IEEE Hadoop Projects
IEEE Hadoop Projects we guide all sub domains like BIG DATA , MAP REDUCE ,SCHEDULING etc…
- Hadoop Projects chosen by engineering students (UG & PG), sometimes some of the research peoples can select their objective based on hadoop
- Big data process should enlarge the concepts of hadoop
- Hadoop projects are processed with 3v’s such as volume, velocity and variety
- Cloud computing should cover hadoop concepts which could be processed by mapreduce framework
Hadoop Architecture
IEEE Hadoop Projects Benefits:
- Scalable
- Cost Effective
- Flexible
- Resilient to failure
- Fast
IEEE Hadoop Projects Features:
- Mapreduce API Support
- Centralized Authorization
- HDFS DARE
- Audit Optimization and governance
- Cascading Support
- Apache Storm
- Self Contained
Challenges of hadoop:
- Data Storage and Modeling
- Data Movement
- Metadata
- Data Access and processing
Hardware Considerations of hadoop:
- Prefer cheap commodity hardware
- More memory the better
- Hadoop deal with graceful of Failure, because it is planned and expected
Methods Involved in Hadoop:
- Load Charcterization
- Shuffle Operations
- Hadoop virtualization
- Apache Mahout
- Data Clustering
- Hadoop Scheduling
- Block Placement Subsection
- Replica Policies
Hadoop Security Algorithms:
- Symmetric Cryptographic algorithms
- Asymmetric Cryptographic Algorithms
- Hash algorithms
