Top 250 Big Data Interview Questions and Answers

Hive is a data warehouse system built on top of Hadoop for querying and analyzing large datasets stored in HDFS.

• Hive translates SQL-like queries into MapReduce jobs to process data stored in HDFS

• It uses a metastore to store metadata about tables and partitions

• HiveQL is the query language used in Hive, similar to SQL

• Hive supports partitioning, bucketing, and indexing for optimizing queries

Bigdata systems architecture involves distributed storage, processing, and analysis of large volumes of data.

• Utilize distributed file systems like HDFS for storage

• Use parallel processing frameworks like Apache Spark or Hadoop for data processing

• Implement data pipelines for ETL processes

• Leverage NoSQL databases like Cassandra or MongoDB for real-time data querying

• Consider data partitioning and replication for fault tolerance

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

• Kafka uses topics to organize and store data streams.

• Producers publish messages to topics.

• Consumers subscribe to topics to read messages.

• ZooKeeper is used for managing Kafka brokers and maintaining metadata.

• Kafka Connect is used for integrating Kafka with external systems.

• Kafka Streams API allows for building stream processing applications.

• Kafka provides fault toler...read more

Mounting process in Databricks allows users to access external data sources within the Databricks environment.

• Mounting allows users to access external data sources like Azure Blob Storage, AWS S3, etc.

• Users can mount a storage account to a Databricks File System (DBFS) path using the Databricks UI or CLI.

• Mounted data can be accessed like regular DBFS paths in Databricks notebooks and jobs.

Spark architecture is a distributed computing framework that provides high-level APIs for various languages.

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

• It uses Resilient Distributed Datasets (RDDs) for fault-tolerant distributed data processing.

• Spark applications run as independent sets of processes on a cluster, coordinated by the SparkContext object.

• It supports various data sources like HDFS, Cassandra, HBase, etc.

• Spark architecture ...read more

Spark Streaming is implemented using DStreams which are a sequence of RDDs.

• DStreams are created by receiving input data streams from sources like Kafka, Flume, etc.

• The input data is then divided into small batches and processed using Spark's RDD operations.

• The processed data is then pushed to output sources like HDFS, databases, etc.

• Example: val lines = ssc.socketTextStream("localhost", 9999)

• Example: val words = lines.flatMap(_.split(" "))

Logical plan represents the high-level abstract representation of the computation to be performed, while physical plan represents the actual execution plan with specific optimizations and details.

• Logical plan is a high-level abstract representation of the computation to be performed.

• Physical plan is the actual execution plan with specific optimizations and details.

• Logical plan is created first and then optimized to generate the physical plan.

• Physical plan includes details lik...read more

Hadoop is an open-source software framework for storing and processing large datasets in a distributed computing environment.

• Hadoop consists of the Hadoop Distributed File System (HDFS) for storage and MapReduce for processing.

• It allows for the distributed processing of large data sets across clusters of computers.

• Hadoop is designed to scale from a single server to thousands of machines, each offering local computation and storage.

• Popular tools in the Hadoop ecosystem include...read more

Spark architecture includes driver, executor, and cluster manager components for distributed data processing.

• Spark architecture consists of a driver program that manages the execution of tasks across multiple worker nodes.

• Executors are responsible for executing tasks on worker nodes and storing data in memory or disk.

• Cluster manager is used to allocate resources and schedule tasks across the cluster.

• Spark applications run as independent sets of processes on a cluster, coordin...read more

Zookeeper is used for managing Kafka cluster and maintaining its metadata.

• Zookeeper stores metadata about Kafka brokers, topics, partitions, and consumer groups.

• It helps in leader election and broker failure detection.

• Kafka clients use Zookeeper to discover the current state of the Kafka cluster.

• Zookeeper also helps in maintaining the offset of messages consumed by a consumer group.

To rename a column in PySpark, use the 'withColumnRenamed' method.

• Use the 'withColumnRenamed' method on the DataFrame

• Specify the current column name and the new column name as arguments

• Assign the result to a new DataFrame to store the renamed column

Spark architecture is a distributed computing framework that provides high-level APIs for various languages.

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

• It uses Resilient Distributed Datasets (RDDs) for fault-tolerant distributed data processing.

• Spark supports various data sources like HDFS, Cassandra, HBase, etc.

• It includes components like Spark Core, Spark SQL, Spark Streaming, MLlib, and GraphX.

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 ensures data integrity and reliability by providing schema enforcement and data versioning capabilities.

• Delta Lake is compatible with Apache Spark and supports various data formats like Parquet, ORC, and Avro.

Spark based programming is a data processing framework that allows for distributed computing.

• Spark is an open-source distributed computing framework

• It allows for processing large datasets in parallel across a cluster of computers

• Spark supports multiple programming languages such as Java, Scala, and Python

• It provides APIs for batch processing, stream processing, machine learning, and graph processing

• Spark uses in-memory caching to improve performance

• Example: Spark can be used ...read more

Error handling in PySpark involves using try-except blocks and logging to handle exceptions and errors.

• Use try-except blocks to catch and handle exceptions in PySpark code

• Utilize logging to record errors and exceptions for debugging purposes

• Consider using the .option('mode', 'PERMISSIVE') method to handle corrupt records in data processing

The default block size of Hadoop is 128 MB.

• Hadoop uses HDFS (Hadoop Distributed File System) to store data in a distributed manner.

• The default block size of HDFS is 128 MB.

• This block size can be changed by modifying the dfs.blocksize property in the Hadoop configuration files.

Apache Spark is a fast and general-purpose cluster computing system. Apache Kafka is a distributed streaming platform.

• Apache Spark is used for big data processing and analytics, providing in-memory computing capabilities.

• Apache Kafka is used for building real-time data pipelines and streaming applications.

• Apache Spark can be integrated with Apache Kafka for real-time data processing.

• Both Apache Spark and Apache Kafka are part of the Apache Software Foundation.

• Apache Spark sup...read more

Databricks workflows are a set of tasks and dependencies that are executed in a specific order to achieve a desired outcome.

• Databricks workflows are used to automate and orchestrate data engineering tasks.

• They define the sequence of steps and dependencies between tasks.

• Tasks can include data ingestion, transformation, analysis, and model training.

• Workflows can be scheduled to run at specific times or triggered by events.

• Databricks provides tools like Apache Airflow and Databr...read more

I used Apache Spark to process large datasets and perform complex data transformations during my internship.

• Implemented Spark jobs to analyze customer behavior data and generate insights for marketing campaigns

• Utilized Spark SQL for querying and aggregating data from multiple sources

• Optimized Spark jobs by tuning configurations and partitioning data for better performance

RDD is a low-level distributed data structure while DataFrame is a high-level structured data abstraction.

• RDD is immutable and unstructured while DataFrame is structured and has a schema

• DataFrames are optimized for SQL queries and can be cached in memory

• RDDs are more flexible and can be used for complex data processing tasks

• DataFrames are easier to use and provide a more concise syntax for data manipulation

• RDDs are the basic building blocks of Spark while DataFrames are built...read more

df.explain() in pyspark is used to display the physical plan of the DataFrame operations.

• df.explain() is used to show the execution plan of the DataFrame operations in pyspark.

• It helps in understanding how the operations are being executed and optimized by Spark.

• The output of df.explain() includes details like the logical and physical plans, optimizations applied, and stages of execution.

Hadoop queue policy determines how resources are allocated to different jobs in a Hadoop cluster.

• Queue policies can be configured at the cluster level or at the job level.

• Different queue policies include FIFO, Fair, and Capacity.

• FIFO policy allocates resources to jobs in the order they are submitted.

• Fair policy allocates resources fairly to all jobs based on their priority and resource requirements.

• Capacity policy allocates a fixed amount of resources to each queue and jobs w...read more

Autoloader in Databricks is a feature that automatically loads new data files as they arrive in a specified directory.

• Autoloader monitors a specified directory for new data files and loads them into a Databricks table.

• It supports various file formats such as CSV, JSON, Parquet, Avro, and ORC.

• Autoloader simplifies the process of ingesting streaming data into Databricks without the need for manual intervention.

• It can be configured to handle schema evolution and data partitionin...read more

Spark is a distributed computing framework used for big data processing and analytics.

• Spark is an open-source framework developed by Apache Software Foundation.

• It is used for processing large datasets in a distributed computing environment.

• Spark provides APIs for programming in Java, Scala, Python, and R.

• It supports various data sources including Hadoop Distributed File System (HDFS), Cassandra, and Amazon S3.

• Spark includes modules for SQL, streaming, machine learning, and gr...read more

Use broadcast join or partition join in pyspark to join two large tables efficiently.

• Use broadcast join for smaller table and partition join for larger table.

• Broadcast join - broadcast the smaller table to all worker nodes.

• Partition join - partition both tables on the join key and join them.

• Example: df1.join(broadcast(df2), 'join_key')

• Example: df1.join(df2, 'join_key').repartition('join_key')

Spark is a fast and general-purpose cluster computing system, while Hadoop is a distributed processing framework.

• Spark is designed for in-memory processing, while Hadoop is disk-based.

• Spark provides real-time processing capabilities, while Hadoop is primarily used for batch processing.

• Spark has a more flexible and expressive programming model compared to Hadoop's MapReduce.

• Spark can be used with various data sources like HDFS, HBase, and more, while Hadoop is typically used w...read more

I have applied optimization techniques like partitioning, caching, and cluster sizing in Databricks projects.

• Utilized partitioning to improve query performance by limiting the amount of data scanned

• Implemented caching to store frequently accessed data in memory for faster retrieval

• Adjusted cluster sizing based on workload requirements to optimize cost and performance

Joins in Spark are used to combine data from two or more dataframes based on a common column.

• Joins can be performed using various join types such as inner join, outer join, left join, right join, etc.

• The join operation in Spark is performed using the join() function.

• The syntax for joining two dataframes is dataframe1.join(dataframe2, 'common_column')

• Spark also supports joining multiple dataframes at once using the join() function.

• Joins can be expensive operations in Spark and...read more

coalesce and repartition are both used to control the number of partitions in a PySpark DataFrame.

• coalesce reduces the number of partitions by combining them, while repartition shuffles the data to create new partitions

• coalesce is a narrow transformation and does not trigger a full shuffle, while repartition is a wide transformation and triggers a shuffle

• coalesce is useful when reducing the number of partitions, while repartition is useful when increasing the number of partit...read more

The default replication factor of Hadoop 2.x is 3.

• Replication factor determines the number of copies of data blocks that are stored across the Hadoop cluster.

• The default replication factor in Hadoop 2.x is 3, which means that each data block is replicated three times.

• The replication factor can be configured in the Hadoop configuration files.

• The replication factor affects the fault tolerance and performance of the Hadoop cluster.

• Increasing the replication factor improves fault...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 projects.

• It integrates with popular tools like Apache Spark for data processing and machine learning.

• Databricks offers automated cluster management and scaling to handle large datasets efficiently.

• It allows for easy visualization of d...read more

Speculative execution is a feature in Spark that allows the framework to launch multiple copies of a task to improve job completion time.

• Spark identifies tasks that are taking longer than expected and launches additional copies of the same task on different nodes

• The first task to complete is used and the others are killed to avoid redundant computation

• Speculative execution is useful in cases where a few slow tasks are holding up the entire job

• It can be enabled or disabled at ...read more

Reading and writing parquet files in PySpark involves using the SparkSession API.

• Create a SparkSession object

• Read a parquet file using spark.read.parquet() method

• Write a DataFrame to a parquet file using df.write.parquet() method

MapReduce is a programming model used in Hadoop for processing large datasets in parallel.

• MapReduce breaks down a big data processing task into smaller chunks that can be processed in parallel.

• The 'map' phase processes input data and produces key-value pairs.

• The 'reduce' phase aggregates the key-value pairs generated by the map phase.

• MapReduce is fault-tolerant and scalable, making it ideal for processing large datasets efficiently.

• Example: Counting the frequency of words in ...read more

Yes, encryption keys can be created in Databricks. Cluster size can be adjusted based on workload.

• Encryption keys can be created using Azure Key Vault or Databricks secrets

• Cluster size can be adjusted manually or using autoscaling based on workload

• Encryption at rest can also be enabled for data stored in Databricks

Apache Spark supports several cluster managers including YARN, Mesos, and Standalone.

• YARN is the default cluster manager for Spark and is used for Hadoop-based clusters.

• Mesos is a general-purpose cluster manager that can be used with Spark, Hadoop, and other frameworks.

• Standalone is a simple cluster manager that comes bundled with Spark and is suitable for testing and development purposes.

Working with nested JSON using PySpark involves using the StructType and StructField classes to define the schema and then using the select function to access nested fields.

• Define the schema using StructType and StructField classes

• Use the select function to access nested fields

• Use dot notation to access nested fields, for example df.select('nested_field.sub_field')

Hadoop ecosystem is a collection of open-source software tools used for distributed storage and processing of big data.

• Hadoop Distributed File System (HDFS) for storage

• MapReduce for processing

• Apache Hive for data warehousing

• Apache Pig for data analysis

• Apache Spark for real-time processing

• Apache HBase for NoSQL database

• Apache Kafka for real-time data streaming

Databricks is built on Apache Spark, a unified analytics engine for big data processing.

• Databricks is built on top of Apache Spark, which provides a unified analytics engine for big data processing.

• It offers a collaborative platform for data scientists, data engineers, and business analysts to work together.

• Databricks provides tools for data ingestion, data processing, machine learning, and visualization.

• It supports multiple programming languages like Python, Scala, SQL, and ...read more

Join two tables in PySpark code and DataFrame

• Create two DataFrames from the tables

• Specify the join condition using join() function

• Select the columns to be displayed using select() function

• Use show() function to display the result

select is used to select specific columns from a DataFrame, while withColumn is used to add or update columns in a DataFrame.

• select is used to select specific columns from a DataFrame

• withColumn is used to add or update columns in a DataFrame

• select does not modify the original DataFrame, while withColumn returns a new DataFrame with the added/updated column

• Example: df.select('col1', 'col2') - selects columns col1 and col2 from DataFrame df

• Example: df.withColumn('new_col', df['...read more

HDFS is a distributed file system designed to store large data sets reliably and fault-tolerantly.

• HDFS stands for Hadoop Distributed File System

• It is the primary storage system used by Hadoop applications

• It is designed to store large files and data sets across multiple machines

• It provides high throughput access to application data

• It is fault-tolerant and can handle node failures

• It uses a master/slave architecture with a NameNode and DataNodes

• The NameNode manages the file syst...read more

Databricks supports two types of clusters: Standard and High Concurrency.

• Databricks supports Standard clusters for single user workloads

• Databricks supports High Concurrency clusters for multi-user workloads

• Standard clusters are suitable for ad-hoc analysis and ETL jobs

• High Concurrency clusters are suitable for shared notebooks and interactive dashboards

Spark is a distributed computing framework used for big data processing and analytics.

• Spark is an open-source framework developed by Apache Software Foundation.

• It is used for processing large datasets in a distributed computing environment.

• Spark supports multiple programming languages such as Java, Scala, Python, and R.

• It provides various libraries for machine learning, graph processing, and streaming data processing.

• Spark can be used for various applications such as fraud de...read more

Rank and Dense Rank are transformations in PySpark used to assign ranks to rows based on a specific column.

• Rank assigns unique ranks to each row based on the order of values in a specific column.

• Dense Rank assigns ranks to each row based on the order of values in a specific column, but with no gaps between ranks.

• Both transformations can be used with the 'over' function to specify the column to order by.

• Example: df.select('name', 'score', rank().over(Window.orderBy('score')).a...read more

Datanode stores actual data while Namenode stores metadata of the data stored in Hadoop Distributed File System (HDFS).

• Datanode is responsible for storing and retrieving data blocks.

• Namenode maintains the directory tree of all files in the file system and tracks the location of each block.

• Datanodes send regular heartbeats to Namenode to report their status and availability.

• If a datanode fails, Namenode replicates the data blocks to other datanodes.

• HDFS is designed to have mul...read more

Display in Databricks is used to visualize data in a tabular format or as charts/graphs.

• Display function is used to show data in a tabular format in Databricks notebooks.

• It can also be used to create visualizations like charts and graphs.

• Display can be customized with different options like title, labels, and chart types.