It presents an abridged and simplified perspective on the history of databases from the s to the late s. It is hard to make out the text in the above diagram, so I would recommend that readers click on the link provided in order to view a much larger version with bigger and more legible text. The infographic references a number of terms. Below I provide links to definitions of several of these, which are taken from The Data and Analytics Dictionary.
To my mind, it is interesting to see just how long we have been grappling with the best way to set up databases. Also of note is that some of the Big Data technologies are actually relatively venerable, dating to the mid-to-late s some elements are even older, consisting of techniques for handling flat files on UNIX or Mainframe computers back in the day. I hope that both the infographic and the definitions provided above contribute to the understanding of the history of databases and also that they help to elucidate the different types of database that are available to organisations today.
From: peterjamesthomas. Follow peterjthomas. Of course Teradata in was twenty or thirty or forty years ahead of their time! Sort of depends on what you mean by database. In my view incorporates network and hierarchical. It is absolutely untrue that relational did not reach mainstream until after Y2K. Additionally, the whole thing is a bout DBMS or database management systems not databases.
But maybe that is just being pedantic. Although this provided very efficient access for the original queries and transactions that the database was designed to handle, it did not provide enough flexibility to access records efficiently when new queries and transactions were identified.
In particular, new queries that required a different storage organization for efficient processing were quite difficult to implement efficiently. Another shortcoming of early systems was that they provided only programming language interfaces.
This made it time-consuming and expensive to implement new queries and transactions, since new programs had to be written, tested, and debugged. Most of these database systems were implemented on large and expensive mainframe computers starting in the mids and continuing through the s and s. The main types of early systems were based on three main paradigms: hierarchical systems, network model based systems, and inverted file systems. Relational databases were originally proposed to separate the physical storage of data from its conceptual representation and to provide a mathematical foundation for data representation and querying.
The relational data model also introduced high-level query languages that provided an alternative to programming language interfaces, making it much faster to write new queries. Relational representation of data somewhat resembles the example we presented in Figure 1.
Relational systems were initially targeted to the same applications as earlier systems, and provided flexibility to develop new queries quickly and to reorganize the database as requirements changed.
Hence, data abstraction and program-data independence were much improved when compared to earlier systems. Early experimental relational systems developed in the late s and the commercial relational database management systems RDBMS introduced in the early s were quite slow, since they did not use physical storage pointers or record placement to access related data records.
With the development of new storage and indexing techniques and better query processing and optimization, their performance improved. Eventually, relational databases became the dominant type of data-base system for traditional database applications. Relational databases now exist on almost all types of computers, from small personal computers to large servers.
The emergence of object-oriented programming languages in the s and the need to store and share complex, structured objects led to the development of object-oriented databases OODBs.
Initially, OODBs were considered a competitor to relational databases, since they provided more general data structures. They also incorporated many of the useful object-oriented paradigms, such as abstract data types, encapsulation of operations, inheritance, and object identity. However, the complexity of the model and the lack of an early standard contributed to their limited use. They are now mainly used in specialized applications, such as engineering design, multimedia publishing, and manufacturing systems.
The World Wide Web provides a large network of interconnected computers. Documents can be linked through hyperlinks , which are pointers to other documents.
In the s, electronic commerce e-commerce emerged as a major application on the Web. It quickly became apparent that parts of the information on e-commerce Web pages were often dynamically extracted data from DBMSs. A variety of techniques were developed to allow the interchange of data on the Web. Currently, eXtended Markup Language XML is considered to be the primary standard for interchanging data among various types of databases and Web pages.
XML combines concepts from the models used in document systems with database modeling concepts. Chapter 12 is devoted to the discussion of XML.
Extending Database Capabilities for New Applications. The success of database systems in traditional applications encouraged developers of other types of applications to attempt to use them. Such applications tradition-ally used their own specialized file and data structures. As a result, customers demanded a standard be developed, in turn leading to Bachman forming the Database Task Group.
Searching for records could be accomplished by one of three techniques:. He wrote a series of papers, in , outlining novel ways to construct databases. His ideas eventually evolved into a paper titled A Relational Model of Data for Large Shared Data Banks , which described a new method for storing data and processing large databases. RDBM Systems were an efficient way to store and process structured data. Unstructured data is both non-relational and schema-less, and relational database management systems simply were not designed to handle this kind of data.
MySQL has evolved into an extremely scalable database system with the ability to operate on multiple platforms. Some key features of MySQL follow:. A DBMS using columns is quite different from traditional relational database systems. It stores data as portions of columns, instead of as rows. The change in focus, from row to a column, lets column databases maximize their performance when large amounts of data are stored in a single column.
This strength can be extended to data warehouses and CRM applications. A key-value pair database is useful for shopping cart data or storing user profiles. All access to the database is done using a primary key. Typically, there is no fixed schema or data model.
The key can be identified by using a random lump of data. Key-value stores are not useful when there are complex relationships between data elements or when data needs to be queried by other than the primary key. Generally speaking, NoSQL databases are preferable in certain use cases to relational databases because of their speed and flexibility. This non-relational system is fast, uses an ad-hoc method of organizing data, and processes high volumes of different kinds of data.
Each of these organizations stores and processes colossal amounts of unstructured data. Unfortunately, NoSQL does come with some problems. It can also be difficult to find tech support if your open-source NoSQL system goes down.
Hardware can fail, but NoSQL databases are designed with a distribution architecture that includes redundant backup storage of both data and function. It does this by using multiple nodes database servers. If one, or more, of the nodes goes down, the other nodes can continue with normal operations and suffer no data loss.
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