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Hadoop Platform and Application Framework Course

Hadoop Platform and Application Framework Course Course

An excellent course for professionals seeking to understand and apply Hadoop and Spark frameworks in real-world scenarios. The course offers a balanced mix of theoretical knowledge and practical exerc...

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9.7/10 Highly Recommended

Hadoop Platform and Application Framework Course on Coursera — An excellent course for professionals seeking to understand and apply Hadoop and Spark frameworks in real-world scenarios. The course offers a balanced mix of theoretical knowledge and practical exercises.

Pros

  • Hands-on experience with Hadoop and Spark
  • Comprehensive coverage of Hadoop ecosystem components
  • Suitable for beginners with no prior experience
  • Flexible schedule with self-paced learning

Cons

  • Requires a system capable of running virtual machines for hands-on exercises
  • Limited focus on advanced topics like Hadoop cluster optimization

Hadoop Platform and Application Framework Course Course

Platform: Coursera

What will you in the Hadoop Platform and Application Framework Course

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  • Understand the architecture and components of the Hadoop ecosystem

  • Gain hands-on experience with Hadoop and Spark frameworks

  • Learn to use the Hadoop Distributed File System (HDFS) for data storage

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  • Implement data processing tasks using the MapReduce programming model

  • Explore tools like Apache Pig, Hive, and HBase for big data analysis

Program Overview

1. Hadoop Basics
Duration: 2 hours

  • Introduction to big data concepts and the Hadoop ecosystem

  • Overview of Hadoop stack and associated tools

  • Hands-on exploration of the Cloudera virtual machine

2. Introduction to the Hadoop Stack
Duration: 3 hours

  • Detailed examination of HDFS components and application execution frameworks

  • Introduction to YARN, Tez, and Spark

  • Exploration of Hadoop-based applications and services

3. Introduction to Hadoop Distributed File System (HDFS)
Duration: 3 hours

  • Understanding the design goals and architecture of HDFS

  • Learning about read/write processes and performance tuning

  • Accessing HDFS data through various APIs

4. Introduction to MapReduce
Duration: 7 hours

  • Learning the MapReduce programming model

  • Designing and executing MapReduce tasks

  • Exploring trade-offs and performance considerations in MapReduce

5. Introduction to Spark
Duration: 9 hours

  • Understanding the Spark framework and its integration with Hadoop

  • Exploring Spark’s core components and functionalities

  • Hands-on experience with Spark for big data processing

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Job Outlook

  • Data Engineers: Enhance skills in big data processing using Hadoop and Spark

  • Data Analysts: Gain proficiency in handling large datasets and performing complex analyses

  • Software Developers: Learn to build scalable applications using Hadoop ecosystem tools

  • IT Professionals: Understand the infrastructure and management of big data platforms

  • Aspiring Data Scientists: Build a strong foundation in big data technologies

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