Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.
Apprendre Why Apache Spark? | Spark Basics
Practice
Projects
Quizzes & Challenges
Quizzes
Challenges
/
Introduction to Big Data with Apache Spark in Python

bookWhy Apache Spark?

What is Apache Spark?

It provides a fast, general-purpose engine for big data processing, capable of handling both batch and real-time data processing tasks.

Spark was developed to overcome the limitations of traditional MapReduce frameworks and offers advanced features like in-memory processing, support for complex analytics, and seamless integration with various data sources.

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

Key Features

  • In-Memory Computing - processes data in memory rather than on disk, which accelerates performance for iterative algorithms and interactive queries.
  • Unified Analytics Engine - supports batch processing, interactive queries, streaming analytics, and machine learning through a single framework.
  • Scalability - scales horizontally by adding more nodes to a cluster, making it capable of handling petabytes of data.

Data Structures

The primary abstractions in Spark are:

  • Resilient Distributed Dataset(RDD);
  • DataFrame;
  • Dataset.

We will discuss them in a more detailed way soon.

Tout était clair ?

Comment pouvons-nous l'améliorer ?

Merci pour vos commentaires !

Section 2. Chapitre 1

Demandez à l'IA

expand

Demandez à l'IA

ChatGPT

Posez n'importe quelle question ou essayez l'une des questions suggérées pour commencer notre discussion

Suggested prompts:

Posez-moi des questions sur ce sujet

Résumer ce chapitre

Afficher des exemples du monde réel

bookWhy Apache Spark?

Glissez pour afficher le menu

What is Apache Spark?

It provides a fast, general-purpose engine for big data processing, capable of handling both batch and real-time data processing tasks.

Spark was developed to overcome the limitations of traditional MapReduce frameworks and offers advanced features like in-memory processing, support for complex analytics, and seamless integration with various data sources.

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

Key Features

  • In-Memory Computing - processes data in memory rather than on disk, which accelerates performance for iterative algorithms and interactive queries.
  • Unified Analytics Engine - supports batch processing, interactive queries, streaming analytics, and machine learning through a single framework.
  • Scalability - scales horizontally by adding more nodes to a cluster, making it capable of handling petabytes of data.

Data Structures

The primary abstractions in Spark are:

  • Resilient Distributed Dataset(RDD);
  • DataFrame;
  • Dataset.

We will discuss them in a more detailed way soon.

Tout était clair ?

Comment pouvons-nous l'améliorer ?

Merci pour vos commentaires !

Section 2. Chapitre 1
some-alt