Spark Core Concepts
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Questions and Answers

What is the primary function of Spark's DAG Scheduler?

  • To persist RDDs in memory and disk
  • To manage Spark's memory hierarchy
  • To execute RDD operations in parallel
  • To break down RDD operations into a DAG of tasks (correct)

    • What is the purpose of the 'Execution Memory' in Spark's memory hierarchy?

  • To store JVM overhead
  • To cache intermediate results (correct)
  • To store system-level memory allocation
  • To store persisted RDDs

    • What happens in Spark's 'Disk Spill Mode'?

  • Spark persists RDDs in memory and disk
  • Spark stores data in memory for faster access
  • Spark spills data to disk when memory is limited (correct)
  • Spark uses only the system memory for allocation

    • What is the difference between 'Cache' and 'Persist' in Spark?

    <p>Cache temporarily stores an RDD in memory, while persist stores it in memory and/or disk</p> Signup and view all the answers

    What is the purpose of 'StorageLevel' in Spark?

    <p>To define the storage behavior for an RDD</p> Signup and view all the answers

    What type of RDD operation triggers computation and returns a result?

    <p>Action</p> Signup and view all the answers

    What is the primary function of Azure Databricks' connectors in data engineering?

    <p>To ingest data from various sources</p> Signup and view all the answers

    What is the benefit of using Apache Spark on Azure Databricks?

    <p>It provides automatic scaling and termination of Spark clusters</p> Signup and view all the answers

    What is the purpose of Azure Databricks' workflows and jobs in data engineering?

    <p>To create, schedule, and manage data pipelines</p> Signup and view all the answers

    What is the benefit of using Azure Databricks' data storage solutions?

    <p>It allows for data storage and management in various formats</p> Signup and view all the answers

    What is a feature of Spark clusters on Azure Databricks?

    <p>Automatic scaling and termination</p> Signup and view all the answers

    What is a benefit of using Azure Databricks' collaborative workspaces?

    <p>It supports collaborative workspaces for data engineers, data scientists, and data analysts</p> Signup and view all the answers

    Study Notes

    Spark Core

    • Resilient Distributed Datasets (RDDs): fundamental data structure in Spark, representing a collection of data that can be split across multiple nodes
    • RDD Operations: two types of operations:
      • Transformations: operations that create a new RDD, e.g., map, filter, groupBy
      • Actions: operations that trigger computation and return a result, e.g., count, collect, reduce
    • DAG (Directed Acyclic Graph) Scheduler: Spark's scheduling engine, responsible for executing RDD operations
      • Breaks down RDD operations into a DAG of tasks
      • Schedules tasks on the cluster, considering dependencies and node availability

    Memory Management

    • Memory Hierarchy: Spark uses a hierarchical memory management system to optimize performance
      • Execution Memory: used for caching and storing intermediate results
      • Storage Memory: used for storing persisted RDDs
      • System Memory: used for JVM overhead and other system-level memory allocation
    • Memory Modes:
      • In-Memory Mode: Spark stores data in memory for faster access
      • Disk Spill Mode: Spark spills data to disk when memory is limited
    • Cache and Persist:
      • Cache: temporarily stores an RDD in memory for faster access
      • Persist: stores an RDD in memory and/or disk for later reuse
      • StorageLevel: defines the storage behavior for an RDD, e.g., MEMORY_ONLY, DISK_ONLY, MEMORY_AND_DISK

    Spark Core

    • Resilient Distributed Datasets (RDDs): a fundamental data structure in Spark, representing a collection of data split across multiple nodes.

    RDD Operations

    • Transformations: operations that create a new RDD, e.g., map, filter, groupBy.
    • Actions: operations that trigger computation and return a result, e.g., count, collect, reduce.

    DAG (Directed Acyclic Graph) Scheduler

    • Scheduling Engine: responsible for executing RDD operations.
    • Task Scheduling: breaks down RDD operations into a DAG of tasks and schedules them on the cluster, considering dependencies and node availability.

    Memory Management

    Memory Hierarchy

    • Hierarchical Memory Management: Spark's memory management system optimizes performance.
    • Execution Memory: used for caching and storing intermediate results.
    • Storage Memory: used for storing persisted RDDs.
    • System Memory: used for JVM overhead and other system-level memory allocation.

    Memory Modes

    • In-Memory Mode: Spark stores data in memory for faster access.
    • Disk Spill Mode: Spark spills data to disk when memory is limited.

    Cache and Persist

    • Cache: temporarily stores an RDD in memory for faster access.
    • Persist: stores an RDD in memory and/or disk for later reuse.
    • StorageLevel: defines the storage behavior for an RDD, e.g., MEMORY_ONLY, DISK_ONLY, MEMORY_AND_DISK.

    Azure Databricks Overview

    • Fast, easy, and collaborative analytics platform based on Apache Spark
    • Provides a managed platform for data engineering, data science, and data analytics

    Data Engineering with Azure Databricks

    • Ingest data from various sources (e.g., Azure Storage, Azure Data Lake Storage, Azure Cosmos DB) using Azure Databricks' connectors
    • Process and transform large datasets using Apache Spark for scalability and performance
    • Create, schedule, and manage data pipelines using Azure Databricks' workflows and jobs
    • Store and manage data in various formats (e.g., CSV, JSON, Avro) using Azure Databricks' data storage solutions (e.g., Databricks File System (DBFS))

    Apache Spark on Azure Databricks

    • Create and manage Spark clusters on Azure Databricks with automatic scaling and termination
    • Supports multiple Apache Spark versions for flexibility and compatibility
    • Leverage Spark's APIs (e.g., DataFrame, Dataset, RDD) for data processing, machine learning, and graph processing
    • Use popular Spark libraries, such as MLlib (machine learning), GraphFrames (graph processing), and Structured Streaming (streaming data processing)

    Additional Features

    • Supports collaborative workspaces for data engineers, data scientists, and data analysts
    • Provides enterprise-grade security features, such as Azure Active Directory integration and encryption
    • Integrates with other Azure services (e.g., Azure Synapse Analytics, Azure Storage) and popular data tools (e.g., Jupyter, Git)

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    Quiz Team

    Description

    Understand the fundamental concepts of Spark Core, including Resilient Distributed Datasets (RDDs), RDD Operations, and DAG Scheduler.

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