Big Data: Challenge for MNC’s like Google, Facebook…

Big Data = Big Challenges

Each minute of every day on the Internet

➡Every second, on average, around 6,000 tweets are tweeted on Twitter, which corresponds to over 350,000 tweets sent per minute.


Big data refers to the large, diverse sets of information that grow at ever-increasing rates. It encompasses the volume of information, the velocity or speed at which it is created and collected, and the variety or scope of the data points being covered.

Big Data as Challenge

Big Data has So many Benefits but these come with an Umbrella of Problems.

  1. Data Comes in many different forms, either structured or unstructured. For Computations or Research, These data must be classified into various categories.
  2. Generated insights from these data must be Fast and Precise.
  3. In order for organizations to capitalize on the opportunities offered by big data, they are going to have to do some things differently. And that sort of change can be tremendously difficult for large organizations.
  4. Security is also a big concern for organizations with big data stores. After all, some big data stores can be attractive targets for hackers or advanced persistent threats (APTs).

To Deal with These Challenges Companies Came up With a new Concept of Distributed Computing and Parallel computing system.

Distributed computing is a model in which components of a software system are shared among multiple computers to improve efficiency and performance.


Based On Google MapReduce, Hadoop runs applications using the MapReduce algorithm, where the data is processed in parallel with others. In short, Hadoop is used to develop applications that could perform a complete statistical analysis on huge amounts of data.

NoSQL Databases :

NoSQL systems are distributed, non-relational databases designed for large-scale data storage and for massively-parallel, high-performance data processing across a large number of commodity servers.


Apache Spark is a data processing framework that can quickly perform processing tasks on very large data sets, and can also distribute data processing tasks across multiple computers, either on its own or in tandem with other distributed computing tools.

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