Apache Spark MLlib & Ease-of Prototyping With Docker

Apache Spark is the most developed library that you can utilize for many of your Machine Learning applications. It provides the users with the ease of developing ML-based algorithms in data scientist’s favorite scientific prototyping environment Jupyter Notebooks.

The Main Differences between MapReduce HDFS & Apache Spark

MapReduce needs files to be stored in a Hadoop Distributed File System, Spark doesn’t need that and it is really really fast compared to MapReduce. In contrast, Spark uses Resilient Distributed Datasets aka RDDs to store data which highly resembles Pandas dataframes. RDDs are also where Spark shines really brightly because you can harness the mighty power of the pandas-like data preprocessing and transformation through them.

The Main Installation Choices for Using Spark

  1. You can launch an AWS Elastic Map Reduce Service and use Zeppelin Notebooks but this is a premium service and you have to deal with creating an AWS account.
  2. Databricks Ecosystem → Has its own file system and dataframe syntax, it is a service started by one of the Spark’s founders, however this will result in a vendor lock-in and it is also not free.
  3. You can install it on your local Ubuntu or Windows too, but this process is very very cumbersome.

How to Install Spark on you local environment with Docker

Disclaimer, I am not going to dive into installation details of docker on your computer here for which many tutorials are available online. This command(embedded below) instantly gives you a Jupyter notebook environment with all the bells & whistles ready to go! I can’t do enough justice to be able to start a development environment with a single command. Therefore I highly highly recommend anyone who wants to take their Data Science & Engineering skills to the next level to learn at the very least how to effectively utilize Docker Hub images.

#artificial-intelligence #python #machine-learning #apache-spark #docker

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Apache Spark MLlib & Ease-of Prototyping With Docker
Jam  Dana

Jam Dana

1596179670

Apache Spark MLlib and Ease-of Prototyping with Docker

Apache Spark is the most developed library that you can utilize for many of your Machine Learning applications. It provides the users with the ease of developing ML-based algorithms in data scientist’s favorite scientific prototyping environment Jupyter Notebooks.

The Main Differences between MapReduce HDFS & Apache Spark

MapReduce needs files to be stored in a Hadoop Distributed File System, Spark doesn’t need that and it is really really fast compared to MapReduce. In contrast, Spark uses Resilient Distributed Datasets aka RDDs to store data which highly resembles Pandas dataframes. RDDs are also where Spark shines really brightly because you can harness the mighty power of the pandas-like data preprocessing and transformation through them.

The Main Installation Choices for Using Spark

  1. You can launch an AWS Elastic Map Reduce Service and use Zeppelin Notebooks but this is a premium service and you have to deal with creating an AWS account.
  2. Databricks Ecosystem → Has its own file system and dataframe syntax, it is a service started by one of the Spark’s founders, however this will result in a vendor lock-in and it is also not free.
  3. You can install it on your local Ubuntu or Windows too, but this process is very very cumbersome.

How to Install Spark on you local environment with Docker

Disclaimer, I am not going to dive into installation details of docker on your computer here for which many tutorials are available online. This command(embedded below) instantly gives you a Jupyter notebook environment with all the bells & whistles ready to go! I can’t do enough justice to be able to start a development environment with a single command. Therefore I highly highly recommend anyone who wants to take their Data Science & Engineering skills to the next level to learn at the very least how to effectively utilize Docker Hub images.

docker run -it — rm -p 8888:8888 jupyter/pyspark-notebook

There you go! Your environment is ready, to start using Spark begin a session by:

Image for post

Every Spark Session Starts with this command

Spark MLlib & The Types of Algorithms That Are Available

You can do both supervised and unsupervised machine learning operations with Spark.

Supervised Learning has labelled data already whereas unsupervised learning does not have labelled data and thus it is more akin to seeking patterns in chaos.

  • Supervised Learning Algorithms → Classification, Regression, Gradient Boosting. The algorithms in this category have both the features column and the labels columns.
  • Unsupervised Learning Algorithms → K-means clustering, Value Decomposition, Self-organizing maps, Nearest Neighbours Mapping. With this class of algorithms you only have the features column available and no labels.

Spark Streaming Capabilities

I am planning to write an article about this in depth in the future but for the time being I just wanted to make you aware of Spark Streaming.

Image for post

Spark Streaming is a great tool for developing real-time online algorithms.

Spark Streaming can ingest data from Kafka Producers, Apache Flume, AWS’s Kinesis Firehose or TCP Websockets.

One of the most crucial advantages of Spark Streaming is that you can do map, filter, reduce and join actions on unbounded data streams.

#apache spark #docker #python

Apache Spark MLlib & Ease-of Prototyping With Docker

Apache Spark is the most developed library that you can utilize for many of your Machine Learning applications. It provides the users with the ease of developing ML-based algorithms in data scientist’s favorite scientific prototyping environment Jupyter Notebooks.

The Main Differences between MapReduce HDFS & Apache Spark

MapReduce needs files to be stored in a Hadoop Distributed File System, Spark doesn’t need that and it is really really fast compared to MapReduce. In contrast, Spark uses Resilient Distributed Datasets aka RDDs to store data which highly resembles Pandas dataframes. RDDs are also where Spark shines really brightly because you can harness the mighty power of the pandas-like data preprocessing and transformation through them.

The Main Installation Choices for Using Spark

  1. You can launch an AWS Elastic Map Reduce Service and use Zeppelin Notebooks but this is a premium service and you have to deal with creating an AWS account.
  2. Databricks Ecosystem → Has its own file system and dataframe syntax, it is a service started by one of the Spark’s founders, however this will result in a vendor lock-in and it is also not free.
  3. You can install it on your local Ubuntu or Windows too, but this process is very very cumbersome.

How to Install Spark on you local environment with Docker

Disclaimer, I am not going to dive into installation details of docker on your computer here for which many tutorials are available online. This command(embedded below) instantly gives you a Jupyter notebook environment with all the bells & whistles ready to go! I can’t do enough justice to be able to start a development environment with a single command. Therefore I highly highly recommend anyone who wants to take their Data Science & Engineering skills to the next level to learn at the very least how to effectively utilize Docker Hub images.

#artificial-intelligence #python #machine-learning #apache-spark #docker

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

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

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