Top 250 Big Data Interview Questions and Answers

To initiate Sparkcontext, create a SparkConf object and pass it to SparkContext constructor.

• Create a SparkConf object with app name and master URL

• Pass the SparkConf object to SparkContext constructor

• Example: conf = SparkConf().setAppName('myApp').set...read more

Pyspark code to read csv file and show top 10 records.

• Import the necessary libraries

• Create a SparkSession

• Read the CSV file using the SparkSession

• Display the top 10 records using the show() method

Troubleshooting in Hadoop involves identifying and resolving issues related to data processing and storage in a Hadoop cluster.

• Identify and resolve issues with data ingestion, processing, and storage in Hadoop

• Check for errors in log files and analyze...read more

Databricks is a unified analytics platform that combines data engineering, data science, and business analytics.

• Databricks provides a collaborative workspace for data engineers, data scientists, and business analysts to work together on big data proj...read more

Kafka Streams is a client library for building real-time, highly scalable, fault-tolerant stream processing applications.

• Kafka Streams allows developers to process and analyze data in real-time as it flows through Kafka topics.

• It provides a high-leve...read more

Yes, I have hands-on experience with big data tools.

• I have worked extensively with Hadoop, Spark, and Kafka.

• I have experience with data ingestion, processing, and storage using these tools.

• I have also worked with NoSQL databases like Cassandra and Mo...read more

Apache Kafka is a distributed streaming platform used for building real-time data pipelines and streaming applications.

• Apache Kafka is designed to handle high-throughput, fault-tolerant, and scalable real-time data streams.

• It allows for the publishin...read more

Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads.

• Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing.

• It stores data in Parqu...read more

Delta Lake provides ACID transactions, schema enforcement, and time travel capabilities for data lakes.

• ACID transactions ensure data consistency and reliability.

• Schema enforcement helps maintain data quality and prevent data corruption.

• Time travel al...read more

Big data refers to large and complex data sets that cannot be processed using traditional data processing methods.

• Big data is characterized by the 3Vs - volume, velocity, and variety.

• It requires specialized tools and techniques for processing and ana...read more

Spark is faster than MapReduce due to in-memory processing and DAG execution model.

• Spark uses in-memory processing while MapReduce uses disk-based processing.

• Spark has DAG (Directed Acyclic Graph) execution model while MapReduce has Map and Reduce ph...read more

EMR stands for Electronic Medical Record, a digital version of a patient's paper chart.

• EMRs contain patient medical history, diagnoses, medications, treatment plans, and test results.

• They improve the efficiency of healthcare delivery by allowing easy...read more

Spark Architecture is a distributed computing framework that provides high-level APIs for in-memory computing.

• Spark Architecture consists of a cluster manager, worker nodes, and a driver program.

• It uses Resilient Distributed Datasets (RDDs) for fault...read more

Delta Table is a type of table in Delta Lake that supports ACID transactions and time travel capabilities.

• Delta Table is a type of table in Delta Lake that supports ACID transactions.

• It allows users to read and write data in an Apache Spark environme...read more

Hadoop is an open-source framework for distributed storage and processing of large data sets.

• Core components include Hadoop Distributed File System (HDFS), Yet Another Resource Negotiator (YARN), and MapReduce.

• HDFS is responsible for storing data acr...read more

A program to count the number of words in a text file using PySpark.

• Read the text file using SparkContext

• Split the lines into words using flatMap

• Map each word to a tuple of (word, 1)

• Reduce by key to count the occurrences of each word

• Save the output t...read more

Use window function to find products with 3 consecutive years sales in Pyspark

• Use window function to partition by product and order by year

• Filter the results where the count of consecutive years is 3

Azure Databricks is a unified analytics platform that provides collaborative environment for big data and machine learning.

• Azure Databricks is built on Apache Spark and provides a collaborative workspace for data engineers, data scientists, and machi...read more

Use dropDuplicates() function in pyspark to remove duplicate rows based on primary key.

• Use dropDuplicates() function on the DataFrame with the primary key column specified.

• Specify the subset parameter in dropDuplicates() to specify the primary key co...read more

Spark testing can be checked by using a spark tester to measure the strength and consistency of the spark.

• Use a spark tester to check the strength and consistency of the spark

• Ensure that the spark is strong and consistent across all cylinders

• Check fo...read more

I have 3 years of experience in Big Data Administration.

• I have worked with Hadoop, Spark, and Hive.

• I have experience in setting up and maintaining Hadoop clusters.

• I have worked with various Big Data tools and technologies.

• I have experience in trouble...read more

Spark Memory management involves configuring memory allocation, monitoring memory usage, and optimizing performance.

• Set memory allocation parameters in Spark configuration (e.g. spark.executor.memory, spark.driver.memory)

• Monitor memory usage using Sp...read more

Technologies related to big data include Hadoop, Spark, Kafka, and NoSQL databases.

• Hadoop - Distributed storage and processing framework for big data

• Spark - In-memory data processing engine for big data analytics

• Kafka - Distributed streaming platform...read more

To create a Spark DataFrame, use the createDataFrame() method.

• Import the necessary libraries

• Create a list of tuples or a dictionary containing the data

• Create a schema for the DataFrame

• Use the createDataFrame() method to create the DataFrame

My day to day activities related to Spark application involve writing and optimizing Spark jobs, troubleshooting issues, and collaborating with team members.

• Writing and optimizing Spark jobs to process large volumes of data efficiently

• Troubleshooting...read more

Hadoop can handle big data by distributing it across multiple nodes and processing it in parallel.

• Hadoop uses HDFS to store data across multiple nodes

• MapReduce is used to process data in parallel

• Hadoop ecosystem includes tools like Hive, Pig, and Spa...read more

Implement fault tolerance by using checkpointing, replication, and monitoring mechanisms.

• Enable checkpointing in Spark Streaming to save the state of the computation periodically to a reliable storage like HDFS or S3.

• Use replication in Kafka to ensur...read more