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Apache Apex - is an open source enterprise grade unified stream and batch processing platform. It is used in GE Predix platform for IOT. What are the key differences between these 2 platforms?


  1. From a data science perspective, how is it different from Spark?
  2. Does Apache Apex provide functionality like Spark MLlib? If we have to built scalable ML models on Apache apex how to do it & which language to use?
  3. Will data scientists have to learn Java to built scalable ML models? Does it have python API like pyspark?
  4. Can Apache Apex be integrated with Spark and can we use Spark MLlib on top of Apex to built ML models?

1 Answer

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by (6.8k points)
  1. Apache Apex an engine for processing streaming data. Some others which try to achieve the same are Apache Storm, Apache Flink. The differentiating factor for Apache Apex is: it comes with built-in support for fault-tolerance, scalability and focus on operability which are key considerations in production use-cases.
  2. Comparing it with Spark: Apache Spark is actually a batch processing. If you consider Spark streaming (which uses spark underneath) then it is micro-batch processing. In contrast, Apache apex is a true stream processing. In a sense that, the incoming record does NOT have to wait for the next record for processing. Record is processed and sent to the next level of processing as soon as it arrives.
  3. Currently, work is under progress for adding support for integration of Apache Apex with machine learning libraries like Apache Samoa, H2O Refer
  4. Currently, it has support for Java, Scala.
  5. For Python, you may try it using Jython. But, I haven't not tried it myself. So, not very sure about it.
  6. Integration with Spark may not be a good idea considering they are two different processing engines. But, Apache apex integration with Machine learning libraries is under progress.
  7. If you have any other questions, requests for features you can post them on the mailing list for apache apex users:

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