Apache Spark Cluster on Docker

Apache Spark is arguably the most popular big data processing engine. With more than 25k stars on GitHub, the framework is an excellent starting point to learn parallel computing in distributed systems using Python, Scala and R.

To get started, you can run Apache Spark on your machine by using one of the many great Docker distributions available out there. Jupyter offers an excellent _dockerized _Apache Spark with a JupyterLab interface but misses the framework distributed core by running it on a single container. Some GitHub projects offer a distributed cluster experience however lack the JupyterLab interface, undermining the usability provided by the IDE.

I believe a comprehensive environment to learn and practice Apache Spark code must keep its distributed nature while providing an awesome user experience.

This article is all about this belief.

In the next sections, I will show you how to build your own cluster. By the end, you will have a fully functional Apache Spark cluster built with Docker and shipped with a Spark master node, two Spark worker nodes and a JupyterLab interface. It will also include the Apache Spark Python API (PySpark) and a simulated Hadoop distributed file system (HDFS).

TL;DR

This article shows how to build an Apache Spark cluster in standalone mode using Docker as the infrastructure layer. It is shipped with the following:

  • Python 3.7 with PySpark 3.0.0 and Java 8;
  • Apache Spark 3.0.0 with one master and two worker nodes;
  • JupyterLab IDE 2.1.5;
  • Simulated HDFS 2.7.

To make the cluster, we need to create, build and compose the Docker images for JupyterLab and Spark nodes. You can skip the tutorial by using the **out-of-the-box distribution **hosted on my GitHub.

Requirements

Docker 1.13.0+;

Docker Compose 3.0+.

Table of contents

  1. Cluster overview;
  2. Creating the images;
  3. Building the images;
  4. Composing the cluster;
  5. Creating a PySpark application.

#overviews #apache spark #docker #python #apache

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Buddha Community

Apache Spark Cluster on Docker

Apache Spark Cluster on Docker

Apache Spark is arguably the most popular big data processing engine. With more than 25k stars on GitHub, the framework is an excellent starting point to learn parallel computing in distributed systems using Python, Scala and R.

To get started, you can run Apache Spark on your machine by using one of the many great Docker distributions available out there. Jupyter offers an excellent _dockerized _Apache Spark with a JupyterLab interface but misses the framework distributed core by running it on a single container. Some GitHub projects offer a distributed cluster experience however lack the JupyterLab interface, undermining the usability provided by the IDE.

I believe a comprehensive environment to learn and practice Apache Spark code must keep its distributed nature while providing an awesome user experience.

This article is all about this belief.

In the next sections, I will show you how to build your own cluster. By the end, you will have a fully functional Apache Spark cluster built with Docker and shipped with a Spark master node, two Spark worker nodes and a JupyterLab interface. It will also include the Apache Spark Python API (PySpark) and a simulated Hadoop distributed file system (HDFS).

TL;DR

This article shows how to build an Apache Spark cluster in standalone mode using Docker as the infrastructure layer. It is shipped with the following:

  • Python 3.7 with PySpark 3.0.0 and Java 8;
  • Apache Spark 3.0.0 with one master and two worker nodes;
  • JupyterLab IDE 2.1.5;
  • Simulated HDFS 2.7.

To make the cluster, we need to create, build and compose the Docker images for JupyterLab and Spark nodes. You can skip the tutorial by using the **out-of-the-box distribution **hosted on my GitHub.

Requirements

Docker 1.13.0+;

Docker Compose 3.0+.

Table of contents

  1. Cluster overview;
  2. Creating the images;
  3. Building the images;
  4. Composing the cluster;
  5. Creating a PySpark application.

#overviews #apache spark #docker #python #apache

Edureka Fan

Edureka Fan

1606982795

What is Apache Spark? | Apache Spark Python | Spark Training

This Edureka “What is Apache Spark?” video will help you to understand the Architecture of Spark in depth. It includes an example where we Understand what is Python and Apache Spark.

#big-data #apache-spark #developer #apache #spark

Anil  Sakhiya

Anil Sakhiya

1595141479

Apache Spark For Beginners In 3 Hours | Apache Spark Training

In this Apache Spark For Beginners, we will have an overview of Spark in Big Data. We will start with an introduction to Apache Spark Programming. Then we will move to know the Spark History. Moreover, we will learn why Spark is needed and covers everything that an individual needed to master its skill in this field. In this Apache Spark tutorial, you will not only learn Spark from the basics but also through this Apache Spark tutorial, you will get to know the Spark architecture and its components such as Spark Core, Spark Programming, Spark SQL, Spark Streaming, and much more.

This “Spark Tutorial” will help you to comprehensively learn all the concepts of Apache Spark. Apache Spark has a bright future. Many companies have recognized the power of Spark and quickly started worked on it. The primary importance of Apache Spark in the Big data industry is because of its in-memory data processing. Spark can also handle many analytics challenges because of its low-latency in-memory data processing capability.

Spark’s shell provides you a simple way to learn the API, as well as a powerful tool to analyze data interactively. It is available in either Scala (which runs on the Java VM and is thus a good way to use existing Java libraries) or Python

This Spark tutorial will comprise of the following topics:

  • 00:00:00 - Introduction
  • 00:00:52 - Spark Fundamentals
  • 00:23:11 - Spark Architecture
  • 01:01:08 - Spark Demo

#apache-spark #apache #spark #big-data #developer

Gunjan  Khaitan

Gunjan Khaitan

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Apache Spark Tutorial For Beginners - Apache Spark Full Course

This full course video on Apache Spark will help you learn the basics of Big Data, what Apache Spark is, and the architecture of Apache Spark. Then, you will understand how to install Apache Spark on Windows and Ubuntu. You will look at the important components of Spark, such as Spark Streaming, Spark MLlib, and Spark SQL. Finally, you will get an idea about implement Spark with Python in PySpark tutorial and look at some of the important Apache Spark interview questions. Now, let’s get started and learn Apache Spark in detail.

Below topics are explained in this Apache Spark Full Course:

  1. Animated Video 01:15
  2. History of Spark 06:48
  3. What is Spark 07:28
  4. Hadoop vs spark 08:32
  5. Components of Apache Spark 14:14
  6. Spark Architecture 33:26
  7. Applications of Spark 40:05
  8. Spark Use Case 42:08
  9. Running a Spark Application 44:08
  10. Apache Spark insallation on Windows 01:01:03
  11. Apache Spark insallation on Ubuntu 01:31:54
  12. What is Spark Streaming 01:49:31
  13. Spark Streaming data sources 01:50:39
  14. Features of Spark Streaming 01:52:19
  15. Working of Spark Streaming 01:52:53
  16. Discretized Streams 01:54:03
  17. caching/persistence 02:02:17
  18. checkpointing in spark streaming 02:04:34
  19. Demo on Spark Streaming 02:18:27
  20. What is Spark MLlib 02:47:29
  21. What is Machine Learning 02:49:14
  22. Machine Learning Algorithms 02:51:38
  23. Spark MLlib Tools 02:53:01
  24. Spark MLlib Data Types 02:56:42
  25. Machine Learning Pipelines 03:09:05
  26. Spark MLlib Demo 03:18:38
  27. What is Spark SQL 04:01:40
  28. Spark SQL Features 04:03:52
  29. Spark SQL Architecture 04:07:43
  30. Spark SQL Data Frame 04:09:59
  31. Spark SQL Data Source 04:11:55
  32. Spark SQL Demo 04:23:00
  33. What is PySpark 04:52:03
  34. PySpark Features 04:58:02
  35. PySpark with Python and Scala 04:58:54
  36. PySpark Contents 05:00:35
  37. PySpark Subpackages 05:40:10
  38. Companies using PySpark 05:41:16
  39. PySpark Demo 05:41:49
  40. Spark Interview Questions 05:50:43

#bigdata #apache #spark #apache-spark

Iliana  Welch

Iliana Welch

1595249460

Docker Explained: Docker Architecture | Docker Registries

Following the second video about Docker basics, in this video, I explain Docker architecture and explain the different building blocks of the docker engine; docker client, API, Docker Daemon. I also explain what a docker registry is and I finish the video with a demo explaining and illustrating how to use Docker hub

In this video lesson you will learn:

  • What is Docker Host
  • What is Docker Engine
  • Learn about Docker Architecture
  • Learn about Docker client and Docker Daemon
  • Docker Hub and Registries
  • Simple demo to understand using images from registries

#docker #docker hub #docker host #docker engine #docker architecture #api