what is difference between hadoop and spark -


as spark growing in market nowadays can see spark’s major use cases on hadoop like:

  1. iterative algorithms in machine learning
  2. interactive data mining , data processing
  3. spark apache hive-compatible data warehousing system can run 100x faster hive.
  4. stream processing: log processing , fraud detection in live streams alerts, aggregates , analysis
  5. sensor data processing: data fetched , joined multiple sources, in-memory dataset helpful easy
    , fast process.

my question is:

  1. is spark going replace hadoop in upcoming days?
  2. hadoop work concurrently while spark runs in parallel?(is true?)

spark differ hadoop in sense let integrate data ingestion, proccessing , real time analytics in 1 tool. spark map reduce framework differ standard hadoop map reduce because in spark intermediate map reduce result cached, , rdd(abstarction distributed collection ii fault tollerant) can saved in memory if there need reuse same results (iterative alghoritms, group , etc etc).

my answer superficial , not not answer question completly point out of main difference (much more in reality) spark , databricks official site documented , question answered there :

https://databricks.com/spark/about

http://spark.apache.org/faq.html


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