?What are the core components of the Hadoop ecosystem used in data analytics, and how do they contribute to processing big data

Core components include HDFS for storage and MapReduce for processing. HDFS distributes data, while MapReduce enables distributed computation.

?How does Hadoop Data Analytics address the scalability and performance challenges associated with processing large datasets, and what are some real-world use cases

Hadoop's distributed architecture allows it to scale horizontally, handling massive datasets efficiently. Use cases include log analysis, recommendation engines, and fraud detection in financial industries.

Hadoop Data Analytics for beginners

Track :

Computer Science

Lessons no : 10

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What will you learn in this course?

Master Hadoop architecture and ecosystem components for scalable big data analytics and processing
Apply Hadoop MapReduce and HDFS to efficiently manage and analyze large datasets in real-world scenarios
Utilize Hadoop tools like Hive and Pig for data querying, transformation, and reporting in big data projects

Implement data ingestion, storage, and retrieval techniques using Hadoop for effective data management
Optimize Hadoop performance and troubleshoot common issues in big data analytics environments
Design and execute end-to-end big data analytics workflows using Hadoop for business intelligence and data-driven decision making

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Lessons | 10