In this Article we are going to discuss how Hadoop so Fastly store and retrieve the Data from the Bigdata. And Discuss About Some Big Company like Facebook …

What is Hadoop?

Hadoop is a framework that allows us to store Big Data in a distributed environment, so that, you can process it parallelly.

Components of Hadoop: -

  1. HDFS (Hadoop Distributed File System)
  2. YARN

HDFS: It allows us to store data of various formats across a cluster.

YARN: We used for resource management in Hadoop. It allows parallel processing over the data

Hadoop Cluster: -

It an extraordinary computational system, where we can interconnect computers using network which are capable enough to communicate with each other and work on a given task as a single unit using commodity Hardware. It Work On the architecture of Master & Slave.

Advantage of Hadoop: -

  • Scalable
  • Cost-effective
  • Flexible
  • Fast

Big Data:-

Big Data is also data but with a huge size. Such data is so large and complex that none of the traditional data management tools are able to store it or process it efficiently. The main idea behind Big Data is that the more you know about anything, the more you can gain insights and make a decision or find a solution.

Some Facts :-

1. The New York Stock Exchange generates about one terabyte of new trade data per day.

2. The statistic shows that 500+terabytes of new data get ingested into the databases of social media site Facebook, every day. This data is mainly generated in terms of photo and video uploads, message exchanges, putting comments etc.

Characteristics Of Big Data:-

i. Volume

ii. Velocity

iii. Variety

Benefits of Big Data Processing:-

i. Businesses can utilize outside intelligence while taking decisions

ii. Early identification of risk to the product/services, if any

iii. Better operational efficiency

Data Analytics:-

Big data analytics describes the process of uncovering trends, patterns, and correlations in large amounts of raw data to help make data-informed decisions. With the explosion of data, early innovation projects like Hadoop, Spark, and NoSQL databases were created for the storage and processing of big data. Even now, big data analytics methods are being used with emerging technologies, like machine learning, to discover and scale more complex insights.

Big data analytics refers to collecting, processing, cleaning, and analyzing large datasets to help organizations operationalize their big data.

Big data analytics tools and technology:-

  1. Hadoop
  2. NoSQL databases
  3. MapReduce
  4. YARN
  5. Spark
  6. Tableau

The big challenges of big data:-

Big data brings big benefits, but it also brings big challenges such new privacy and security concerns, accessibility for business users, and choosing the right solutions for your business needs.

  1. Making big data accessible
  2. Maintaining quality data
  3. Keeping data secure
  4. Finding the right tools and platforms

Some Interesting Case study :-

  1. https://eng.uber.com/uber-big-data-platform/
  2. https://data-flair.training/blogs/data-science-at-netflix/

B.Tech student and Researcher. Like To Study And Publish Article Related To New Technologies.

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