With this storage, MMDB or IMDB can resume securely from its state upon reboot.
Non-volatile random access memory (NVRAM), usually in the form of static RAM backed up with battery power (battery RAM), or an electrically erasable programmable ROM (EEPROM). With this storage, the MMDB system can recover the data store from its last consistent state upon reboot.
High availability implementations that rely on database replication, with automatic failover to an identical standby database in the event of primary database failure. To protect against loss of data in the case of a complete system crash, replication of a MMDB is normally used in conjunction with one or more of the mechanisms listed above.
Some MMDBs allow the database schema to specify different durability requirements for selected areas of the database - thus, faster-changing data that can easily be regenerated or that has no meaning after a system shut-down would not need to be journaled for durability (though it would have to be replicated for high availability), whereas configuration information would be flagged as needing preservation.
Hybrids with on-disk databases
The first database engine to support both in-memory and on-disk tables in a single database was WebDNA: it was released in 1995. The advantage to this approach is flexibility: the developer can strike a balance between performance (which is enhanced by sorting, storing and retrieving specified data entirely in memory, rather than going to disk); cost, because a less costly hard disk can be substituted for more memory; persistence; and form factor, because RAM chips cannot approach the density of a small hard drive.
Manufacturing efficiency is another reason a combined in-memory/on-disk database system may be chosen. Some device product lines, especially in consumer electronics, include some units with permanent storage, and others that rely on memory for storage (set-top boxes, for example). If such devices require a database system, a manufacturer can adopt a hybrid database system at lower and upper cost, and with less code customization, than using separate in-memory and on-disk databases, respectively, for its disk-less and disk-based products.
Another variation is to have large amounts of nonvolatile memory in the server. For example Flash memory chips as addressable memory rather than structured as disk arrays. A database in this form of memory combines very fast access speed with persistence over reboots and power losses.
- EXASolution EXASOL
- Oracle Exalytics
- H2 (DBMS)
- Oracle Coherence
- SAP HANA
Datablitz (formerly Dali) Bell Labs (Alcatel-Lucent) Proprietary Dali prototype was developed for in-house Bell Labs needs Polyhedra ENEA AB (previously Perihelion Software) Proprietary, with a free to use edition (Polyhedra Lite) Relational (SQL, ODBC, JDBC) in-memory database system originally developed for use in SCADA and embedded systems, but used in a variety of other applications including financial systems. Supports data durability via snapshots and journal logging, and high availability via a hot-standby. First release was in 1993; 8.7 released in March 2013. Polyhedra Lite was released under a free-to-use license in 2012.
Kognitio Analytical Platform Kognitio, Limited Proprietary Development of an in-memory database, specialized for analytical workloads was started at White Cross Systems, Limited in 1988. The first beta release of that system was in 1989. It was based on the INMOS Transputer. The first full production release was offered in 1992. White Cross merged in 2005 with Kognitio, Limited in the United Kingdom and is currently marketing version 8 of the same code base as the "Kognitio Analytical Platform".
- Ehcache Terracotta, Inc. (Software AG) Open source (Apache License) For Java, distributed
- TimesTen Oracle Corporation Proprietary Standalone database or in-memory cache for Oracle Database
- ALTIBASE HDB Altibase Corporation Proprietary "Hybrid DBMS" that combines an in-memory database with a conventional disk-resident
- Microsoft COM+ IMDB Microsoft Corporation Proprietary Defunct
- eXtremeDB McObject LLC Proprietary Variety of editions
- solidDB IBM Proprietary Relational
- VoltDB VoltDB Inc. Open source (GPL) / Proprietary Relational; implements H-Store design
- BigMemory Terracotta, Inc. (Software AG) Proprietary (free editions)
- Xeround Xeround Inc. Proprietary/Not for sale, service only Cloud database
- SQLFire VMware Proprietary Relational, distributed, NewSQL
- ActiveSpaces TIBCO Software Proprietary with developer download For Java/.Net./C, distributed, hybrid, event enabled, NewSQL
- Microsoft SQL Server Microsoft Proprietary SQL Server 2012 contains an in-memory technology called xVelocity column store indexes targeted for data warehouse workload. The recently announced SQL Server 2014 will contain a in-memory technology with the code name Hekaton targeted for OLTP type workloads.
UnQLite Embedded Database Symisc Systems BSD, SPL UnQLite has support for in-memory databases as well on-disk databases using the same API with pluggable run-time interchangeable storage engines (B+tree, Hash, etc.)
Many DBMS support in-memory-only storage engines, including:
- Adaptive Server Enterprise
- Embedded Database :
An embedded database system is a database management system (DBMS) which is tightly integrated with an application software that requires access to stored data, such that the database system is "hidden" from the application�s end-user and requires little or no ongoing maintenance. It is actually a broad technology category that includes database systems with differing application programming interfaces (SQL as well as proprietary, native APIs); database architectures (client-server and in-process); storage modes (on-disk, in-memory and combined); database models (relational, object-oriented, entity-attribute-value model and network/CODASYL); and target markets.
The term embedded database can be confusing because only a small subset of embedded database products is used in real-time embedded systems such as telecommunications switches and consumer electronics devices.
Accuracer Database System
Accuracer Database System from AidAim Software is a compact, embedded, single-file, multi-user (file-server and client-server) x86 and x64 Microsoft Windows and Linux cross-platform DBMS with SQL database engine released as independent packages for different IDEs and ODBC API as a DLL for Windows. Accuracer Database System supports Embarcadero Delphi, C++Builder, and Borland Kylix IDEs from the old versions to the newest ones. Accuracer Database System has Bold for Delphi support, a number of modules for visual query building, Manager, SQLConsole, as well as other utilities with full source code, and Accuracer Database Server, a server application for Microsoft Windows. Accuracer Database System provides wide set of data compression and encryption modes for network traffic in client-server mode as well as for a database file. Database file in Accuracer format can be compiled into EXE and accessed in read-only mode. All Accuracer Database System products, commercial and free, are released under royalty-free licenses.
Advantage Database Server
Sybase's Advantage Database Server (ADS) is a full-featured embedded database management system. It provides both ISAM and relational data access and is compatible with multiple platforms including Windows, Linux, and Netware. It is available as a royalty-free local file-server database or a full client-server version. ADS has been around for many years and is highly scalable, with no administration, and has support for a variety of IDEs including .NET Framework (.NET), Object Pascal (Delphi), Visual FoxPro (FoxPro), PHP, Visual Basic (VB), Visual Objects (VO), Vulcan, Clipper, Perl, Java, xHarbour, etc.
Derby is an embeddable SQL engine written entirely in Java. Fully transactional, multi-user with a decent SQL subset, Derby is a mature engine and freely available under the Apache license and is actively maintained. Derby project page. It is also distributed as part of Oracle's Java SE Development Kit (JDK) under the name of Java DB.
C#-SQLite is a port of the SQLite embedded SQL database engine, versionn 220.127.116.11, from the original native C into fully managed C#. The port is complete aside from some minor optional features of SQLite. csharp-sqlite project page
CSQL is an open source transactional, persistent in-memory SQL database engine.csql project page
Effiproz is a transactional, persistent in-memory SQL database engine written entirely in C#. SQL features include SQL Stored Procedures, Functions, Triggers,etc. Support .NET Framework 3.5, Silverlight 3 and .NET Compact Framework.EffiProz project page
ElevateDB is a royalty-free, SQL:2003-compliant, compact, embedded database engine available for Delphi (Win32), C++Builder (Win32), Lazarus (Win32/WinCE), Visual Studio (.NET and .NET CF), and any ODBC-compliant application. Under Delphi, Lazarus, and C++Builder, ElevateDB can be compiled directly into the application, whereas under .NET it is one assembly and the ODBC driver is implemented as one .DLL. ElevateDB operates in single-user, multi-user file-sharing, and client-server modes, and includes the ElevateDB Server for client-server operation.
Embedded InnoDB is a standalone, embeddable form of the InnoDB Storage Engine. Given that Embedded InnoDB is based on the same code base as the InnoDB Storage Engine, it contains many of the same features: high-performance and scalability, multiversion concurrency control (MVCC), row-level locking, deadlock detection, fault tolerance, automatic crash recovery, etc. However, because the embedded engine is completely independent from MySQL, it lacks server components such as networking, object-level permissions, etc. By eliminating the MySQL server overhead, InnoDB has a small footprint and is well-suited for embedding in applications which require high-performance and concurrency. As with most embedded database systems, Embedded InnoDB is designed to be accessed primarily with an ISAM-like C API rather than SQL (though an extremely rudimentary SQL variant is supported).
Embedded InnoDB has not seen any product updates since 2009 (and downloads currently seem to be broken), but a non-Oracle maintained fork exists with HailDB.
Empress Embedded Database
Empress Software, Inc., developer of the Empress Embedded Database, is a privately held company founded in 1979. Empress Embedded Database is a full-function, relational database that has been embedded into applications by organizations small to large, with deployment environments including medical systems, network routers, nuclear power plant monitors, satellite management systems, and other embedded system applications that require reliability and power. Empress is an ACID compliant, SQL database engine with C, C++, Java, JDBC, ODBC, SQL, ADO.NET and kernel level APIs. Applications developed using these APIs may be run in standalone and/or server modes. Empress Embedded Database runs on Linux, Unix, Microsoft Windows and real-time operating systems.
Extensible Storage Engine
ESE is an Indexed Sequential Access Method (ISAM) data storage technology from Microsoft. ESE is notably a core of Microsoft Exchange Server and Active Directory. Its purpose is to allow applications to store and retrieve data via indexed and sequential access. Windows Mail and Desktop Search in the Windows Vista operating system also make use of ESE to store indexes and property information respectively.
McObject launched eXtremeDB as the first in-memory embedded database designed from scratch for real-time embedded systems. The initial product was soon joined by eXtremeDB High Availability (HA) for fault tolerant applications. The product family now includes 64-bit and transaction logging editions, and the hybrid eXtremeDB Fusion, which combines in-memory and on-disk data storage. In 2008, McObject introduced eXtremeDB Kernel Mode, the first embedded DBMS designed to run in an operating system kernel. Today, eXtremeDB is used in millions of real-time and embedded systems worldwide. McObject also offers Perst, an open source, object-oriented embedded database for Java, Java ME, .NET, .NET Compact Framework and Silverlight.
Firebird Embedded is a relational database engine. It's an open source fork of InterBase, is ACID compliant, supports triggers and stored procedures, and is available on Linux and Win32/Win64 systems. It has the same features as the classic and superserver version of Firebird, two or more threads (and applications) can access the same database at the same time starting with Firebird 2.5. So Firebird embedded acts as a local server for one threaded client accessing its databases (that means it works properly for ASP.NET web applications, because there, each user has its own thread, which means two users could access the same database at the same time, but they would not be in the same thread, because ASP.NET opens a new thread for each user). It exports the standard Firebird API entrypoints. The main advantage of Firebird embedded databases is, that unlike SQlite or Access databases, they can be plugged into a full Firebird server without any modifications at all also is multiplatform (runs on Linux, OS X with full asp.net mono support)
Written in Java Open source very fast database engine. Embedded and Server mode, Clustering support, can run inside the Google App Engine. Supports encrypted database files (AES or XTEA). The development of H2 was started in May 2004, but it was first published on December 14th 2005. H2 is dual licensed and available under a modified version of the MPL 1.1 (Mozilla Public License) or under the (unmodified) EPL 1.0 (Eclipse Public License).
HamsterDB is a non-relational library for persistent and ephemeral (cached) record storage. Native support exists for transactions, compressing, and encrypting data. HamsterDB provides a native API for C and C++. API wrappers exist for Java, Python, .NET languages, and Erlang. HamsterDB is available under the GPL (with an exception for linking to other free, open-source code) and commercial licensing. It is also possible to use HamsterDB as a network-accessed (non-embedded) database.
HSQLDB is an opensource relational database management system with a BSD-like license that runs in the same Java Virtual Machine as the embedded application. HSQLDB supports a variety of in-memory and disk-based table modes, Unicode and SQL:2008.
The HSS Database by HighSpeed-Solutions, is a client/embedded, zero-configuration, auto schema evolution, acid/transactional, LINQ Database engine with a common API for all platforms - MonoTouch, Mono for Android, .NET 4/4.5, Windows 8, Windows Phone 7.5/8 and Silverlight 5.
InfinityDB is an all Java B+Tree Database Engine that is embeddable in the smallest to the largest applications that run on hand held devices, workstations, servers, or in distributed systems. InfinityDB provides a data integrity guarantee through all non-media failures, provides a simple API with only a few basic methods, and requires no administrative support. Version 2 offers full ACID transactionality without a log and with fine-grained locks. Programmers can superimpose their own data model, or employ the entity-attribute-value model, by direct low-level access. The basic engine provides multi-valued or set attributes, all primitive Java types, unlimited large objects, heterogenous values, composite keys (Entities) and values, and unlimited sparse attributes, all extensible in-place with no schema changes.
Informix Dynamic Server
Informix Dynamic Server (IDS) is characterized as an enterprise class embeddable database server, combining embeddable features such as low footprint, programmable and autonomic capabilities with enterprise class database features such as high availability and flexible replication features. IDS is used in deeply embedded scenarios such as IP telephony call-processing systems, point of sale applications and financial transaction processing systems.
InterBase is a cross-platform, Unicode enabled SQL database platform able to be embedded within turn-key applications. Out of the box SMP support (Server Edition), SQL 92 compliance and support for Windows, Linux, Solaris, and Macintosh platforms. Ideal for small-to-medium enterprises.
ITTIA DB is a cross-platform embedded database for embedded system and intelligent mobile device software developers. ITTIA DB is a true relational database management system, supporting runtime SQL queries, isolation levels, write ahead logging, and B+ tree indexes. To support the wide variety of operating systems and hardware used in embedded development, ITTIA DB databases use a portable format that can be accessed with or without SQL through C and C++ application programming interfaces (API). Disk, memory, and hybrid databases are supported.
Kyoto Cabinet is a straightforward implementation of a dbm, it compares well to Oracle Berkeley DB, but on large datasets fails more gracefully, it is released under the GPL (with commercial licensing available) by Mikio Hirabayashi in 2009.
LevelDB is an ordered key/value store created by Google as a lightweight implementation of the BigTable storage design. As a library (which is the only way to use LevelDB), its native API is C++. It also includes official C wrappers for most functionality. Third-party API wrappers exist for Python, PHP, Go (pure Go LevelDB implementation exists but is in progress still), and Objective C. Google distributes LevelDB under the New BSD License.
LightningDB is a memory-mapped database developed by Symas for the OpenLDAP Project. It is written in C and the API is modeled after the Berkeley DB API, though much simplified. The library is extremely compact, compiling down to under 40KB of x86 object code, corruption proof, and orders of magnitude faster, more robust, more scalable, and more efficient than similar libraries like Berkeley DB, LevelDB, etc. The library implements B+trees with multiversion concurrency control (MVCC), Single_level_store, Copy_on_write and provides full ACID transactions with no deadlocks. The library is optimized for high read concurrency; readers need no locks at all. Readers don't block writers and writers don't block readers, so read performance scales perfectly linearly across arbitrarily many threads and CPUs. Third-party wrappers exist for C++, Erlang and Python. Lightning DB is distributed by the OpenLDAP Project under the OpenLDAP Public License. As of 2013 the OpenLDAP Project is deprecating the use of Berkeley DB, in favor of LightningDB.
MySQL Embedded Server Library
The libmysqld, MySQL Embedded Server Library provides most of the features of regular MySQL as a linkable library that can be run in the context of a client process. After initialization clients can use the same C API calls as when talking to a separate MySQL server but with less communication overhead and with no need for a separate database process.
NexusDB is the commercial successor to the FlashFiler database which is now open source. They can both be embedded in Delphi applications to create stand-alone executables with full database functionality.
Oracle Berkeley DB
As the name implies, Oracle�s embedded database is actually Berkeley DB, which Oracle acquired from Sleepycat Software. It was originally developed at the University of California. Berkeley DB is a fast, open-source embedded database and is used in several well-known open-source products, including the Linux and BSD Unix operating systems, Apache Web server, OpenLDAP directory, OpenOffice productivity suite.
RDM Embedded, produced by Raima was one of the first database management systems to be categorized as an embedded database when it made its debut in 1984 under the name db_Vista. According to Raima's definition, the product is embedded in two senses: first, it is embedded within an application, becoming an extension to the application, second, it is possible to use it in embedded computer/OS or real-time environments because of its small footprint and efficient operation. Its APIs (for C/C++ and SQL) have been designed to support the limited resources of embedded environments. Since its initial release, RDM Embedded has been continually evolving and is currently released as version 10.1. Today Raima produces two products under the product names RDM Embedded and RDM Server.
Scimore is an embedded database running on Windows. It performs fast and can easily handle millions of rows. This database provides full data reliability (ACID properties), manages heavy loads and includes features such as support for T-SQL, Read/Merge replication with ScimoreDB server, full text search. Clients can access database via .NET provider or C++ library.
IBM's SolidDB is another embedded database bought by a large technology company. Originally owned by Solid Information Technology, SolidDB was acquired in January 2008 by IBM. SolidDB is a hybrid disk/in-memory, relational database and is historically used as an embedded system database in telecommunications equipment, network software, and similar systems.
SQLite is a software library that implements a self-contained, serverless, zero-configuration, transactional SQL database engine. SQLite is the most widely deployed SQL database engine in the world. The source code, chiefly C, for SQLite is in the public domain. Includes both a native C library and a simple command line client for its database. It is embedded in the Google Android smart phone operating system.
SQL Server Compact
Microsoft's SQL Server Compact is an embedded database with wide variety of features like multi-process connections, T-SQL, ADO.NET Sync Services to sync with any back end database, Merge Replication with SQL Server, Programming API: LINQ to SQL, LINQ to Entities, ADO.NET. The product runs on both Desktop and Mobile Windows platforms. It has been in the market for long time, used by many enterprises in production software (Case Studies). The product went through multiple re-brandings and was known with multiple names like: SQL CE, SQL Server CE, SQL Server Mobile, SQL Mobile.
TurboDB is the family name for various embedded in-process SQL database engines by dataweb. TurboDB for VCL is an implementation in Delphi and can be used as a compatible database replacement with additional features like transactions and full-text search. If it becomes necessary, a client for the embedded engine can easily be upgraded to the server version. TurboDB for ODBC is based on the Delphi implementation and can be accessed via the ODBC interface while still being an in-process database. TurboDB for .NET is implemented in C# and can be used with .NET Framework, .NET Compact Framework, Silverlight and Windows Phone.
Paradigma Software Valentina DB is an embedded SQL database with wide variety of features, including broad support for native implementations on Windows, Linux and Mac OS X. It is available as a local engine for over 18 platforms. Developers can also use VDN to deploy a royalty free Embedded Server on Windows, Linux and Mac OS X, which also natively supports PHP and Ruby-on-Rails server side scripting.
VistaDB is an embedded SQL database written entirely in C# and supports a number of features to make it compatible with SQL Server like T-SQL datatypes and syntax. The product runs on .NET platforms, including Mono. It has been in the market since 2004, and is used by many companies (Customer List).
VistaDB was acquired by Gibraltar Software as of September 15, 2010.
- Mobile Database :
A mobile database is either a stationary database that can be connected to by a mobile computing device - such as smart phones or PDAs - over a mobile network, or a database which is actually carried by the mobile device. This could be a list of contacts, price information, distance travelled, or any other information.
Many applications require the ability to download information from an information repository and operate on this information even when out of range or disconnected. An example of this is a mobile workforce. In this scenario, a user would require access to update information from files in the home directories on a server or customer records from a database. This type of access and work load generated by such users is different from the traditional workloads seen in client-server systems of today.
Mobile databases are highly concentrated in the retail and logistics industries. They are increasingly being used in aviation and transportation industry.
Mobile users must be able to work without a network connection due to poor or even non-existent connections. A cache could maintained to hold recently accessed data and transactions so that they are not lost due to connection failure. Users might not require access to truly live data, only recently modified data, and uploading of changing might be deferred until reconnected.
Bandwidth must be conserved (a common requirement on wireless networks that charge per megabyte or data transferred). Mobile computing devices tend to have slower CPUs and limited battery life.
Users with multiple devices (e.g. smartphone and tablet) may need to synchronize their devices to a centralized data store. This may require application-specific automation features.
Users may change location geographically and on the network. Usually dealing with this is left to the operating system, which is responsible for maintaining the wireless network connection.
- Hekaton (database) :
Hekaton (also known as SQL Server In-Memory OLTP) is the project code name for an upcoming high performance In-memory Database for OLTP workloads built into Microsoft SQL Server. Traditional RDBMS architecture was designed when memory resources were expensive, and was optimized for disk I/O. Modern hardware has much more memory, which affects database design principles dramatically. Modern design can now optimize for a working set stored entirely in main memory. Hekaton fully provides ACID database properties.
In addition to memory optimization, Hekaton design also considers additional HW trends - such as multi-core processors and stalling CPU clock rate. Hekaton is built with Microsoft research support and is fundamentally different from the obsolescent "DBCC PINTABLE" feature in earlier SQL Server versions.
Hekaton's announcement at PASS conference 2012 (Professional Association for SQL Server) was warmly welcomed by industry analysts.
This SQL Server In-memory OLTP capability will be released in SQL 2014 version. The Data Warehousing In-memory technology is also available - as in the Columnstore technology.
- NoSQL :
"Structured storage" redirects here. For the Microsoft technology also known as structured storage, see COM Structured Storage.
A NoSQL database provides a mechanism for storage and retrieval of data that uses looser consistency models than traditional relational databases. Motivations for this approach include simplicity of design, horizontal scaling and finer control over availability. NoSQL databases are often highly optimized key - value stores intended for simple retrieval and appending operations, with the goal being significant performance benefits in terms of latency and throughput. NoSQL databases are finding significant and growing industry use in big data and real-time web applications. NoSQL systems are also referred to as "Not only SQL" to emphasize that they do in fact allow SQL-like query languages to be used.
ACID vs BASE NoSQL cannot necessarily give full ACID guarantees. Usually eventual consistency is guaranteed or transactions limited to single data items. This means that given a sufficiently long period of time over which no changes are sent, all updates can be expected to propagate eventually through the system.
Carlo Strozzi used the term NoSQL in 1998 to name his lightweight, open-source relational database that did not expose the standard SQL interface. Strozzi suggests that, as the current NoSQL movement "departs from the relational model altogether; it should therefore have been called more appropriately 'NoREL'.
Eric Evans (then a Rackspace employee) reintroduced the term NoSQL in early 2009 when Johan Oskarsson of Last.fm wanted to organize an event to discuss open-source distributed databases. The name attempted to label the emergence of a growing number of non-relational, distributed data stores that often did not attempt to provide atomicity, consistency, isolation and durability guarantees that are key attributes of classic relational database systems.
There have been various approaches to classify NoSQL databases, each with different categories and subcategories. Because of the variety of approaches and overlappings regarding the nonfunctional requirements and the feature-set it could be difficult to get and maintain an overview of the nonrelational database market. Nevertheless, the most basic classification that most would agree is one based on the data model.
- List of Databases we provide :
List of Databases we provide :
- 4D (4th Dimension) : 4D S.A.S. 1984 v13.2 2012-11-12 Proprietary
- ADABAS : Software AG 1970 8.1 2013-06 Proprietary
- Adaptive : Server Enterprise Sybase 1987 15.7 Proprietary
- Advantage : Database Server (ADS) Sybase 1992 11.1 2012 Proprietary
- Altibase : Altibase Corp. 2000 6.1.1 2012-04-01 Proprietary
- Apache : Derby Apache 2004 10.10.1.1 2013-04-15 Apache License
- Clustrix : Clustrix 2010 v5.0 2013-05-01 Proprietary
- CUBRID : NHN Corporation 2008-11 8.4.1 2012-02-24 GPL v2
- Datacom : CA, Inc. ? 11.2 Proprietary
- DB2 : IBM 1983 10.5 2013-04-23 Proprietary
- Drizzle : Brian Aker 2008 Build 1126 BSD, GPL v2
- Empress : Embedded Database Empress Software Inc 1979 10.20 2010-03 Proprietary
- EXASolution : EXASOL AG 2004 4.1 2012-07-17 Proprietary
- Firebird : Firebird project 2000-07-25 2.5.2 2013-03-24 IPL and IDPL
- HSQLDB : HSQL Development Group 2001 2.2.9 2013-07-08 BSD
- H2 : H2 Software 2005 1.3.171 2013-03-17 EPL and modified MPL
- Informix : Dynamic Server IBM 1980 12.10.xC1 2013-03-26 Proprietary
- Ingres : Ingres Corp. 1974 Ingres Database 10 2010-10-12 GPL and Proprietary
- InterBase : Embarcadero 1984 InterBase XE 2010-09-21 Proprietary
- Linter : SQL RDBMS RELEX Group 1990 6.x Proprietary
- LucidDB : The Eigenbase Project 2007-01 0.9.3 GPL v2
- MariaDB : MariaDB Community 2010-02-01 5.5.30 2013-03-12 GPL v2
- MaxDB : SAP AG 2003-05 7.6 2008-01 Proprietary
- Microsoft Access (JET) : Microsoft 1992 15 (2013) 2012-10-02 Proprietary
- Microsoft Visual Foxpro : Microsoft 1984 9 (2005) 2007-10-11 Proprietary
- Microsoft SQL Server : Microsoft 1989 2012 (v11) Proprietary
- Microsoft SQL Server : Compact (Embedded Database) Microsoft 2000 2010 (v3.5 SP2) Proprietary
- MonetDB/SQL : The MonetDB Developer Team 2004 11.9.1 2012-04 MonetDB Public License v1.1
- mSQL : Hughes Technologies 1994 3.9 2011-02 Proprietary
- MySQL : Sun Microsystems (now Oracle Corporation) 1995-11 5.5.29 2012-12-21 GPL or Proprietary
- MemSQL : MemSQL 2012-06 1.8 (2012) 2012-12 Proprietary
- Nexusdb : Nexus Database Systems Pty Ltd 2003-09 3.04 2010-05-08 Proprietary
- HP NonStop SQL : Hewlett-Packard 1987 SQL/MX 2.3 Proprietary
- Omnis Studio : TigerLogic Inc 1982-07 4.3.1 Release 1no 2008-05 Proprietary
- OpenBase SQL : OpenBase International 1991 11.0.0 Proprietary
- OpenEdge : Progress Software Corporation 1984 11.0 Proprietary
- OpenLink Virtuoso : OpenLink Software 1998 6.x 2012-08-02 GPL or Proprietary
- Oracle : Oracle Corporation 1979-11 12c Release 1 2013-06-25 Proprietary
- Oracle Rdb : Oracle Corporation 1984 18.104.22.168 2011-06-20 Proprietary
- Paradox Corel : Corporation 1985 11 2003 Proprietary
- Pervasive PSQL : Pervasive Software 1982 v11 SP3 2013 Proprietary
- Polyhedra DBMS : ENEA AB 1993 8.7 2013-03 Proprietary
- PostgreSQL : PostgreSQL Global Development Group 1989-06 9.2.4 2013-02-07 PostgreSQL Licence (a liberal Open Source license)
- R:Base : R:BASE Technologies 1982 9.5 Proprietary
- RDM : Raima Inc. 1984 11.0 2012-06-29 Proprietary
- RDM Server : Raima Inc. 1993 8.4 2012-10-31 Proprietary
- SAP HANA : SAP AG 2010 1.0 Proprietary
- ScimoreDB : Scimore 2005 3.0 2008-03-03 Proprietary
- SmallSQL : SmallSQL 2005-04-16 0.20 2008-12 LGPL
- SQL Anywhere : Sybase 1992 12.0 2010-07-09 Proprietary
- SQLBase : Unify Corp. 1982 11.5 2008-11 Proprietary
- SQLite : D. Richard Hipp 2000-08-17 22.214.171.124 2013-01-09 Public domain
- Superbase : Superbase 1984 Scientific (2004) Proprietary
- Teradata : Teradata 1984 14.10 Proprietary
- UniData : Rocket Software 1988 7.2.12 2011-10 Proprietary
- Xeround : Cloud Database
- Desktop Solutions
- Dotnet Solutions
- Portal Solutions
- Windows Developments
- Xml Solutions
- Open Source Developments
- Custom App Developments
- Database Solutions
- Data Warehousing
- Product Testing
- Cloud Services
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- Contact - Us
A solid working knowledge of productivity software and other IT tools has become a basic foundation for success in virtually any career. Beyond that, however, I don't think you can overemphasise the importance of having a good background in maths and science.....
"Every software system needs to have a simple yet powerful organizational philosophy (think of it as the software equivalent of a sound bite that describes the system's architecture)... A step in thr development process is to articulate this architectural framework, so that we might have a stable foundation upon which to evolve the system's function points. "
"All architecture is design but not all design is architecture. Architecture represents the significant design decisions that shape a system, where significant is measured by cost of change"
"The ultimate measurement is effectiveness, not efficiency "
"It is argued that software architecture is an effective tool to cut development cost and time and to increase the quality of a system. "Architecture-centric methods and agile approaches." Agile Processes in Software Engineering and Extreme Programming.
"Java is C++ without the guns, knives, and clubs "
"When done well, software is invisible"
"Our words are built on the objects of our experience. They have acquired their effectiveness by adapting themselves to the occurrences of our everyday world."
"I always knew that one day Smalltalk would replace Java. I just didn't know it would be called Ruby. "
"The best way to predict the future is to invent it."
"In 30 years Lisp will likely be ahead of C++/Java (but behind something else)"
"Possibly the only real object-oriented system in working order. (About Internet)"
"Simple things should be simple, complex things should be possible. "
"Software engineering is the establishment and use of sound engineering principles in order to obtain economically software that is reliable and works efficiently on real machines."
"Model Driven Architecture is a style of enterprise application development and integration, based on using automated tools to build system independent models and transform them into efficient implementations. "
"The Internet was done so well that most people think of it as a natural resource like the Pacific Ocean, rather than something that was man-made. When was the last time a technology with a scale like that was so error-free? The Web, in comparison, is a joke. The Web was done by amateurs. "
"Software Engineering Economics is an invaluable guide to determining software costs, applying the fundamental concepts of microeconomics to software engineering, and utilizing economic analysis in software engineering decision making. "
"Ultimately, discovery and invention are both problems of classification, and classification is fundamentally a problem of finding sameness. When we classify, we seek to group things that have a common structure or exhibit a common behavior. "
"Perhaps the greatest strength of an object-oriented approach to development is that it offers a mechanism that captures a model of the real world. "
"The entire history of software engineering is that of the rise in levels of abstraction. "
"The amateur software engineer is always in search of magic, some sensational method or tool whose application promises to render software development trivial. It is the mark of the professional software engineer to know that no such panacea exist "
Core Values ?Agile And Scrum Based Architecture
Agile software development is a group of software development methods based on iterative and incremental development, where requirements and solutions evolve through collaboration.....more
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Total Quality Management / TQM is an integrative philosophy of management for continuously improving the quality of products and processes. TQM is based on the premise that the quality of products and .....more
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We are more than code junkies. We're a company that cares how a product works and what it says to its users. There is no reason why your custom software should be difficult to understand.....more
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With extensive software development experience, our development team is up for any challenge within the Great Plains development environment. our Research works on IEEE international papers are consider....more
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We have a proven track record of developing and delivering solutions that have resulted in reduced costs, time savings, and increased efficiency. Our clients are very much ....more
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We simply dont release anything that isnt tested well. Tell us something cant be tested under automation, and we will go prove it can be. We create tests before we write the complementary production software......more
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If a customer needs technical support for one of our products, no-one can do it better than us. Our offices are open from 9am until 9pm Monday to Friday, and soon to be 24hours. Unlike many companies, you are able to....more
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We have a reputation for process genius, fanatical testing, high quality, and software joy. Whatever your business, our methods will work well in your field. We have done work in Erp Solutions ,e-commerce, Portal Solutions,IEEE Research....more
Why Choose Us ?
The intellectual commitment of our development team is central to the leonsoft ability to achieve its mission: to develop principled, innovative thought leaders in global communities.Read More
Today's most successful enterprise applications were once nothing more than an idea in someone's head. While many of these applications are planned and budgeted from the beginning.Read More
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