Most Commonly Asked Apache Spark-Based Conceptual Questions in Data Engineer Interviews.
Having Clarity On Apache Spark-based Concepts => Excellent Data Engineer!!
When tackling immense or complex challenges in our lives, we need to follow Spark’s approach i.e. breaking down the problems into manageable chunks and tackling them individually.
Like Spark, we can slice our obstacles into pieces and address them systematically.
In this rapidly evolving digital era, The demand for skilled data engineers is increasing day by day. The most important skill a data engineer should have is handling and processing a large amount of data.
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Apache Spark is the framework used for handling large amounts of data to process with different techniques and many other tasks that Apache Spark performs efficiently in data engineering.
Therefore, A data engineer should possess excellent knowledge of this framework and its underlying working principles and In-Out of the framework to excel in the role of a data engineer in data engineering.
I hope by this time you will understand how important becoming Excel at Apache Spark is!!! to Excel in the role of a data engineer.
The reasons mentioned above might be the reason, these days almost all interviewers who are going to hire data engineers are much more focused on spark concepts to check whether a candidate is good at basic understanding of spark concepts or not.
Revise the entire PySpark scripting as in real-world problem-solving in less time by clicking on The Essential PySpark Cheat Sheet for Data Engineers.
In this article, I’m going to share questions based on spark concepts that I faced during data engineer interviews. Answers to these questions will vary based on individuals’ experiences and their understandings of their usages in real-world projects.
Explain the main components of Apache Spark Architecture and how they interact with each other.
Why Apache Spark is an efficient data processing framework as compared to Hadoop. Why?
What exactly will happen if you suddenly stop the spark context?
What is RDD? Explain in detail about it.
Explain the concepts of Serialization, De Serialization, and Kryo serialization.
Explain what is execution plan is, how it works and what are logical and physical plans.
What are Caching and persistence in spark?
What is Lazy Evaluation? Explain in detail with examples.
Are you aware of what is meant by predicate pushdown and upsert?
Explain with simple examples, what is broadcasting, broadcasting variables, and broadcasting joins.
Define the terms Skewing and Salting. What is the difference between them?
Explain concepts of Bucketing and Partitioning with real-world examples, and when to use each in specific.
Brief me about spark optimization techniques.
What are the modes in which a spark application can be deployed? Specify the reason Why to choose a particular mode.
Explain with examples about Transformations and actions in Spark.
What is Lineage in Spark and what is its significance?
How Spark Manages its Memory.
- What are Accumulators in Spark?
- What is AQE in spark?
- What will you do if a given data is skewed data?
- Explain the types of functions in Spark.
- Define DAG in Apache Spark. How does it work?
- What are the limitations of Apache Spark?
I strongly believe these are the conceptual questions that are more often asked in data engineering interviews. If you have a passion for becoming a data engineer, this article will be helpful for you. Once you fully understand these concepts, I’m sure you will excel as a data engineer.
Python Coding Questions for Data Engineer Interview Part-I (Easy Level)
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