Top 250 Big Data Interview Questions and Answers

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:...read more

Improving merge requests through design enhancements

• Implement a clearer review process with defined roles and responsibilities

• Utilize templates for MRs to ensure consistency and completeness

• Integrate automated testing and code quality checks to strea...read more

Spark processes data in parallel using its distributed computing framework.

• Spark divides data into partitions and processes each partition independently.

• Tasks are executed in parallel across multiple nodes in a cluster.

• Spark uses in-memory processing...read more

Skills required for a Business Development Associate include strong communication, negotiation, analytical, and networking abilities.

• Strong communication skills to effectively interact with clients and team members

• Negotiation skills to secure deals a...read more

The Spark application lifecycle involves stages from submission to execution and completion of tasks in a distributed environment.

• 1. Application Submission: The user submits a Spark application using spark-submit command.

• 2. Driver Program: The driver...read more

Reducer is a function in Redux that specifies how the application's state changes in response to actions.

• Reducer functions take the current state and an action as arguments, and return the new state.

• Reducers are pure functions, meaning they do not mo...read more

Spark optimization involves tuning configurations, partitioning data, using appropriate transformations, and caching intermediate results.

• Tune Spark configurations based on cluster resources and workload requirements

• Partition data to distribute workl...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...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.

• La...read more

BDC stands for Batch Data Communication. It is a method used in SAP to upload data from external systems into SAP.

• BDC is used to automate data entry into SAP systems.

• There are two methods of BDC - Call Transaction and Session Method.

• BDC is commonly u...read more

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 transformatio...read more

Shuffling can be reduced by optimizing data partitioning and minimizing data movement.

• Use partitioning techniques like bucketing and sorting to minimize shuffling

• Avoid using wide transformations like groupBy and join

• Use broadcast variables to reduce ...read more

Hadoop architecture is a framework for distributed storage and processing of large data sets across clusters of computers.

• Hadoop consists of HDFS for storage and MapReduce for processing.

• It follows a master-slave architecture with a single NameNode a...read more

Big data system distribution for storage and compute involves partitioning data across multiple nodes for efficient processing.

• Data is partitioned across multiple nodes to distribute storage and processing load.

• Hadoop Distributed File System (HDFS) i...read more

RDD can be created in Apache Spark by parallelizing an existing collection or by loading data from an external dataset.

• Create RDD by parallelizing an existing collection using sc.parallelize() method

• Create RDD by loading data from an external dataset...read more

Data can be loaded into BigQuery using Dataflow by creating a pipeline in Dataflow that reads data from a source and writes it to BigQuery.

• Create a Dataflow pipeline using Apache Beam SDK

• Read data from a source such as Cloud Storage or Pub/Sub

• Transfo...read more

Spark context is the main entry point for Spark functionality and represents the connection to a Spark cluster.

• Main entry point for Spark functionality

• Represents connection to a Spark cluster

• Used to create RDDs, broadcast variables, and accumulators

Use the withColumn method in PySpark to combine two columns in a DataFrame.

• Use the withColumn method to create a new column by combining two existing columns

• Specify the new column name and the expression to combine the two columns

• Example: df = df.wit...read more

RDD is resilient due to its ability to recover from failures and maintain data integrity.

• RDDs are fault-tolerant and can recover from node failures by recomputing lost data from the original source.

• RDDs store data lineage information, allowing them t...read more

Executor memory is the amount of memory allocated to each executor in a Spark application.

• Executor memory is specified using the 'spark.executor.memory' configuration property.

• It determines how much memory each executor can use to process tasks.

• It is...read more

Apache Airflow is a platform to programmatically author, schedule, and monitor workflows.

• Apache Airflow allows users to define workflows as Directed Acyclic Graphs (DAGs) in Python scripts.

• It provides a web-based UI for users to visualize and monitor...read more

Large data processing in Pyspark involves partitioning, caching, and optimizing transformations for efficient processing.

• Partitioning data to distribute workload evenly across nodes

• Caching intermediate results to avoid recomputation

• Optimizing transfo...read more

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 clas...read more

Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads, while Delta Warehouse is a cloud-based data warehouse service.

• Delta Lake is an open-source storage layer that brings ACID transactions to...read more

Spark cluster is a group of interconnected computers that work together to process large datasets using Apache Spark.

• Consists of a master node and multiple worker nodes

• Master node manages the distribution of tasks and resources

• Worker nodes execute th...read more

I have used HUDI and Iceberg in my previous project for managing large-scale data lakes efficiently.

• Implemented HUDI for incremental data ingestion and managing large datasets in real-time

• Utilized Iceberg for efficient table management and data versi...read more

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, numb...read more