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This complete presentation has PPT slides on wide range of topics highlighting the core areas of your business needs. It has professionally designed templates with relevant visuals and subject driven content. This presentation deck has total of seventy nine slides. Get access to the customizable templates. Our designers have created editable templates for your convenience. You can edit the color, text and font size as per your need. You can add or delete the content if required. You are just a click to away to have this ready-made presentation. Click the download button now.

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Content of this Powerpoint Presentation

Slide 1: This slide introduces Hadoop (IT). State Your Company Name and begin.
Slide 2: This is an Agenda slide. State your agendas here.
Slide 3: This slide shows Table of Content for the presentation.
Slide 4: This slide presents Table of Content for the presentation.
Slide 5: This slide displays title for topics that are to be covered next in the template.
Slide 6: This slide represents current situation of the company, including structured data, unstructured data, etc.
Slide 7: This slide showcases title for topics that are to be covered next in the template.
Slide 8: This slide shows why Hadoop is important based on big data storage capacity.
Slide 9: This slide presents importance of the Hadoop Platform, including open-source, Hadoop ecosystem, etc.
Slide 10: This slide displays global market share of Hadoop, including classification based on services.
Slide 11: This slide represents advantages of Hadoop on the basis of scalability, flexibility, cost, etc.
Slide 12: This slide showcases title for topics that are to be covered next in the template.
Slide 13: This slide shows Core Components of Hadoop Framework.
Slide 14: This slide presents Hadoop distributed file system architecture, including its components.
Slide 15: This slide displays Goals of Hadoop Distributed File System (HDFS).
Slide 16: This slide represents Hadoop MapReduce architecture and how it processes a huge amount of information.
Slide 17: This slide showcases Map and Reduce Function of MapReduce Task.
Slide 18: This slide shows map phase of the MapReduce task, including record reader, map, combiner, etc.
Slide 19: This slide presents reduce phase of the MapReduce task that includes the sort and shuffle.
Slide 20: This slide displays job execution flow of MapReduce, including input data stored on HDFS.
Slide 21: This slide represents Hadoop Yet Another Resource Negotiator (YARN) Architecture.
Slide 22: This slide showcases Components of Yet Another Resource Negotiator (YARN).
Slide 23: This slide shows title for topics that are to be covered next in the template.
Slide 24: This slide presents Hadoop cluster and how it helps to process queries on a massive amount of data.
Slide 25: This slide displays architecture of the Hadoop cluster’s component.
Slide 26: This slide represents functions of Name Node in master in Hadoop cluster architecture.
Slide 27: This slide showcases functions of Resource Manager in master in Hadoop cluster.
Slide 28: This slide shows slaves in Hadoop cluster architecture along with functions of its additional components.
Slide 29: This slide presents client node in Hadoop cluster architecture and its various functions.
Slide 30: This slide displays Communication Protocols used in Hadoop Cluster.
Slide 31: This slide represents Best Practices for Building Hadoop Cluster.
Slide 32: This slide showcases Features of Hadoop Cluster Management Tool.
Slide 33: This slide shows benefits of the Hadoop cluster, including scalable, cost-effective, robustness, etc.
Slide 34: This slide presents title for topics that are to be covered next in the template.
Slide 35: This slide displays architecture of Hadoop, including its various components and elements.
Slide 36: This slide represents internal working of Hadoop, including how it distributes data storage and processing.
Slide 37: This slide showcases Operation Modes of Hadoop Framework.
Slide 38: This slide shows title for topics that are to be covered next in the template.
Slide 39: This slide presents Hadoop as a big data management platform and how it stores data in the Hadoop data lakes.
Slide 40: This slide displays Apache HBase Tool for Big Data Management in Hadoop.
Slide 41: This slide represents Apache Flume Tool for Big Data Management in Hadoop.
Slide 42: This slide showcases Apache Hive Tool for Big Data Management in Hadoop.
Slide 43: This slide shows Apache Pig Tool for Big Data Management in Hadoop.
Slide 44: This slide presents title for topics that are to be covered next in the template.
Slide 45: This slide displays checklist to implement the Hadoop framework in the organization.
Slide 46: This slide represents Deployment of Hadoop Framework in Company.
Slide 47: This slide showcases Single Node Hadoop Cluster or Pseudo Distributed Mode.
Slide 48: This slide shows Multi Node Hadoop Cluster or Fully Distributed Mode.
Slide 49: This slide presents methods to get data into the Hadoop framework.
Slide 50: This slide displays challenges of the Hadoop platform, including slow processing speed, no caching, etc.
Slide 51: This slide represents solutions to Hadoop challenges such as Spark, Flink, Hadoop Archives, etc.
Slide 52: This slide showcases title for topics that are to be covered next in the template.
Slide 53: This slide shows Comparison between Hadoop 2.x and Hadoop 3.x.
Slide 54: This slide presents comparison between Hadoop and Spark based on factors such as performance, cost, etc.
Slide 55: This slide displays title for topics that are to be covered next in the template.
Slide 56: This slide represents impacts of Hadoop on businesses, including big data analysis and queries.
Slide 57: This slide showcases impacts of Hadoop on the business, including data-driven decisions, better data access, etc.
Slide 58: This slide shows title for topics that are to be covered next in the template.
Slide 59: This slide presents 30-60-90 Days Plan for Hadoop Implementation.
Slide 60: This slide displays title for topics that are to be covered next in the template.
Slide 61: This slide represents roadmap for Hadoop implementation by displaying the tasks to be performed.
Slide 62: This slide showcases title for topics that are to be covered next in the template.
Slide 63: This slide shows dashboard of Hadoop implementation in the business by covering details of HDFS.
Slide 64: This slide presents dashboard for Hadoop and covering the details of NameNode heap, HDFS disk usage, etc.
Slide 65: This slide is titled as Additional Slides for moving forward.
Slide 66: This slide represents title for topics that are to be covered next in the template.
Slide 67: This slide shows what Hadoop is, including its various components such as storage layer or HDFS, batch processing engine, etc.
Slide 68: This slide presents Hadoop ecosystem by including its core module and associated sub-modules.
Slide 69: This slide displays disadvantages of Hadoop on the basis of security, vulnerability by design, etc.
Slide 70: This slide represents use cases of Hadoop in different sectors, including healthcare, telecom, finance, etc.
Slide 71: This slide showcases Icons for Hadoop(IT).
Slide 72: This slide represents Stacked Column chart with two products comparison.
Slide 73: This slide showcases Magnifying Glass to highlight information, specifications etc
Slide 74: This is an Idea Generation slide to state a new idea or highlight information, specifications etc.
Slide 75: This slide depicts Venn diagram with text boxes.
Slide 76: This slide shows Circular Diagram with additional textboxes.
Slide 77: This slide contains Puzzle with related icons and text.
Slide 78: This slide displays Mind Map with related imagery.
Slide 79: This is a Thank You slide with address, contact numbers and email address.

FAQs

Hadoop is an open-source framework for storing and processing large data sets on commodity hardware clusters. It is important because it provides a cost-effective and scalable solution to handle Big Data storage and analysis.

Hadoop offers numerous advantages, including scalability, flexibility, cost-effectiveness, and fault tolerance. It can handle large sets of data without the need for a large, expensive system and provides data protection with its data replication feature.

The core components of the Hadoop framework are Hadoop Distributed File System (HDFS) and MapReduce. HDFS provides storage for large sets of data, and MapReduce provides processing power to analyze and transform that data.

Hadoop MapReduce architecture processes a huge amount of information by dividing it into smaller chunks and processing each chunk on a different node in the Hadoop cluster. The MapReduce framework handles the processing and analysis of the data.

Implementing Hadoop in an organization provides numerous benefits, such as cost savings, scalability, and flexibility. Hadoop enables organizations to store and analyze large sets of data, providing valuable insights and aiding in data-driven decision-making.

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  1. 80%

    by Chauncey Ramos

    Appreciate the research and its presentable format.

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