Project Ideas Hadoop MapReduce
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Project Ideas Hadoop MapReduce
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Our top experts train you in the following concepts:
- Understand about MapReduce
- Elements of MapReduce
- How does MapReduce works?
- Anatomy of MapReduce job
- Types of I/O formats [Combine FileInputFormat, Multiple outputs, NLINE input format]
- Brief description about classic MapReduce algorithms
- Design patterns of MapReduce
- MapReduce tools installation and configuration
- MapReduce Programming [JAVA Programming]
- MapReduce compression codec’s
MapReduce Processes:
- Dache: Dataware cache
- Data Centric Scheduling
- Data Exchange Policy
- Resource Provisioning
- Hybrid Clouds
Characteristics/functionalities of Hadoop MapReduce:
- It handles scheduling
- It allocate workers to map and reduce large datasets
- Data distribution is effectively performed
- It process synchronization process
- It can easily moves processes data
- It handles faults and errors
- Sorts, search, gathers, shuffle data
- Detects failures and easily restarts
- Everything done on top of a distributed file system
Hadoop MapReduce Concepts:
- Projection and selection
- Union, intersection and difference
- Joining and Time series
- Groupby and aggregation
- Distinct values
- Cross correlation
- Distributed task execution
- Parsing, validation and filtering
- Summing and counting
- Iterative message parsing
Typical Issues involving in MapReduce:
- Iterate over a large number of records
- Extract something of interest from each
- Shuffle and sort intermediate results
- Aggregate intermediate results
- Generate final output
Application concepts in MapReduce:
- ETL (Extraction, Translation and Load)
- Machine Learning (Supervised and Unsupervised)
- Data Validation
- Performance Testing
- Data Querying
- Similar Item Detection
- Data Warehousing
- Information Retrieval
- Log Analysis
List of Project Ideas Hadoop MapReduce:
- Big Data Mining and Gathering Pipelines for Customer Relationship Management (CRM) Using Open-source
- Small File Storage Based on Optimized MapFile in Hadoop Paradigm
- Design Hadoop Platform by Seismic Data Analysis and Processing
- Processing and Analysis of Higher Institution Academic Data Using Big Data Tools
- Improved MapReduce Based SCAM (Scenario Based Clustering Algorithm for MANET) Algorithm to Detect Plagiarism on Big Data
- Run-Time Capacity Allocation Using Game Theoretic Technique in MapRaduce
- Load Massive Data Over HBase (Hadoop Database) on Distributed Database Frameworks
- Roadside Air Quality Control Monitoring and Control Using Traffic Regulation Framework Based on Agent
- Schedule Cross Platform Resource for MapReduce and Spark on Yet Another Resource Negotiator (YARN)
- Relevant Subspace Based Scalable Mining Algorithm for Contextual Outliers
- MapReduce for Analyze Geographical Based Distributed Big Data Processing
- Enhance Vehicle Controls Using MapReduce Based Approach on Big Traffic Events
- Predict Data Attacks Using LSTM (Long Short Term Memory) Based Memory Profile in Distributed Big Data Frameworks
- Heterogeneous Hadoop Frameworks Using Adaptive Scheduling Algorithm
- Investigate Hadoop Progress Control Based Adaptive Delay Scheduling Algorithm
