Reverse Outsourcing: The New Trend in Outsourcing
Gone are the days when outsourcing meant a steady stream of jobs being shipped to India. Now the trend is for Indian companies to create jobs in the West. This phenomenon which is gathering pace in recent times is known as reverse outsourcing or the trend of Indian IT and BPO companies opening offices in […]
Shapes of Data and Characteristics of Shape
The shape of the data determines the type of tools that can be used to draw conclusions from it. Here is how to graphically plot out the data to find its shape: Step 1: Plot Data into Categories To begin with, the data must be divided into equal categories. The categories must have equal intervals […]
Data Collection Plan
A data collection plan is a detailed document. It describes the exact steps as well as the sequence that needs to be followed in gathering the data for the given Six Sigma project. This document is important because the people that design the data gathering plan are not the same people that will actually be […]
Building Model-Driven Decision Support System (MDSS)
As the name itself explains, a model-driven decision support system utilizes a model to solve problems or aid in decision making. A model can be statistical, financial, mathematical, analytical, simulation or optimization. A model-driven DSS may employ a single model or a combination of two or more models, depending upon the specific needs of its […]
The Impact of COVID-19 on Start-ups
Startups had become an important part of the modern economic system. Most of the companies which have achieved billion-dollar valuations in the past few years have come out of the startup ecosystem. Companies like Uber, Twitter, Facebook, and Airbnb are testimony to this fact. The startup system has often been viewed as a risky place. […]
One of the most important purposes of development of computer systems was the database application which could have been used on them. Data processing drove growth of computer processor speed.
In fact, data processing predates the computers; punched cards were used in the US for collecting data for census during beginning of 20th century. Earliest data processing was done by punched cards on mechanical devices. The real development in data processing speed, storage of data and development of DB applications started much later i.e. from 1950s.
Magnetic tapes were used to store data and being read from it. These database applications had hierarchical structure and used network systems. They were extremely efficient when used with the original query, exactly developed for them, but the DB was not designed to handle new queries or transactions. Also the magnetic tapes must be in same sorted order so as to retrieve the authentic data.
Later in 60s hard disks came about and data retrieval was faster and did not need be stored sequentially. This period was also remarkable in terms of advancement in DB Systems.
Later in 1970 Edgar Codd, father or Relational Database Model, conceptualized a new structure for Database construction and wrote a groundbreaking paper ‘A Relational Model of Data for Large Shared Data Banks’. He freed database from procedural ways of querying and marked the beginning of Data Abstraction i.e. hiding details of how Database is implemented to application programmers and end users.
System R, based on Codd’s concept was developed by IBM and it was first to have a language for querying called SQL or Structured Query Language. Later, System R was further developed to a mainstream commercial DBMS product known as DB2.
Object oriented programming was rapidly developing in the 80s and it also helped break into what we know as Object Oriented Databases. The idea was to treat data as objects and it became easier to conceptualize and program using this idea.
Another great development which happened was processing speed of processors and also conceptualization of indexing which greatly increased data access times, and performances of DB.
90s was a time of a World Wide Web, so unprecedented like world had never seen before. The data was here on the internet.
Databases to which links were forwarded were varied and different and it needed a technique to interchange data efficiently. Also the database had to be of very high availability working 24x7.
XML or eXtended Markup Language is a standard for providing data exchange among different databases and WebPages.
More recently, there has been a growing trend of NoSQL database. These are different from so called classical databases and do not rely on Relational Model for their structure. They do not query data using Structured Query Language but UnQL or Unstructured Query Language which is still in development stage (it is similar to XQuery). These databases are generally used when working with huge quantities of data. Some examples are Mongo DB, CouchBase, HBase used by facebook, Big Table used by Google and Dynamo DB used by Amazon.
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