Master Azure Databricks: 6. Apache Spark to Data Engineering Platform

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Questions and Answers

Which of the following is NOT a capability offered by Apache Spark?

  • Graph processing APIs
  • Operating system development (correct)
  • Spark SQL for batch processing
  • Structured Streaming for stream processing

Which of the following cluster resource managers can be used by Spark?

  • YARN
  • Kubernetes
  • Mesos
  • All of the above (correct)

Which of the following is NOT a reason for Apache Spark's popularity?

  • Provides a unified platform for various processing types
  • Difficult to use (correct)
  • Open-source nature with a wide ecosystem
  • Offers a high level of abstraction, hiding complexities of distributed data processing

Which of the following is NOT a data storage system that Spark can integrate with?

<p>Proprietary Database X (B)</p> Signup and view all the answers

Which API is NOT directly supported by Spark?

<p>TensorFlow API (C)</p> Signup and view all the answers

What critical feature is NOT inherently part of Apache Spark and requires integration with other solutions?

<p>ACID transaction capabilities (D)</p> Signup and view all the answers

Which of the following components is NOT included within the open-source Apache Spark framework itself?

<p>Cluster management capabilities (A)</p> Signup and view all the answers

Which characteristic does NOT describe the Databricks platform compared to other Spark platforms like Amazon EMR or Azure HDInsight?

<p>Based on Hadoop technology (D)</p> Signup and view all the answers

Which of the following is a key difference between Databricks and platforms like Amazon EMR and Azure HDInsight?

<p>Databricks is a purely cloud-based Spark platform, while EMR and HDInsight are based on Hadoop. (C)</p> Signup and view all the answers

In the context of enterprise-grade data engineering, what is a limitation of Apache Spark?

<p>It does not include built-in data storage infrastructure. (C)</p> Signup and view all the answers

Why is a platform like Databricks often preferred for enterprise-grade data engineering solutions compared to using open-source Apache Spark alone?

<p>Databricks provides additional capabilities, such as robust cluster management and automation tools, that are not included in Apache Spark. (A)</p> Signup and view all the answers

Considering the limitations of Apache Spark, which of the following is NOT a feature typically required in enterprise-grade data engineering applications but missing from Spark?

<p>Support for multiple programming languages like Scala, Java, Python, and R (B)</p> Signup and view all the answers

Which platform relies on Hadoop technology behind the scenes?

<p>Azure HDInsight (B)</p> Signup and view all the answers

What is a main reason Apache Spark does not have built-in data storage?

<p>Spark is designed to be storage-agnostic, allowing it to work with various systems (C)</p> Signup and view all the answers

What does the wide ecosystem of Apache Spark refer to?

<p>The variety of integrations and capabilities developed by different companies with their tools (C)</p> Signup and view all the answers

A data engineer needs to automate the creation and management of Spark clusters. Which of the following platforms provides native capabilities for this task?

<p>Databricks (B)</p> Signup and view all the answers

Which of the following is a valid reason to choose Databricks over open-source Apache Spark for an enterprise-grade data engineering project?

<p>Databricks provides enhanced automation and cluster management capabilities. (A)</p> Signup and view all the answers

A company wants to implement a data lake solution with ACID transaction support on their Spark-based data pipelines. How can they achieve this?

<p>Integrate a solution like Delta Lake with Apache Spark to provide ACID properties. (D)</p> Signup and view all the answers

What is the primary advantage of Databricks' architecture compared to platforms like Amazon EMR or Azure HDInsight?

<p>Cloud-native architecture optimized for Spark (C)</p> Signup and view all the answers

A data science team needs to build and deploy machine learning models using Apache Spark. Which set of APIs would they primarily utilize?

<p>MLlib (A)</p> Signup and view all the answers

Flashcards

What is Apache Spark?

An engine for executing data engineering, stream processing, and machine learning on distributed clusters.

Spark SQL

ANSI SQL-compliant batch processing APIs, also known as DataFrame APIs.

Structured Streaming

Enables real-time data processing.

Graph Processing APIs

APIs for processing graph-structured data.

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Machine Learning APIs (MLlib)

Libraries for common machine learning algorithms.

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Spark Framework

Framework that runs on a cluster of computers using resource managers like Yarn, Kubernetes and Mesos.

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Spark Compatible Storage Systems

HDFS, S3, ADLS, Google Cloud Storage

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Spark Core APIs

Core APIs for data processing activities, available in Scala, Java, Python, and R.

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Spark Abstraction

Hides complexities of distributed data processing.

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Unified Platform

A single platform offering SQL, DataFrame processing, batch processing, stream processing, machine learning, and graph processing.

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Spark's Data Storage

Lacks its own storage later, requiring integration with other storage solutions.

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Acid Properties

It lacks atomicity, consistency, isolation, durability; essential features of data systems.

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Metadata Catalog

Lacks a robust, centralized metadata catalog.

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Cluster Management

It lacks the ability to create, destroy, or manage clusters using Apache Spark.

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Automation APIs

Tools and APIs to automate project tasks are limited.

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Cloudera Hadoop Platform

A Hadoop platform that runs Spark on premise.

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Amazon EMR

Spark platform offered by Amazon, based on Hadoop technology.

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Azure HDInsight

Spark platform from Azure, leveraging Hadoop technology.

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Google Dataproc

Google's cloud-based Spark platform on Hadoop technology.

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Databricks Platform

A cloud-native Spark platform, independent of Hadoop technology.

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Study Notes

  • Apache Spark is an engine for executing data engineering, stream processing, and machine learning on distributed clusters.
  • Spark provides capabilities through Spark SQL, stream processing APIs, graph processing APIs, and machine learning APIs.
  • Thousands of companies use Spark, including 80% of Fortune 500 companies.

Spark Architecture

  • Spark runs on a distributed cluster of computers.
  • It requires a cluster manager like Yarn, Standalone, Kubernetes, or Mesos.
  • It integrates with distributed storage systems like HDFS, S3, ADLs, and Google Cloud Storage.
  • Spark offers Core APIs (RDD APIs) available in Scala, Java, Python, and R, but recommends more advanced APIs.
  • More advanced APIs include Spark SQL, Data Frame APIs, Spark Structured Streaming APIs, Mllib APIs, and GraphX.

Popularity of Spark

  • Spark offers a high level of abstraction, hiding the complexities of distributed data processing.
  • Spark SQL is the simplest interface for developing Spark applications.
  • It is a unified platform for SQL, data frame processing, batch, stream, machine learning and graph processing
  • It is open source, and has a wide ecosystem for integrations and capabilities.

Limitations of Spark

  • Apache Spark lacks its own data storage infrastructure and needs to integrate with external solutions like HDFS or Amazon S3.
  • It doesn't have built-in ACID transaction capabilities (Atomicity, Consistency, Isolation, Durability).
  • Spark has a simple catalog but lacks a robust, centralized metadata catalog.
  • It does not include cluster management capabilities for creating, destroying, or managing clusters.
  • Spark lacks automation APIs, SDKs, or command-line tools for project automation.
  • Spark requires additional capabilities for enterprise-grade data engineering solutions.

Spark Platforms

  • Cloudera Hadoop Platform is a popular on-premise platform on which Spark runs.
  • Amazon EMR, Azure HDInsight, and Google Dataproc are cloud-based Spark platforms built on Hadoop technology.
  • These platforms use Yarn as the resource manager and launch Hadoop clusters on the cloud.
  • Databricks is a cloud-native Spark platform, not based on Hadoop technology.
  • Databricks does not use a Hadoop cluster or Yarn resource manager behind the scenes.
  • Databricks is designed for developing enterprise-grade data engineering solutions on the cloud, and is not available for on-premise solutions.

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