PySpark interview questions are typically asked at data interviews where companies evaluate candidates on their knowledge of big data tools and frameworks. PySpark is essentially an open-source Python API for Apache Spark. It is a distributed computing framework containing a set of libraries, used widely for large-scale and real-time data processing of data.
We’ve listed these common PySpark interview questions for freshers and experienced professionals to give you an idea of what to expect at your interview. Knowing how to use the framework is a big bonus in data and software engineering interviews and can tile the odds in your favor.
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Let’s go ahead and look at some common Panda interview questions asked in technical interviews.
Let’s go ahead and look at some common PySpark interview questions for freshers and experienced professionals. We’ll also look at some frequently asked questions around the Python framework.
Here’s what we’ll cover in this article:
PySpark is a big data framework tool for Python that helps to perform large-scale data processing, analysis, and manipulation. The tool offers many benefits to developers and data professionals, as it is equipped with an extensive library with functions capable of performing a wide range of tasks.
Some notable benefits of using PySpark include:
If you’re a fresher applying for a Python developer or data analyst role, being able to answer common PySpark interview questions is essential from an interview perspective.
To give you an idea of the type of questions asked, below are some common PySpark interview questions.
Experienced Python programmers, developers, data engineers, data scientists, and analysts are expected to know their way around PySpark. If you’re an experienced tech professional, you can expect quite a few PySpark interview questions for experienced professionals at tech interviews.
Here are the top questions to give you context about the questions to expect.
Practicing these above PySpark interview questions can help you prepare for your upcoming technical interview. For specific coding-related problems for technical interviews, check out our Problems Page.
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PySpark is an open-source, cluster-computing framework integrated into Apache Spark. It helps to perform large-scale data analysis and processing by offering a wide set of libraries that help developers and analysts to perform complex data-related functions.
Practicing PySpark interview questions is crucial if you’re appearing for a Python, data engineering, data analyst, or data science interview, as companies often expect you to know your way around powerful data-processing tools and frameworks (like PySpark).
Roles that require a good understanding of PySpark include – data engineering, data scientist, data analyst, big-data, and Python developer.
Some benefits of PySpark are – i) it offers a huge set of libraries, ii) it can be integrated with 3rd party applications, iii) it allows for swift data processing, up to 10x faster than traditional frameworks iv) it is easy to use.
Some industrial applications of PySpark include – Real-time media streaming, Financial analysis, processing healthcare information and analyzing healthcare records, in-app travel and e-commerce recommendations, and telecommunication services.
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