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

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

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

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

Gunjan  Khaitan

Gunjan Khaitan

1582649280

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