big data analytics - Wikitechy https://www.wikitechy.com/interview-questions/tag/big-data-analytics/ Interview Questions Tue, 14 Sep 2021 09:54:53 +0000 en-US hourly 1 https://wordpress.org/?v=6.9 https://www.wikitechy.com/interview-questions/wp-content/uploads/2025/10/cropped-wikitechy-icon-32x32.png big data analytics - Wikitechy https://www.wikitechy.com/interview-questions/tag/big-data-analytics/ 32 32 Why Hadoop used for Big Data Analytics ? https://www.wikitechy.com/interview-questions/big-data/why-hadoop-used-for-big-data-analytics/ https://www.wikitechy.com/interview-questions/big-data/why-hadoop-used-for-big-data-analytics/#respond Mon, 12 Jul 2021 17:16:29 +0000 https://www.wikitechy.com/interview-questions/?p=269

Why Hadoop used for Big Data Analytics ?

  • Big data analytics is the process of examining large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information.
  • Hadoop is a framework to store and process big data. Hadoop specifically designed to provide distributed storage and parallel data processing that big data requires.

Hadoop is the best solution for storing and processing big data because:

Hadoop stores huge files as they are (raw) without specifying any schema.

  • High scalability – We can add any number of nodes, hence enhancing performance dramatically.
  • High availability – In hadoop data is highly available despite hardware failure. If a machine or few hardware crashes, then we can access data from another path.
  • Reliable – Data is reliably stored on the cluster despite of machine failure.
  • Economic – Hadoop runs on a cluster of commodity hardware which is not very expensive.

What is Hadoop ?

  • Hadoop is an open source project from Apache Software Foundation.
  • It provides a software framework for distributing and running applications on clusters of servers that is inspired by Google’s Map-Reduce programming model as well as its file system(GFS).
  • Hadoop was originally written for the nutch search engine project.
  • Hadoop is open source framework written in Java. It efficiently processes large volumes of data on a cluster of commodity hardware.
  • Hadoop can be setup on single machine , but the real power of Hadoop comes with a cluster of machines , it can be scaled from a single machine to thousands of nodes. Hadoop consists of two key parts,
    • Hadoop Distributes File System(HDFS)
    • Map-Reduce.
Hadoop Overview

Hadoop Distributed File System(HDFS)

  • HDFS is a highly fault tolerant, distributed, reliable, scalable file system for data storage.
  • HDFS stores multiple copies of data on different nodes; a file is split up into blocks (Default 64 MB) and stored across multiple machines.
  • Hadoop cluster typically has a single namenode and number of datanodes to form the HDFS cluster.

Map-Reduce

  • Map-Reduce is a programming model designed for processing large volumes of data in parallel by dividing the work into a set of independent tasks.
  • It is also a paradigm for distributed processing of large data set over a cluster of nodes.
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What are the steps to deploy a big data solution ?

    • There are three steps to deploy a Big Data Solution
Deploying Big Data solution

Data Ingestion

  • The first step for deploying a big data solution is the data ingestion i.e. extraction of data from various sources.
  • The data source may be a CRM like Salesforce, Enterprise Resource Planning System like SAP, RDBMS like MySQL or any other log files, documents, social media feeds etc.
  • The data can be ingested either through batch jobs or real-time streaming. The extracted data is then stored in HDFS.
Data Ingestion in Big Data

Data Storage

  • After the data ingestion, the next step is to store the extracted data. The data either be stored in HDFS or NoSQL database (i.e. HBase).
  • The HDFS storage works well for sequential access whereas HBase for random read/write access.
Data Storage

Data Processing

  • The final step in deploying a big data solution is the data processing.
  • The data is processed through one of the processing frameworks like Spark, MapReduce, Pig, etc.
Data Processing in Big Data
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How big data analysis helpful in increasing business revenue ?

  • Big data analysis has become most important for the businesses. It helps businesses to differentiate themselves from others and increase the revenue. Through predictive analytics, big data analytics provides businesses customized recommendations and suggestions.
How big data analysis helpful in increasing business revenue
  • And also, big data analytics enables businesses to launch new products depending on customer needs and preferences. These factors make businesses earn more revenue, and thus companies are using big data analytics.
  • Companies may encounter a significant increase of 5-20% in revenue by implementing big data analytics.
  • Some popular companies are using big data analytics to increase their revenue is – Walmart, LinkedIn, Facebook, Twitter, Bank of America etc.
How big data analysis helpful in increasing business revenue
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What is Big Data ?

  • Big Data is a term associated with complex and large datasets.
  • A relational database cannot handle big data, and that’s why special tools and methods are used to perform operations on a vast collection of data.
  • Big data enables companies to understand their business better and helps them derive meaningful information from the unstructured and raw data collected on a regular basis.
  • Big data also allows companies to make better business decisions backed by data.
What is Big Data

Characteristics Of Big Data

  • Volume – The Size of data plays a very essential role in determining value out of data.
  • Variety – The next aspect of Big Data is its variety. Variety refers to heterogeneous sources and the nature of data, both structured and unstructured.
  • Velocity – The term ‘velocity’ refers to the speed of generation of data.
  • Variability – The process of being able to handle and manage the data effectively.
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