Top 50 Spark Interview Questions and Answers 2024

Spark has a master-slave architecture with a cluster manager and worker nodes.

• Spark has a driver program that communicates with a cluster manager to allocate resources and schedule tasks.

• Worker nodes execute tasks and return results to the driver program.

• Spark supports multiple cluster managers like YARN, Mesos, and standalone.

• Spark also has a DAG (Directed Acyclic Graph) scheduler that optimizes task execution.

• Spark's architecture allows for in-memory processing and caching ...read more

Driver node is the node in Spark that manages the execution of a Spark application, while Executors are the nodes that actually perform the computation.

• Driver node coordinates tasks and schedules work across Executors

• Executors are responsible for executing tasks assigned by the Driver node

• Driver node maintains information about the Spark application and distributes tasks to Executors

• Executors run computations and store data for tasks

Yes, pyspark is a Python API for Apache Spark, used for big data processing and analytics.

• pyspark is a Python API for Apache Spark, allowing users to write Spark applications using Python.

• It provides high-level APIs in Python for Spark's functionality, making it easier to work with big data.

• pyspark is commonly used for data processing, machine learning, and analytics tasks.

• Example: Using pyspark to read data from a CSV file, perform transformations, and store the results in a...read more

Code in pyspark

• Use SparkSession to create a Spark application

• Read data from a source like CSV or JSON

• Perform transformations and actions on the data using Spark functions

• Write the processed data back to a destination

Dataframe is a distributed collection of data organized into named columns while RDD is a distributed collection of data organized into partitions.

• Dataframe is immutable while RDD is mutable

• Dataframe has a schema while RDD does not

• Dataframe is optimized for structured and semi-structured data while RDD is optimized for unstructured data

• Dataframe has better performance than RDD due to its optimized execution engine

• Dataframe supports SQL queries while RDD does not

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 for any irregularities or abnormalities in the spark pattern

• Compare the results to manufacturer specifications

• Make any necessary adjustments or repairs to improve the spark performance

Spark internal memory management involves allocating memory for storage, execution, and caching.

• Spark uses a unified memory management system that dynamically allocates memory between storage and execution.

• Memory is divided into regions for storage (cache) and execution (task memory).

• Spark also uses a spill mechanism to write data to disk when memory is full, preventing out-of-memory errors.

• Users can configure memory allocation for storage and execution using properties like ...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 phases.

• Spark supports real-time processing while MapReduce is batch-oriented.

• Spark has a higher level of abstraction and supports multiple languages while MapReduce is limited to Java.

• Spark has built-in libraries for SQL, streaming,...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

Spark submit command to run a Scala application on a cluster

• Include the path to the application jar file

• Specify the main class of the application

• Provide any necessary arguments or options

• Specify the cluster manager and the number of executors

• Example: spark-submit --class com.example.Main --master yarn --num-executors 4 /path/to/application.jar arg1 arg2

RDD stands for Resilient Distributed Dataset in Spark, which is an immutable distributed collection of objects.

• RDD is the fundamental data structure in Spark, representing a collection of elements that can be operated on in parallel.

• RDDs are fault-tolerant, meaning they can automatically recover from failures.

• RDDs support two types of operations: transformations (creating a new RDD from an existing one) and actions (triggering computation and returning a result).

Use Spark and SQL to find the top 5 countries with the highest population.

• Use Spark to load the data and perform data processing.

• Use SQL queries to group by country and sum the population.

• Order the results in descending order and limit to top 5.

• Example: SELECT country, SUM(population) AS total_population FROM table_name GROUP BY country ORDER BY total_population DESC LIMIT 5

Transformations and actions in Spark, reducing shuffling, and project-related questions.

• Transformations in Spark are operations that create a new RDD from an existing one, while actions are operations that return a value to the driver program.

• Examples of transformations include map, filter, and reduceByKey, while examples of actions include count, collect, and saveAsTextFile.

• To reduce shuffling in Spark, you can use techniques like partitioning, caching, and using appropriate...read more

Optimizing Spark queries involves tuning configurations, partitioning data, using appropriate data formats, and caching intermediate results.

• Tune Spark configurations for memory, cores, and parallelism

• Partition data to distribute workload evenly

• Use appropriate data formats like Parquet for efficient storage and retrieval

• Cache intermediate results to avoid recomputation

Only one job will run in parallel in Spark with four cores and four worker nodes.

• In Spark, each core can only run one task at a time, so with four cores, only four tasks can run concurrently.

• Since there are four worker nodes, each with four cores, a total of four tasks can run in parallel.

• Therefore, only one job will run in parallel in this scenario.

Set spark configuration with appropriate memory and cores for efficient processing of 2 GB data

• Increase executor memory and cores to handle larger data size

• Adjust spark memory overhead to prevent out of memory errors

• Optimize shuffle partitions for better performance

Optimization in Spark refers to improving the performance of Spark jobs by tuning configurations and utilizing best practices.

• Optimization in Spark involves tuning configurations such as memory allocation, parallelism, and caching.

• Utilizing best practices like partitioning data properly and using efficient transformations can improve performance.

• Examples of optimization techniques include using broadcast variables, avoiding shuffling, and leveraging data locality.

Spark optimization techniques improve performance and efficiency of Spark jobs.

• Partitioning data correctly to avoid data shuffling

• Caching intermediate results to avoid recomputation

• Using appropriate data formats like Parquet for efficient storage and retrieval

• Tuning memory settings for optimal performance

• Avoiding unnecessary data transformations

Performance optimization in Spark involves tuning configurations, optimizing code, and utilizing caching.

• Tune Spark configurations such as executor memory, number of executors, and shuffle partitions.

• Optimize code by reducing unnecessary shuffles, using efficient transformations, and avoiding unnecessary data movements.

• Utilize caching to store intermediate results in memory and avoid recomputation.

• Example: In my project, I optimized Spark performance by increasing executor me...read more

Spark is a distributed computing framework for big data processing.

• Spark is used for processing large datasets in parallel across a cluster of computers.

• It can be used for various use cases such as data processing, machine learning, and real-time stream processing.

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

• Examples of companies using Spark include Netflix, Uber, and Airbnb.

Reading files from different locations using Spark

• Use SparkSession to create a DataFrameReader

• Use the .option() method to specify the file location and format

• Use the .load() method to read the file into a DataFrame

Spark performance tuning involves optimizing various configurations and parameters to improve the efficiency and speed of Spark jobs.

• Optimize resource allocation such as memory and CPU cores to prevent bottlenecks

• Use partitioning and caching to reduce data shuffling and improve data locality

• Adjust the level of parallelism to match the size of the data and available resources

• Monitor and analyze job execution using Spark UI and logs to identify performance issues

• Utilize advance...read more

Spark applications can be deployed using various methods like standalone mode, YARN, Mesos, or Kubernetes.

• Deploy Spark application in standalone mode by submitting the application using spark-submit command

• Deploy Spark application on YARN by setting the master to yarn and submitting the application to the YARN ResourceManager

• Deploy Spark application on Mesos by setting the master to mesos and submitting the application to the Mesos cluster

• Deploy Spark application on Kubernete...read more

Kafka is a distributed streaming platform used for building real-time data pipelines and streaming applications. Spark is a fast and general-purpose cluster computing system for big data processing.

• Kafka is used for building real-time data pipelines by enabling high-throughput, low-latency data delivery.

• Spark is used for processing large-scale data processing tasks in a distributed computing environment.

• Kafka can be used to collect data from various sources and distribute it ...read more

Spark Dataset is a distributed collection of data organized into named columns.

• It is an extension of the Spark DataFrame API.

• It provides type-safe, object-oriented programming interface.

• It offers better performance and optimization compared to DataFrames.

• Example: val dataset = spark.read.json("path/to/file").as[MyCaseClass]

Spark memory allocation is the process of assigning memory to different components of a Spark application.

• Spark divides memory into two regions: storage region and execution region.

• The storage region is used to cache data and the execution region is used for computation.

• Memory allocation can be configured using spark.memory.fraction and spark.memory.storageFraction properties.

• Spark also provides options for off-heap memory allocation and memory management using garbage collec...read more

Implementing SCD type2 in Spark code

• Use DataFrame operations to handle SCD type2 changes

• Create a new column to track historical changes

• Use window functions to identify the latest record for each key

• Update existing records with end dates and insert new records with start dates

Spark and MapReduce are both distributed computing frameworks used for processing large datasets.

• Spark is a fast and general-purpose cluster computing system that provides in-memory processing capabilities.

• MapReduce is a programming model for processing and generating large data sets with a parallel, distributed algorithm on a cluster.

• Spark is known for its speed and ease of use, while MapReduce is more traditional and slower in comparison.

• Both Spark and MapReduce are commonl...read more

Spark handles fault tolerance through resilient distributed datasets (RDDs) and lineage tracking.

• Spark achieves fault tolerance through RDDs, which are immutable distributed collections of objects that can be rebuilt if a partition is lost.

• RDDs track the lineage of transformations applied to the data, allowing lost partitions to be recomputed based on the original data and transformations.

• Spark also replicates data partitions across multiple nodes to ensure availability in ca...read more

Optimizing Spark job involves tuning configurations, partitioning data, caching, and using efficient transformations.

• Tune Spark configurations like executor memory, cores, and parallelism for optimal performance.

• Partition data correctly to distribute workload evenly across nodes and avoid shuffling.

• Cache intermediate results in memory to avoid recomputation.

• Use efficient transformations like map, filter, and reduceByKey instead of costly operations like groupByKey.

• Optimize da...read more

Spark is lazy execution to optimize performance by delaying computation until necessary.

• Spark delays execution until an action is called to optimize performance.

• This allows Spark to optimize the execution plan and minimize unnecessary computations.

• Lazy evaluation helps in reducing unnecessary data shuffling and processing.

• Example: Transformations like map, filter, and reduce are not executed until an action like collect or saveAsTextFile is called.

Spark cluster sizing depends on workload, data size, memory requirements, and processing speed.

• Consider the size of the data being processed

• Take into account the memory requirements of the Spark jobs

• Factor in the processing speed needed for the workload

• Scale the cluster based on the number of nodes and cores required

• Monitor performance and adjust cluster size as needed

Large Spark datasets can be handled by partitioning, caching, optimizing transformations, and tuning resources.

• Partitioning data to distribute workload evenly across nodes

• Caching frequently accessed data to avoid recomputation

• Optimizing transformations to reduce unnecessary processing

• Tuning resources like memory allocation and parallelism for optimal performance

Core components of Spark include Spark Core, Spark SQL, Spark Streaming, MLlib, and GraphX.

• Spark Core: foundation of the Spark platform, provides basic functionality for distributed data processing

• Spark SQL: module for working with structured data using SQL and DataFrame API

• Spark Streaming: extension of the core Spark API that enables scalable, high-throughput, fault-tolerant stream processing of live data streams

• MLlib: machine learning library for Spark that provides scalabl...read more

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').setMaster('local[*]') sc = SparkContext(conf=conf)

• Stop SparkContext using sc.stop()

SparkConfig is a configuration object used in Apache Spark to set various parameters for Spark applications.

• SparkConfig is used to set properties like application name, master URL, and other Spark settings.

• It is typically created using SparkConf class in Spark applications.

• Example: val sparkConf = new SparkConf().setAppName("MyApp").setMaster("local")

Spark submit command is used to submit Spark applications to a cluster

• Used to launch Spark applications on a cluster

• Requires specifying the application JAR file, main class, and any arguments

• Can set various configurations like memory allocation, number of executors, etc.

• Example: spark-submit --class com.example.Main --master yarn --deploy-mode cluster myApp.jar arg1 arg2

Spark framework is a distributed computing system that provides in-memory processing capabilities for big data analytics.

• Spark framework is built on top of the Hadoop Distributed File System (HDFS) for storage and Apache Mesos or Hadoop YARN for resource management.

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

• Spark provides high-level APIs like Spark SQL for structured data processing, MLlib for machine learning, GraphX for graph processing, a...read more

Spark memory optimisation techniques

• Use broadcast variables to reduce memory usage

• Use persist() or cache() to store RDDs in memory

• Use partitioning to reduce shuffling and memory usage

• Use off-heap memory to avoid garbage collection overhead

• Tune memory settings such as spark.driver.memory and spark.executor.memory

Spark can be used for real-time data processing in streaming use cases.

• Spark Streaming allows for processing real-time data streams.

• It can handle high-throughput and fault-tolerant processing.

• Examples include real-time analytics, monitoring, and alerting.

Spark is a distributed computing engine that processes large datasets in parallel across a cluster of computers.

• Spark uses a master-slave architecture with a driver program that coordinates tasks across worker nodes.

• Data is stored in Resilient Distributed Datasets (RDDs) that can be cached in memory for faster processing.

• Spark supports multiple programming languages including Java, Scala, and Python.

• Spark can be used for batch processing, streaming, machine learning, and grap...read more

Partition in Spark is a way to divide data into smaller chunks for parallel processing.

• Partitions are basic units of parallelism in Spark

• Data in RDDs are divided into partitions which are processed in parallel

• Number of partitions can be controlled using repartition() or coalesce() methods

DAG (Directed Acyclic Graph) in Apache Spark is used to represent a series of data processing steps and their dependencies.

• DAG in Spark helps optimize the execution of tasks by determining the order in which they should be executed based on dependencies.

• It breaks down a Spark job into smaller tasks and organizes them in a way that minimizes unnecessary computations.

• DAGs are created automatically by Spark when actions are called on RDDs or DataFrames.

• Example: If a Spark job in...read more

Optimisation techniques in Spark improve performance by efficiently utilizing resources.

• Use partitioning to distribute data evenly across nodes

• Cache intermediate results to avoid recomputation

• Use broadcast variables for small lookup tables

• Optimize shuffle operations to reduce data movement

Spark architecture is based on master-slave architecture with a cluster manager and worker nodes.

• Spark has a master node that manages the cluster and worker nodes that execute tasks.

• The cluster manager allocates resources to worker nodes and monitors their health.

• Spark uses a distributed file system like HDFS to store data and share it across the cluster.

• Spark applications are written in high-level languages like Scala, Java, or Python and compiled to run on the JVM.

• Spark sup...read more

Repartition increases or decreases the number of partitions in a DataFrame, while Coalesce only decreases the number of partitions.

• Repartition can increase or decrease the number of partitions in a DataFrame, leading to a shuffle of data across the cluster.

• Coalesce only decreases the number of partitions in a DataFrame without performing a full shuffle, making it more efficient than repartition.

• Repartition is typically used when there is a need to increase the number of parti...read more

Spark is faster than Hadoop MapReduce due to in-memory processing and supports multiple types of workloads.

• Spark performs in-memory processing, while Hadoop MapReduce writes to disk after each task.

• Spark supports multiple types of workloads like batch processing, interactive queries, streaming data, and machine learning, while Hadoop MapReduce is mainly for batch processing.

• Spark provides higher-level APIs in Java, Scala, Python, and R, making it easier to use than Hadoop Map...read more

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 issues related to Spark application performance or errors

• Collaborating with team members to design and implement new features or improvements

• Monitoring Spark application performance and resource usage

Repartition increases the number of partitions in a DataFrame, while coalesce reduces the number of partitions without shuffling data.

• Repartition involves a full shuffle of the data across the cluster, which can be expensive.

• Coalesce minimizes data movement by only creating new partitions if necessary.

• Repartition is typically used when increasing parallelism or evenly distributing data, while coalesce is used for reducing the number of partitions without a full shuffle.

• Exampl...read more

Spark test in GLR is carried out by applying a small amount of spark to the sample to observe the color and intensity of the spark produced.

• Ensure the sample is clean and free of any contaminants

• Apply a small amount of spark to the sample using a spark tester

• Observe the color and intensity of the spark produced

• Compare the results with a reference chart to determine the quality of the sample

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 Spark UI or monitoring tools like Ganglia

• Optimize performance by tuning memory allocation based on workload and cluster resources

• Use techniques like caching and persistence to reduce memory usage and improve performance

spark-submit is a command-line tool used to submit Spark applications to a cluster

• spark-submit is used to launch applications on a Spark cluster

• It allows users to specify application parameters and dependencies

• Example: spark-submit --class com.example.MyApp myApp.jar

Spark RDD stands for Resilient Distributed Dataset, which is a fundamental data structure in Apache Spark.

• RDD is an immutable distributed collection of objects that can be operated on in parallel.

• It allows for fault-tolerant distributed data processing in Spark.

• RDDs can be created from Hadoop InputFormats, local collections, or by transforming other RDDs.

• Operations on RDDs are lazily evaluated, allowing for efficient data processing.

• Example: val rdd = sc.parallelize(List(1, 2...read more

Optimisation in Spark refers to improving the performance of Spark jobs by tuning configurations and utilizing best practices.

• Optimisation can involve adjusting Spark configurations such as memory allocation, parallelism, and caching.

• Utilizing partitioning and bucketing techniques can improve data processing efficiency.

• Avoiding unnecessary shuffling of data can also enhance performance.

• Using appropriate data formats and storage options like Parquet can optimize Spark jobs.

• App...read more

Optimization techniques in Spark improve performance and efficiency of data processing.

• Partitioning data to distribute workload evenly

• Caching frequently accessed data in memory

• Using broadcast variables for small lookup tables

• Avoiding shuffling operations whenever possible

• Tuning memory settings and garbage collection parameters

Performance optimization of Spark involves tuning various parameters and optimizing code.

• Tune memory allocation and garbage collection settings

• Optimize data serialization and compression

• Use efficient data structures and algorithms

• Partition data appropriately

• Use caching and persistence wisely

• Avoid shuffling data unnecessarily

• Monitor and analyze performance using Spark UI and other tools

Spark performance tuning methods involve optimizing resource allocation, data partitioning, and caching.

• Optimize resource allocation by adjusting memory and CPU settings in Spark configurations.

• Partition data effectively to distribute work evenly across nodes.

• Utilize caching to store intermediate results in memory for faster access.

• Use broadcast variables for small lookup tables to reduce shuffle operations.

• Monitor and analyze Spark job performance using tools like Spark UI a...read more

Optimizing Spark jobs involves tuning configurations, partitioning data, caching, and using efficient transformations.

• Tune Spark configurations for memory, cores, and parallelism

• Partition data to distribute workload evenly

• Cache intermediate results to avoid recomputation

• Use efficient transformations like map, filter, and reduce

• Avoid shuffling data unnecessarily

Apache Spark is a fast and general-purpose cluster computing system.

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

• It has a unified architecture that combines SQL, streaming, machine learning, and graph processing capabilities.

• Spark architecture consists of a driver program that coordinates the execution of tasks on a cluster of worker nodes.

• It uses a mas...read more

Repartitioning increases partitions while Coalesce reduces partitions.

• Repartitioning shuffles data across the cluster and can be used to increase parallelism.

• Coalesce merges partitions without shuffling data and can be used to reduce overhead.

• Repartitioning is expensive and should be used sparingly.

• Coalesce is faster but may not be as effective as repartitioning in increasing parallelism.

• Both can be used to optimize data processing and improve performance.

Spark is a fast and distributed data processing engine that can perform in-memory processing.

• Spark is faster than Hadoop because it can perform in-memory processing, reducing the need to write intermediate results to disk.

• Spark uses DAG (Directed Acyclic Graph) for processing tasks, which optimizes the workflow and minimizes data shuffling.

• Spark allows for iterative computations, making it suitable for machine learning algorithms that require multiple passes over the data.

• Spa...read more

Repartition increases or decreases the number of partitions in a DataFrame, while coalesce only decreases the number of partitions.

• Repartition involves shuffling data across the network, while coalesce tries to minimize shuffling by only creating new partitions if necessary.

• Repartition is typically used when increasing the number of partitions for parallelism, while coalesce is used when decreasing partitions to optimize performance.

• Example: df.repartition(10) vs df.coalesce(...read more

Optimizing Spark involves tuning configurations, partitioning data, using efficient transformations, and caching intermediate results.

• Tune Spark configurations for optimal performance

• Partition data to distribute workload evenly

• Use efficient transformations like map, filter, and reduce

• Cache intermediate results to avoid recomputation

Spark optimisation techniques focus on improving performance and efficiency of Spark jobs.

• Use partitioning to distribute data evenly

• Cache intermediate results to avoid recomputation

• Optimize shuffle operations to reduce data movement

• Use broadcast variables for small lookup tables

• Tune memory and executor settings for optimal performance

Spark performance problems can arise due to inefficient code, data skew, resource constraints, and improper configuration.

• Inefficient code can lead to slow performance, such as using collect() on large datasets.

• Data skew can cause uneven distribution of data across partitions, impacting processing time.

• Resource constraints like insufficient memory or CPU can result in slow Spark jobs.

• Improper configuration settings, such as too few executors or memory allocation, can hinder p...read more

Spark architecture includes driver, cluster manager, and worker nodes for distributed processing.

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

• Cluster manager is responsible for allocating resources and scheduling tasks across worker nodes.

• Worker nodes execute the tasks and store data in memory or disk for processing.

• Example: In a Spark application, the driver program sends tasks to worker nodes for parallel processing of ...read more

Hadoop is a distributed storage system while Spark is a distributed processing engine.

• Hadoop is primarily used for storing and processing large volumes of data in a distributed environment.

• Spark is designed for fast data processing and can perform in-memory computations, making it faster than Hadoop for certain tasks.

• Hadoop uses MapReduce for processing data, while Spark uses Resilient Distributed Datasets (RDDs) for faster processing.

• Spark is more suitable for real-time proc...read more

Optimization in Spark involves tuning various parameters to improve performance and efficiency.

• Optimizing Spark jobs can involve adjusting the number of partitions to balance workload

• Utilizing caching and persistence to reduce unnecessary recalculations

• Using broadcast variables for efficient data sharing across tasks

• Leveraging data skew handling techniques to address uneven data distribution

• Applying proper resource allocation and cluster configuration for optimal performance

Some Spark optimization techniques include partitioning, caching, and using appropriate data formats.

• Partitioning data to distribute workload evenly

• Caching frequently accessed data to avoid recomputation

• Using appropriate data formats like Parquet for efficient storage and processing

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

Spark is a fast and general-purpose cluster computing system.

• Spark is designed for speed and ease of use in data processing.

• It can run programs up to 100x faster than Hadoop MapReduce.

• Spark provides high-level APIs in Java, Scala, Python, and R.

• It supports various workloads such as batch processing, interactive queries, streaming analytics, and machine learning.

• Spark can be used standalone, on Mesos, or on Hadoop YARN cluster manager.

Spark optimisation techniques improve performance and efficiency of Spark jobs.

• Partitioning data correctly to avoid data shuffling

• Caching intermediate results to avoid recomputation

• Using broadcast variables for small lookup tables

• Optimizing the number of executors and memory allocation

• Avoiding unnecessary transformations and actions

Spark is faster for real-time processing, while Hadoop is better for batch processing and large-scale data storage.

• Spark is faster than Hadoop due to in-memory processing.

• Hadoop is better for batch processing and large-scale data storage.

• Spark is more suitable for real-time processing and iterative algorithms.

• Hadoop is more suitable for processing large volumes of data in a distributed manner.

• Spark is commonly used for machine learning and streaming data processing.

• Hadoop is ...read more

Spark optimization techniques improve performance and efficiency of Spark jobs.

• Use partitioning to distribute data evenly across nodes

• Cache intermediate results to avoid recomputation

• Use broadcast variables for small lookup tables

• Optimize shuffle operations by reducing data shuffling

• Tune memory settings for better performance