Apache Hadoop is something that will always pop up whenever a big data term is used. It has almost become a mandatory piece when dealing with Big Data. There is no doubt that Hadoop is an excellent choice, but it does have some inherent aspects that put a doubt in developers' minds when the choice has to be made, especially when big data and its processing is ever increasing in any enterprise, obviously due to changing business dynamics. Some of its pointed disadvantages are Hadoop's complexity and the way it actually does execution. Due to these reasons, there have been some recent innovations to simplify Hadoop processing further, and some of these simplifications have been brought in by the advent of Pig scripts and Apache Spark.

Pig scripts provide a good alternate to simplify MapReduce activity with pig Latin language, while still enabling non-Java developers to perform MapReduce via a simpler programming style.

Apache Spark streaming, on the other hand, has simplified the querying mechanism via programming languages, such as Scala, Java, and Python. If we might have observed in the examples covered, HIVE is good at querying HDFS data, but when it comes to joining one table with another, it kicks into more complex MapReduce jobs with high probability of failure. Hence, the HIVE MapReduce has been deprecated as the same action can be performed in a very optimized way with Apache Spark and Apache Tez.