10+ Big Data Developer Interview Questions and Answers

OOP in Python is based on four pillars: Encapsulation, Abstraction, Inheritance, and Polymorphism.

• Encapsulation: Bundling data and methods that operate on the data within one unit (class). Example: class Car with attributes and methods.

• Abstraction: Hiding complex implementation details and showing only essential features. Example: using a simple interface for a complex system.

• Inheritance: Mechanism where a new class derives from an existing class, inheriting its attributes an...read more

Reading a CSV file involves loading data, transforming it, and processing it for analysis or storage.

• Use libraries like Pandas in Python: `import pandas as pd; df = pd.read_csv('file.csv')`.

• Data cleaning: Remove duplicates with `df.drop_duplicates()`.

• Data type conversion: Convert a column to datetime with `df['date'] = pd.to_datetime(df['date'])`.

• Filtering data: Use conditions like `df[df['age'] > 30]` to filter rows.

• Aggregation: Group data with `df.groupby('category').sum()`...read more

RDDs, DataFrames, and Datasets are core abstractions in Apache Spark for handling large-scale data processing.

• RDD (Resilient Distributed Dataset): Immutable distributed collection of objects, fault-tolerant, and supports parallel processing.

• Example: val rdd = spark.parallelize(Seq(1, 2, 3, 4)) creates an RDD from a sequence.

• DataFrame: Distributed collection of data organized into named columns, similar to a table in a relational database.

• Example: val df = spark.read.json('dat...read more

Python lambda functions are anonymous functions defined using the lambda keyword, useful for short, throwaway functions.

• Lambda functions can take any number of arguments but can only have one expression.

• They are often used in conjunction with functions like map(), filter(), and reduce().

• Example: square = lambda x: x ** 2; print(square(5)) outputs 25.

• Lambda functions can be used to sort lists: sorted_list = sorted(my_list, key=lambda x: x[1]).

AWS Glue can process petabytes of data per hour.

• AWS Glue can process petabytes of data per hour, making it suitable for large-scale data processing tasks.

• It can handle various types of data sources, including structured and semi-structured data.

• AWS Glue offers serverless ETL (Extract, Transform, Load) capabilities, allowing for scalable and cost-effective data processing.

• It integrates seamlessly with other AWS services like S3, Redshift, and Athena for data storage and analys...read more

Distribution in Spark refers to how data is divided across different nodes in a cluster for parallel processing.

• Distribution in Spark determines how data is partitioned across different nodes in a cluster

• It helps in achieving parallel processing by distributing the workload

• Examples of distribution methods in Spark include hash partitioning and range partitioning

Pandas, NumPy, and Matplotlib are essential Python libraries for data manipulation, numerical analysis, and visualization.

• Pandas: A powerful data manipulation library that provides data structures like Series and DataFrame for handling structured data.

• Example: Importing pandas and creating a DataFrame: ```python import pandas as pd data = {'Name': ['Alice', 'Bob'], 'Age': [25, 30]} df = pd.DataFrame(data) print(df) ```

• NumPy: A library for numerical computing that provides sup...read more

Hadoop is an open-source framework for distributed storage and processing of large data sets, while HDFS is the Hadoop Distributed File System used for storing data across multiple machines.

• Hadoop is designed to handle big data by distributing the data processing tasks across a cluster of computers.

• HDFS is the primary storage system used by Hadoop, which breaks down large files into smaller blocks and distributes them across multiple nodes in a cluster.

• HDFS provides high faul...read more

Spark is a fast and general-purpose cluster computing system, while PySpark is the Python API for Spark.

• Spark is a distributed computing system that provides an interface for programming entire clusters with implicit data parallelism and fault tolerance.

• PySpark is the Python API for Spark that allows developers to write Spark applications using Python.

• Spark and PySpark are commonly used for big data processing, machine learning, and real-time analytics.

• Example: Using PySpark ...read more

Spy-Spark is a tool used for monitoring and debugging Apache Spark applications.

• Spy-Spark is an open-source library that provides insights into the execution of Spark applications.

• It allows developers to monitor the progress of Spark jobs, track resource utilization, and identify performance bottlenecks.

• Spy-Spark can be used to collect detailed metrics about Spark applications, such as task execution times, data shuffling, and memory usage.

• It provides a web-based user interfa...read more