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Big Data and Hadoop

Course Reviews

Track :

Computer Science

Lessons no : 67

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What will you learn in this course?
  • Understanding Hadoop architecture and its components for big data storage and processing using Java and MapReduce techniques
  • Implementing scalable Hadoop clusters by adding nodes to enhance performance and handle large datasets effectively
  • Applying Hadoop's raw data storage capabilities without predefined schemas for flexible big data management
  • Utilizing Hadoop's high availability features to ensure data resilience and fault tolerance in big data environments
  • Developing MapReduce programs to process and analyze large datasets efficiently in Hadoop ecosystems
  • Configuring Hadoop clusters for optimal performance, scalability, and data security in real-world big data projects
  • Troubleshooting common Hadoop issues related to data storage, processing, and cluster management
  • Integrating Hadoop with other big data tools and frameworks for comprehensive data analysis and business intelligence

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Lessons | 67
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Hadoop Big data Hadoop is an open source, Java based framework used for storing and processing big data. The data is stored on inexpensive commodity servers that run as clusters. ... Cafarella, Hadoop uses the MapReduce programming model for faster storage and retrieval of data from its nodes.Why is Hadoop used for big data? 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.