Expected Properties of a Big Data System

Prerequisite – Introduction to Big Data, Benefits of Big Data
There are various properties which mostly relies on complexity as per their scalability in the big data. As per these properties, Big data system should perform well, efficient, and reasonable as well. Let’s explore these properties step by step.

  1. Robustness and error tolerance –
    As per the obstacles in distributed system encountered, it is quite arduous to build a system that “do the right thing”. Systems are required to behave in a right manner despite machines going down randomly, the composite semantics of uniformity in distributed databases, redundancy, concurrency, and many more. These obstacles make it complicated to reason about the functioning of the system. Robustness of big data system is the solution to overcome the obstacles associated with it.

    It’s domineering for system to tolerate the human-fault. It’s an often-disregarded property of the system which can not be overlooked. In a production system, its domineering that the operator of the system might make mistakes, such as by providing incorrect program that can interrupt the functioning of the database. If re-computation and immutability is built in the core of a big data system, the system will be distinctively robust against human fault by delivering a relevant and quite cinch mechanism for recovery.



  2. Debuggability –
    A system must be debug when unfair thing happens by the required information delivered by the big data system. The key must be able to recognize, for every value in the system. Debuggability is proficient in the Lambda Architecture via the functional behaviour of the batch layer and with the help of re-computation algorithm when needed.

  3. Scalability –
    It is the tendency to handle the performance in the context of growing data and load by adding resources to the system. The Lambda Architecture is straight scalable diagonally to all layers of the system stack: scaling is achieved by including further number of machines.

  4. Generalization –
    A wide range of applications can be function in a general system. As Lambda Architecture is based on function of all data, a number of applications can run in a generalized system. Also, Lambda architecture can generalize social networking, applications, etc.

  5. Ad hoc queries –
    The ability to perform ad hoc queries on the data is significant. Every large dataset contains unanticipated value in it. Having the ability of data mining constantly provides opportunities for new application and business optimization.

  6. Extensibility –
    Extensible system enables to function to be added cost effectively. Sometimes, a new feature or a change to an already existing system feature needs to reallocate of pre-existing data into a new data format. Large-scale transfer of data become easy as it is the part in building an extensible system.

  7. Low latency reads and updates –
    Numerous applications are needed the read with low latency, within a few milliseconds and hundred milliseconds. In Contradict, Update latency varies within the applications. Some of the applications needed to be broadcast with low latency, while some can function with few hours of latency. In big data system, there is a need of applications low latency or updates propagated shortly.

  8. Minimal Maintenance –
    Maintenance is like penalty for developers. It is the operations which is needed to keep the functionality of the systems smooth. This includes forestalling when to increase number of machines to scale, keeping processes functioning well along with their debugging.

    Selecting components with probably little complexity plays a significant role in minimal maintenance. A developer always willing to rely on components along with quite relevant mechanism. Significantly, distributed database has more probability of complicated internals.



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