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Top Apache Spark Interview Questions You Should Prepare In 2021

2018 has become a large year data – years when large data and analytic makes extraordinary progress through innovative technology, decision making driven by data and analytic centric results. Revenues around the world for large data and business analytic (BDA) will grow from $ 130.1 billion in 2016 to more than $ 203 billion in 2021 (IDC source). Prepare with the question of the Apache Spark interview questions to get the advantage in the growing large data market where global and local, large or small companies, looking for large quality data and hadoop experts.

As a large professional data, it is important to know the right keywords, studying the right technology and preparing the right answer for the spark interview questions that are usually requested. With questions and answers around Spark Core, Spark Streaming, Spark SQL, Graphx, MLLIB, among other things, this blog is your gateway to the next spark job.

Apache triggers questions and interview answers

1. Compare Hadoop and Spark.

Single cook cooking dishes is ordinary computing. Hadoop is a few chefs cooking dishes into pieces and let each cook the part.

Each cook has a separate stove and a food rack. Cook the first cooking meat, the second cook cooking sauce. This phase is called “map”. The end of the main cuisine assembles complete dishes. This is called “subtract”. For Hadoop, the cooks are not allowed to keep the stove between operations. Every time you make a particular operation, the cook places the results on the shelf. This slows everything.

For spark, the chef is allowed to keep things on the stove between operations. This speeds up everything. Finally, for Hadoop recipes are written in irrational languages ​​and are difficult to understand. For splashes, recipes are well written.

2. What is Apache Spark?

Apache Spark is an open-source cluster computing framework for real-time processing.

It has an open-source community that develops and is the most active Apache project today.

Spark provides an interface for programming all clusters with implicit data parallelism and error tolerance.

Spark is the most successful project in the Apache software foundation. Spark has clearly evolved as a market leader for large data processing. Many organizations run clusters with thousands of nodes. Today, Spark is being adopted by major players such as Amazon, eBay, and Yahoo!

3. Explain the main features of Apache Spark.

The following are the main features of Apache Spark:

Polyglot

Speed

Multiple format support

Lazy evaluation

Real time calculation

Hadoop Integration.

Machine learning

Let’s look at these features in detail:

Polyglot: Spark provides high-level APIs in Java, Scala, Python and R. Code Spark can be written in one of these four languages. It provides shells in Scala and Python. Scala Shell can be accessed through ./bin/spark-shell and python shell through ./bin/pyspark of the installed directory.

Speed: Spark runs up to 100 times faster than Hadoop MapReduce for large-scale data processing. Spark is able to reach this speed through a controlled partition. It manages data using a partition that helps parallelize distributed data processing with minimal network traffic.

Various formats: Spark supports various data sources such as Parquet, JSON, Hive and Cassandra. API data sources provide a mechanism that can be accessed to access structured data even though Spark SQL. The data source can be more than a simple pipe that changes the data and pulls it into a spark.

Lazy evaluation: Apache Spark delay evaluation until it is absolutely necessary. This is one of the key factors that contribute to its speed. For transformation, the spark adds to the computing dag and only when the driver asks for some data, whether this dag is truly executed.

Real time calculation: Splash calculation is real time and has fewer latency because of its calculations in memory. Spark is designed for massive scalability and spark teams have documented system users who run production clusters with thousands of nodes and support several computing models.

Integration of Hadoop: Apache Spark provides smooth compatibility with Hadoop. This is a good boon for all major data engineers who start their careers with Hadoop. Spark is a potential replacement for the Mapreduce Hadoop function, while the spark has the ability to run on the existing Hadoop cluster using thread for resource scheduling.

Machine Learning: MLLIB Spark is a machine learning component that is useful in terms of large data processing. It eradicates the need to use several tools, one for processing and one for machine learning. Spark provides data engineers and data scientists with a powerful and united machine that is fast and easy to use.

4. What language is supported by Apache Spark and which is the most popular?

Apache Spark supports the following four languages: Scala, Java, Python and R. Among these languages, Scala and Python have interactive commotion for sparks. Scala Shell can be accessed via ./bin/spark-shell and python shell through ./bin/pyspark. Scala is the most widely used among them because the spark was written in Scala and it was the most popular used for sparks.

5. What are the benefits of splashing in MapReduce?

Spark has the following benefits above MapReduce:

Because of the availability of processing in memory, the spark implements processing around 10 to 100 times faster than Hadoop MapReduce while MapReduce utilizes the storage of persistence for one of the data processing tasks.

Unlike Hadoop, Spark provides a default library to do some tasks from the same core as batch processing, steaming, machine learning, interactive SQL queries. However, Hadoop only supports batch processing.

Hadoop relies heavily on the disk while the spark promotes caching and storing data in memory.

Spark is able to calculate several times on the same dataset. This is called recurrent calculation while no iterative computing applied by Hadoop.

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