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As an industry renowned Big Data professional, it is essential to know all of the terms and technologies, including Apache Spark, one of the most powerful and in-demand technologies in Big Data.
Apache Spark, as we know, is currently a booming technology. Big Data is being processed more quickly with growing industry demand, with Apache Spark gaining enormous momentum for business admission. Apache Spark developers are increasingly demanded to validate their expertise in implementing Spark’s best practices – to build complex big data solutions to support their drive for quicker Big-Data processing.
It is therefore essential to know all aspects of Apache Spark as well as Spark interview questions. Thus, this blog certainly will help you. This blog covers all aspects of Spark, which can also be frequently asked questions from Spark Interview.
Top 10 Frequently asked Apache Spark Interview Questions and Answers
Preparation is vital in any big data job interview to reduce nervous energy. Each candidate rears the face-to-face big data job interview irrespective of his/her Big Data expertise and skills. It is impossible to predict precisely the questions in any Big Data or Spark Developer job interview. These questions and answers to the Apache spark interview could help you better prepare for these interviews.
Q1) What is Apache Spark?
- Apache Spark is a real-time cluster computing open-source framework.
- It is the most active Apache project at present and has a flourishing Open Source community.
- Spark offers an interface for programming all clusters with the implicated data parallelism and fault tolerance.
Q2) Why Apache Spark?
Ans: Spark is the data processing platform of the third generation. Big data solution is unified for all major data processing problems such as batch processing, interacting, and streaming. It can alleviate many issues with big data.
Q3) What’s the critical difference between a map and a flat map?
Ans: a) Map: one row of input to one row.
- b) Flat map – one input row or several output rows
Q4) Explain the key features of Apache Spark?
- Support for various programming languages – You can write spark code in any one of four languages of programming, namely, Java, Python, R, and Scala. The programming languages include high-level APIs. Apache Spark also offers Python and Scala shells. You can access the Python shell via the ./bin/pyspark directory. Still, you must go to the .bin/spark-shell directory when you access the Scala shell.
- Lazy Assessment – Apache Spark uses the lazy assessment concept to delay the evaluation until it is mandatory.
- Machine Learning – Apache Spark’s MLib machine learning component is useful for big data processing. It eliminates the need to use different processing and machine learning engines.
- Support for Multiple Formats – Apache Spark offers multiple data sources, including Cassandra, Hive, JSON, and Parquet. A plug-in mechanism to access structured data via Spark SQL is available in the Data Sources API. These data sources are more than simple pipes that can convert data and pull it into Spark.
- Real-Time Computing – Spark is specifically designed to fulfill large scalability needs. Spark’s computation is real-time and has less latency.
- Speed – Spark is up to 100 times faster than Hadoop MapReduce for large-scale data processing. With controlled portioning, Apache Spark can achieve this tremendous speed. For general purposes, the distributed cluster computing framework manages data with partitions that simultaneously parallelize distributed data with minimum traffic in the network.
- Hadoop Integration – Spark provides smooth Hadoop connectivity. In addition to being a potential substitute for Hadoop MapReduce functions, Spark can use YARN to plan resources to run on top of the existing Hadoop cluster.
Q5) What is RDD?
Ans: Resilient Distributed Datasets (RDD) is the primary core abstraction of Spark. RDD is a partitioned data collection that fulfills these characteristics. The common RDD properties are immutable, distributed, lazily evaluated, and catchable.
Q6) What is Immutable?
Ans: Once a value is created and assigned, one cannot change this property. Spark can be unaltered by default, as well as updates and changes are not permitted. It is not immutable to collect data, but it is firm to the color data value.
Q7) What is Spark core?
Ans: Spark core is the core of the Spark project as a whole. It offers all kinds of functions such as dispatching tasks, programming, and operating inputs. Spark uses the special Resilient Distributed Dataset (RDD) data structure. The RDD is defined and manipulated by API. Spark Core is an execution engine distributed with all functions attached to it.
Q8) What are the advantages of using Spark with Apache Mesos?
Ans: It gives scalable division and dynamic divisions between Sparks and other big-data frameworks in different Spark instances.
Q9) Define what a partition is?
Ans: As the name implies, a partition is like ‘split’ in MapReduce, a smaller and more logical data division. Partitioning is the logical data unit process that allows to speed up data processing. All things are a partitioned RDD in Spark.
Q10) What are the udfs, and how to use them?
Ans: UDFs are user-defined functions used to modify all rows in specific columns, such as timestamp today and week conversion.
With the Spark interviews becoming increasingly difficult, it is time for you to become smarter with the latest interview-cracking skills. These questions from Spark’s interview will help you prepare for the Spark Interviews. You can answer questions on the key features of the Spark, RDD, Spark engine, MLib, GraphX, Spark Driver, and Spark Ecosystem. Also, it helps you land your dream job quickly as a Spark Developer and Spark programmer. By using the following interview questions of Apache Spark, build your trust and excel up your upcoming interview of Spark.
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