Projects in Hadoop in Thrissur | Hadoop Small Projects | Hadoop Projects in Thrissur


Course Duration: Minimum 10 Days
Curriculum Delivery: Online / Offline
Timings & Schedules: Flexible time shift as per the convenience of candidates
Certification Authority: AIT Issued Seminar Completion Certificate
Seminar Index: Seminar Paper Research, Seminar Presentation Training, Documentation, Demo viva Section, PPT Presentation

About The Seminar

ATEES Industrial Training seminar paper involves everything that one needs to be successful in their respective career. The courses are designed to help candidates comprehend the topic and incorporate apt skills in their professional writing. We welcome questions from our candidates on how we can help them with the seminar preparation. In addition, our support team will guide one with seminar topics including quality discussion on the logical idea and technologies involved. The seminar paper is presented in four major sections: Introduction; Background; Analysis; and Conclusion. Each sub-sections will be researched and examined in detail in the subsequent pages.

A network of numerous computers can be used to address problems involving enormous volumes of data and processing thanks to the open-source software tools in Apache Hadoop. It offers a software framework for the MapReduce programming model-based distributed storage and processing of massive data. In general, the following topics will be covered: modeling with Hadoop, Hadoop strategies, the Hadoop process, and Hadoop overview. By looking at a few of the presentations that make up the section, you can see how each element interacts with the section as a whole. Before the data is cross-checked utilising a range of technology-based research techniques, each component of a topic is specified.

  • Why was created?
  • Features
  • Conclusion

No. Seminar Topics
1 Big Data Analysis in Healthcare Apache Hadoop, Apache spark and Apache Flink.
2 A Data Aware Scheme for Scheduling Big Data Applications with SAVANNA Hadoop
3 Predictive Schedule and Prefetching Mechanism for Improved Performance in Hadoop MapReduce Clusters.
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