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BDA Lc03 Basic algorithm design- Big Data Analytics product image (1)
BDA Lc03 Basic algorithm design- Big Data Analytics product image (1)

BDA Lc03 Basic algorithm design- Big Data Analytics

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Basic Algorithm Design: Big Data Analytics

Course Overview
This study material introduces the fundamentals of algorithm design in the context of Big Data Analytics, focusing on the Hadoop MapReduce framework and cloud computing. It provides practical examples and real-world applications to help you develop efficient algorithms for large-scale data processing.

Key Topics Covered:

  • Hadoop API: Learn the basics of the Hadoop API, including the roles of the Mapper, Reducer, and Partitioner in distributing and processing large datasets.
  • Java MapReduce APIs: Compare the old and new Java MapReduce APIs to understand how modern applications are built for scalability and performance.
  • Local Aggregation: Explore strategies for local aggregation to optimize data processing within individual nodes.

Practical Examples:

  • Word Count Examples: Review different versions of the Word Count algorithm (Baseline, Version 1.1, 2.0, and 3.0), demonstrating key improvements in efficiency.
  • In-Mapper Combining Design Pattern: Understand the in-mapper combining design pattern to reduce communication costs in large datasets.
  • Efficient Co-occurrence Counting: Learn techniques for improving co-occurrence counting and handling ambiguous terms in data processing.

Cloud Computing and Big Data:

  • Data Center Basics: Discover what a data center is and the critical components of its anatomy.
  • Amazon Web Services (AWS): Explore AWS services like Elastic MapReduce (EMR) and learn how to architect scalable data processing pipelines in the cloud.
  • Pricing Models: Understand the pricing for services like Elastic MapReduce and Amazon S3, and how they impact big data operations.

Real-World Applications:

  • Auto-Completion in Search: Learn how auto-completion is implemented using big data algorithms and how to compute results efficiently using the Pairs method.

This material is perfect for students and professionals looking to understand the algorithmic foundations of big data processing and cloud computing using Hadoop and AWS.

Why Choose This Material?

  • Hands-on examples with Word Count algorithm versions.
  • Detailed explanations of MapReduce and cloud computing.
  • Real-world applications like auto-completion and co-occurrence counting.
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