Why Apache Spark has Become Hot Topic of Discussion in Big Data Forums?
The platform will be highly useful for enterprises in accurate data management and data interrogation. With a wide variety of options available to manage big data, this is the responsibility of Company’s owner to select the correct platform for assured results – Hadoop, spark or Hive.
Will Apache Spark Replace Hadoop Big Data Platform?
Hadoop hive architecture is traditional data management platform to run MapReduce jobs where whole task can take hours to complete. Spark is especially developed to work on top of Hadoop for quick data analysis and real time streaming jobs.
The biggest advantage of using this platform is that interactive queries can be handled within seconds only. Hadoop can be integrate with both either Apache Spark or traditional MapReduce jobs. Apache spark cannot be considered replacement but enhancement over Hadoop. It could be used as alternative whenever required by the organization.
Spark or Hadoop – What to choose?
This is really a tough question what to choose between Apache Spark and Hadoop big data platform. Spark uses RAM instead of network so it is relatively fast and efficient. But it demands for dedicated hardware so increases overall cost of a project. We cannot decide based on assumption but several factors are there to force you to decide on the right platform.
Apache spark is designed to work with Hadoop components like HDFS and hive. Spark should be taken as advantage as it would increase overall capability of Hadoop stack. It is not necessary that to use Apache Spark you should learn Hadoop. Both have different capabilities and different uses. it depends on developers what to choose for better outcomes.
Spark can be integrated with multiple frameworks other than Hadoop big data so it is obvious that it will gain more popularity in near future soon. In Spark you can quickly write code in Java, Python and Scala so it is easy to programmers to get hands-on experience over Spark platform.
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