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I'm currently am interested in performing real time data analytics using real time aircraft performance data for predictive analysis. What tools and technologies could be used to implement such a system on research level?

TheShark
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  • Hi and welcome to Stack Overflow, please read how to create a [Minimal, Complete, and Verifiable example](https://stackoverflow.com/help/mcve) and also check [How to Ask Good Questions](https://stackoverflow.com/help/how-to-ask) so you increase your chances to get feedback and useful answers. – DarkCygnus Jul 04 '17 at 18:00
  • I just need to know what may the tools and technologies be, such as Sparks, Hive, HBase, Kafka could be used to do real time analytics? – TheShark Jul 04 '17 at 18:09
  • Asking that is Off topic for stack overflow, also your question is not really clear. Please read the links provided to know why, and if possible edit your question or remove it. – DarkCygnus Jul 04 '17 at 18:10
  • Ok, thank you ! Appreciate it. – TheShark Jul 04 '17 at 18:11

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For real time data analytics if i would be in your place i would have choose the following technologies

1) Kafka for real time Data Ingestion.

2) Spark Streaming for Streaming Processing

3) Spark ML for using machine learning algorithms (Prediction)

4) Apache Zeppelin for Visualization.

5) Data Storage you can use Hive or HDFS as per your needs

6) Ganglia for performance monitoring

Hope this Helps!!!...

Bhavesh
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  • Great ! Thank a lot. And a quick question again.. If I'm to strictly confine in to Hadoop eco-system, what are my possibilities ? Or does it strictly allow batch processing because of Map-Reduce ? – TheShark Jul 05 '17 at 14:21
  • I would say HADOOP is good for batch processing not very good for real-time processing, Spark will definitely serve you real-time streaming processing as well predictions (Machine Learning) all in one.. I say Spark will definitely provide great processing it will be a great choice!!!... – Bhavesh Jul 05 '17 at 15:56
  • Thank you for you comment ! – TheShark Jul 05 '17 at 16:35