Big Data has become a radical norm for enterprises which intend to turn into giant industries today. Last century has been a turning point in the journey of our technological intelligence. Big Data is broadly the generation of vast information (under three V’s of volume, velocity and variety) which leads to rise of insights through processing and analysis. While we have perfected the first step of collecting data, enterprises face huge challenges in categorizing them (according to relevancy) and analyzing them effectively (depending on type of data). The task of data classification, processing and analysis can be undertaken by different technologies which generally follow one of these models: Hadoop and NoSQL.
While Hadoop denotes a matrix of software packets (including HDFS and MapReduce) which are backed by a highly distributed filesystem supporting large-scale data computation, NoSQL (Not-Only SQL) refers to non-relational solutions of handling data like HBase, Riak, Cassandra and CouchDC. Traditional schemes follow the Hadoop way by using relational database management structures while NoSQL is taking up the progressive route of non-tabular and non-relational data management.
The weightlifters of the technology world – Google, LinkedIn, Facebook and Amazon pitched in for the development of NoSQL technology. NoSQL was a response to the emergence of highly varied and complex forms of data procured from millions of daily onliners through Cloud Computing and Internet of Things. While the relational scheme picks up data and fragments it into tables (related to one another), NoSQL follows a different methodology. For instance, a NoSQL database which is doc-oriented will select the data you wish to store and then cumulate it into documents through the JSON format. Here, every JSON doc will act as a tool used by your application. This allows for greater flexibility and dynamic molding of the information.
What can NoSQL model teach the Big Data revolution? Here are 4 insights we can pick up from the NoSQL development.
Insight 1: The simpler it is, the more effectively it can be used
When the data has poured into our tech parlors, the main task is to run it through a Big Data technology that can sift and make it potentially useful. There can either be complex or simple Big Data tools. Those tools would be complex which requires a heavily trained staff to set and get it going while a simple Big Data technology will present an intuitive interface that can be easily put to use. Apart from draining human resources, having a simple Big Data technology will allow you to divert intelligence to production of insights and policies. NoSQL works on the principle of bringing together complexity and creating simplicity through stacking. By being easy to learn and perceptive, it stands as an attractive option for enterprises which are dealing with humongous amounts of diverse information.
Insight 2: Adaptation to the Changing Data Types is Essential
Darwin’s dangerous idea spelled out the pattern of survival for all beings. Surprisingly, including artificially created beings. Today, Big Data has become an organism in itself which is continually producing novel data types. Any enterprise can only survive in such a consistently changing environment when it learns to adapt and handle these different data types. In NoSQL, a wide range of data types are included and the process of developing ways of dealing with other forms of data is ongoing.
For instance, consider Craigslist that previously used MySQL for storing data. Faced with lack of elasticity and cost of database management, they shifted over two billion documents to MongoDB which runs on NoSQL model. With this shift came greater scalability and performance where auto-sharding was used to initially handle about 5 billion docs and 10TB data. Learning through such case studies, enterprises can imply the crucial need for adaptation capacities (especially, for unstructured data) in its Big Data technology.
Insight 3: Kingdom of JSON in Big Data world
Insight 4: Updating the Age-Old Assumptions
When Big Data was initiated into the cult of technology, there were certain starting points which are today obsolete. Initially, the schemas were the priority. They were defined in the beginning and then, other tasks were taken up. However, load time is not the moment when structure of data is declared. The query time has become the center point of defining data structure. After the loading and interpretation of data, analysis is undertaken. This is the point where the structure is determined. Why not? Because the data changes at too fast a pace – in size and in form.
When relational management systems are used, the optimum performance can only be received with those data whose type is fixed. However, when relational structures are faced with unstable and constantly changing data, its processing faculties fall greatly. Thus, an enterprise needs to choose Big Data technology that is not attached to only fixed data type and can dynamically approach different forms and sizes of data.
Development methodologies have also evolved from waterfall-to-agile moving into the ‘schema-on-read’ methods. The biggest requirement today is to get our hands on the rapidly rising data uploaded constantly onto the internet. We cannot go with the traditional method where the data is converted into a new format every time. With the schema-on-read approach, rather than accommodating the data as it is processed, it is filtered and fitted into a plan or scheme during the first steps.