WHAT IS APACHE SPARK?
Spark is an engine whose general purpose is to distribute data that is suitable to use in a huge or wide range of circumstances. There are libraries for SQL, machine learning, graph computation and also stream processing and all these are on the top of the spark core data processing engine, which can also be used together for any application. Different programming languages are being supported by a spark which is included with Java Script Training, Python, Scala and R. Application developers. Many tasks are mostly very frequently associated with the spark. Due to the tough competition in the market, this became essential to know each and every concept for the Apache Spark so that interview can be cleared.
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WHAT IS THE PURPOSE BEHIND USING SPARK?
Engineers get help out from spark, as it is provided by the ability to abstract data access complexity. Whatever kind of data has been stored in it, Spark does not care about the storage of data. It also helps to enable the near and real-time solution at a web-scale, which can be like the pipelined machine-learning flow of work.
INTERVIEW QUESTIONS AND ANSWERS FOR APACHE SPARK
To analysis which is capable of handling and is capable engage to get the position in this sector, freshers have to go through some interview procedure.
1) LIST SOME MAIN FEATURES OF APACHE SPARK
A) Features that are present in Apache Spark are as followed;
-Integration with Hadoop
-It includes an interactive shell of language, which is called Scala, which is used to write on.
-Robust Distributed of data is set a cached between computer nodes in between cluster.
It often offers many various kinds of analytical tools for real-time analysis, possessor of graphic and also analysis of interactive query.
2) GIVE THE DEFINITION FOR RDD
A) RDD or Resilient Distribution Datasets that represents a fault-tolerant set of those elements that are operated in parallel. Those data which are present in the RDD section are distributed and are immutable. There are basically two types of RDDs -Parallelized collection -Hadoop datasets
3) USAGE OF THE SPARK ENGINE
A) The main work or usage for Spark Engine was to plan, distribute and also monitor data applications that are present in the cluster.
4) DIFFERENT TYPE OF OPERATIONS WHICH ARE SUPPORTED BY RDD SUPPORT
A) Basically, there are many operations that support their respective program, but they go with the best options or methods only. Like that, there are two types of operations that are supported by RDD are Transformations and actions.
5) EXPLAIN THE TERM ACTIONS
A.) Action is a method in a spark that makes it possible to bring out the data from RDD to the machine in locals. The function of actions is Reduce () and Take ().
Reduce () works only at that time when action is one by one until the one, and last value leaves while Local () can accept all RDD values to the key of local.
6) WHAT WORK DOES SPARK DRIVER DO?
A.) Spark Driver is a program that runs on the main node of the device announces the transformations and the actions which are there on data RDD.
7) LIST DOWN SOME MOST FREQUENTLY USED SPARK ECOSYSTEMS
A.) Some ecosystems which are used most frequently are;
-For the developers, Spark SQL
-To process with the live data streams
-For the generation and compute of Graph
-For the promotion of R programming in the Spark Engine.
8) EXPLANATION OF SPARK STREAMING
A.) Spark Streaming is a process for the extension to the Spark API, which allows live data streaming. It is similar to the batch processing for diving data into the streams like batches which is in the term of input data.
9) WHAT WORK DOES MLlib DO?
A.) MLlib is supported by Spark, which is a scalable Machine for the purpose of learning library. The basic objective behind this is to make Machine learning in an easy way sailable with the algorithms with commonly learning and also helps in using cases like regression filtering, clustering and dimensional in reduction.
10)WHAT ADVANTAGES DOES SPARK HAS OF USING OVER MapReduce
A.) There are many advantages for using Spark over MapReduce are as followed;
-Spark can work many more times faster data processing than MapReduce because of the availability of in-memory processing.
11)DESCRIBE SPARK EXECUTOR
A.) The executor is a spark process that performs predictions and saves the data in the workplace. The final task from Spark context is to send for executions to the executor.