Big data is primarily defined by the volume of a data set. Big data sets are generally huge – measuring tens of terabytes – and sometimes crossing the threshold of petabytes. The term big data was preceded by very large databases (VLDBs) which were managed using database management systems (DBMS). Today, big data falls under three categories of data sets – structured, unstructured and semi-structured.
Structured data sets comprise of data which can be used in its original form to derive results. Examples include relational data such as employee salary records. Most modern computers and applications are programmed to generate structured data in preset formats to make it easier to process.
Unstructured data sets, on the other hand, are without proper formatting and alignment. Examples include human texts, Google search result outputs, etc. These random collections of data sets require more processing power and time for conversion into structured data sets so that they can help in deriving tangible results.Semi-Structured data sets are a combination of both structured and unstructured data. These data sets might have a proper structure and yet lack defining elements for sorting and processing. Examples include RFID and XML data.
Semi-Structured data sets are a combination of both structured and unstructured data. These data sets might have a proper structure and yet lack defining elements for sorting and processing. Examples include RFID and XML data.
Big data processing requires a particular setup of physical and virtual machines to derive results. The processing is done simultaneously to achieve results as quickly as possible. These days big data processing techniques also include Cloud Computing and Artificial Intelligence. These technologies help in reducing manual inputs and oversight by automating many processes and tasks.
The evolving nature of big data has made it difficult to give it a commonly accepted definition. Data sets are consigned the big data status based on technologies and tools required for their processing.
Big data analytics is the process of extracting useful information by analysing different types of big data sets. Big data analytics is used to discover hidden patterns, market trends and consumer preferences, for the benefit of organizational decision making. There are several steps and technologies involved in big data analytics.
Data acquisition has two components: identification and collection of big data. Identification of big data is done by analyzing the two natural formats of data – born digital and born analogue.
It is the information which has been captured through a digital medium, e.g. a computer or smartphone app, etc. This type of data has an ever expanding range since systems keep on collecting different kinds of information from users. Born digital data is traceable and can provide both personal and demographic business insights. Examples include Cookies, Web Analytics and GPS tracking.
When information is in the form of pictures, videos and other such formats which relate to physical elements of our world, it is termed as analogue data. This data requires conversion into digital format by using sensors, such as cameras, voice recording, digital assistants, etc. The increasing reach of technology has also raised the rate at which traditionally analogue data is being converted or captured through digital mediums.
The second step in the data acquisition process is collection and storage of data sets identified as big data. Since the archaic DBMS techniques were inadequate for managing big data, a new method is used for collecting and storing big data. The process is called MAD – magnetic, agile and deep. Since, managing big data requires a significant amount of processing and storage capacity, creating such systems is out-of-reach for most entities which rely on big data analytics. Thus, the most common solutions for big data processing today are based on two principles – distributed storage and Massive Parallel Processing a.k.a. MPP. Most of the high-end Hadoop platforms and specialty appliances use MPP configurations in their system.
These database storage systems are designed to overcome one of the major hurdles in the way of big data processing – the time taken by traditional databases to access and process information. IMDB systems store the data in the RAM of big data servers, therefore, drastically reducing the storage I/O gap. Apache Spark is an example of IMDB systems. VoltDB, NuoDB and IBM solidDB are some more examples of the same.
Apache Hadoop is a hybrid data storage and processing system which provides scalability and speed at reasonable costs for mid and small-scale businesses. It uses a Hadoop Distributed File System (HDFS) for storing large files across multiple systems known as cluster nodes. Hadoop has a replication mechanism to ensure smooth operation even during instances of individual node failures. Hadoop uses Google’s MapReduce parallel programming as its core. The name originates from ‘Mapping’ and ‘Reduction’ of functional programming languages in its algorithm for big data processing. MapReduce works on the premise of increasing the number of functional nodes over increasing processing power of individual nodes. Moreover, Hadoop can be run using readily available hardware which has sped up its development and popularity, significantly.
It is a recent concept which is based on contextual analysing of big data sets to discover the relationship between separate data items. The objective is to use a single data set for different purposes by different users. Data mining can be used for reducing costs and increasing revenues.
Big data is finding usage in almost all industries today. Here is a list of the top segments using big data to give you an idea of its application and scope.
Today, the advent of Internet of Things and the development of AI technology has simplified implementation of big data solutions to the degree that even medium to small scale businesses are benefiting from it. And since the top 10 list comprises of sectors which are directly or indirectly associated with various businesses, the imperative of this technology increases even further. Using big data analytics, businesses can take informed decisions and better their operational efficiency in a number of ways.
E.g. Using big data analytics, businesses can take informed decisions and better their operational efficiency in a number of ways. E.g.
Deploying data science for your business –
The world is moving towards a more connected future, and big data solutions are going to play a big part in automation and development of AI technologies. Companies like Google are already using Machine Learning processes for greater precision in delivering their services. As technologies around the globe become more synchronous and interoperable, big data will become the core that connects them together. Therefore, companies using big data solutions need to keep up with its evolving nature while those still reluctant to invest should rethink their organizational policies. There are a few pointers which can be helpful in getting the most out of your investment in big data.
Moreover, big data is also resonating with government and public-sector agencies, which is a good sign for businesses all around the world as this will help deepen the public-private collaboration in a range of fields.
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