1) What is Apache Spark?
spark interview questions is an open-source, easy to use, supple, big data framework or united analytics locomotive used for large-scale data dispensation. It is a cluster calculation framework for real-time processing. Apache Spark can be set upon Hadoop, standalone, or in the cloud and capable of measuring diverse data sources, including HDFS, Cassandra, and others. Apache Spark provides an border for entire programming bunches with implicit data correspondence and fault tolerance.
Apache Spark is one of the most fruitful projects in the Apache Software Foundation. It is changed as the market leader for Big Data processing. Nowadays, many administrations run Spark on clusters with thousands of nodes. Some big companies which have assumed Apache Spark are Amazon, eBay, Yahoo etc.
2) What type of big data problems Apache Spark can be resolved?
As we know that Apache Spark is an open-source big data outline. It provides a communicative APIs to enable big data professionals to perform streaming and batching competently. It is designed for fast totalling and also provides a faster and more general data processing stage engine.
Apache Spark was industrialized at UC Berkeley in 2009 as an Apache project termed “lighting fast cluster computing”. It can allocate data in a file system across the cluster and processes that data in equivalent.
3) What was the need for Apache Spark?
Many general-purpose cluster calculation tools in the market, such as Hadoop MapReduce, Apache Storm, Apache Impala, Apache Giraph and many more. But each one has some confines in its functionalities.
Apache Spark comes into being. It is a powerful open-source engine that offers interactive processing, real-time stream processing, graph dispensation, in-memory processing and batch processing. It provides a very fast speed, ease of use, and a standard interface concurrently.
4) Which limitations of MapReduce Apache Spark can remove?
Apache Spark was developed to overcome the confines of the MapReduce cluster computation example. Apache Spark saves things in memory, whereas MapReduce keeps shambling things in and out of disk.
Next is a slope of few things which are better in Apache Spark:
- Apache Spark keeps the hoard data in memory, which is useful in reiterative algorithms and can easily be used in machine learning.
- Apache Spark is easy to use as it knows how to function on data. It supports SQL queries, cyclosis data as well as graph data processing.
- Spark doesn’t need Hadoop to route. It can run on its own using other packings like Cassandra, S3, from which Spark can read and write.
- Apache Spark’s speed is very high as it can run agendas up to 100 times faster in-memory or ten times sooner on disk than MapReduce.
5) list all of the languages supported by Apache Spark?
Apache Spark is written in Scala language. It delivers an API in Scala, Python, Java, and R languages to collaborate with Spark.
7) Name most important categories of the Apache Spark that comprise its ecosystem?
Following are the three very important categories in Apache Spark that encompass its ecosystem:
- Core Components:Apache Spark ropes five main core mechanisms. These are namely Spark Core, Spark SQL, Spark Streaming, Spark MLlib, and GraphX.
- Cluster Management:Apache Spark can be in the subsequent three environments. These are known as the Standalone cluster, Apache Mesos, and YARN.
- Language support:We can integrate Apache Spark with some dissimilar languages to make applications and perform analytics. These tongues are generally Java, Python, Scala, and R.
8.What is the difference between Apache Spark and Hadoop?
The key changes between Apache Spark and Hadoop are specified below:
- Apache Spark is designed to jobwise handle real-time data, whereas Hadoop is designed to workwise handle batch dispensation.
- Apache Spark is a low latency calculation and can process data interactively, whereas Hadoop is a high dormancy calculation framework, which does not have a much interactive mode.
6) What do you understand by YARN?
Just like in Hadoop, YARN is one of the key topographies in Apache Spark, which is used to provide a central and resource organization platform to deliver scalable processes across the cluster. Spark can run on YARN, as the same way Hadoop Map Reduce can track on YARN.