Real World Spark 2 – Interactive Python pyspark Core

Real World Spark 2 – Interactive Python pyspark Core, available at $19.99, has an average rating of 2.88, with 21 lectures, based on 4 reviews, and has 148 subscribers.

You will learn about Simply run a single command on your desktop, go for a coffee, and come back with a running distributed environment for cluster deployment Ability to automate the installation of software across multiple Virtual Machines Code in Python against Spark. Transformation, Actions and Spark Monitoring This course is ideal for individuals who are Software engineers who want to expand their skills into the world of distributed computing or Developers / Data Scientists who want to write/test their code against Python / Spark It is particularly useful for Software engineers who want to expand their skills into the world of distributed computing or Developers / Data Scientists who want to write/test their code against Python / Spark.

Enroll now: Real World Spark 2 – Interactive Python pyspark Core

Summary

Title: Real World Spark 2 – Interactive Python pyspark Core

Price: $19.99

Average Rating: 2.88

Number of Lectures: 21

Number of Published Lectures: 21

Number of Curriculum Items: 21

Number of Published Curriculum Objects: 21

Original Price: £89.99

Quality Status: approved

Status: Live

What You Will Learn

Simply run a single command on your desktop, go for a coffee, and come back with a running distributed environment for cluster deployment
Ability to automate the installation of software across multiple Virtual Machines
Code in Python against Spark. Transformation, Actions and Spark Monitoring

Who Should Attend

Software engineers who want to expand their skills into the world of distributed computing
Developers / Data Scientists who want to write/test their code against Python / Spark

Target Audiences

Software engineers who want to expand their skills into the world of distributed computing
Developers / Data Scientists who want to write/test their code against Python / Spark

Note : This course is built on top of the “Real World Vagrant – Build an Apache Spark Development Env! – Toyin Akin” course. So if you do not have a Spark environment already installed (within a VM or directly installed), you can take the stated course above.

Spark’s python shell provides a simple way to learn the API, as well as a powerful tool to analyze data interactively. It is available in Python. Start it by running the following anywhere within a bash terminal within the built Virtual Machine�

pyspark

Spark’s primary abstraction is a distributed collection of items called a Resilient Distributed Dataset (RDD). RDDs can be created from collections, Hadoop InputFormats (such as HDFS files) or by transforming other RDDs

Spark Monitoring and Instrumentation

While creating RDDs, performing transformations and executing actions, you will be working heavily within the monitoring view of the Web UI.

Every SparkContext�launches a web UI, by default on port 4040, that displays useful information about the application. This includes:

A list of scheduler stages and tasks
A summary of RDD sizes and memory usage
Environmental information.
Information about the running executors

Why Apache Spark …

Apache Spark�run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk. Apache Spark has an advanced DAG execution engine that supports cyclic data flow and in-memory computing. Apache Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python and R shells. Apache Spark can combine SQL, streaming, and complex analytics.

Apache Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application.

Course Curriculum

Chapter 1: Introduction to Python, Spark Core via pyspark

Lecture 1: A quick tour of Python pyspark

Lecture 2: Suggested Spark Udemy curriculum courses to follow …

Chapter 2: Author, Equipment and Compensation

Lecture 1: My experience within the Enterprise

Lecture 2: Spark job compensation for those in this field.

Lecture 3: Memory Requirements

Lecture 4: Recommended Hardware for Spark and Hadoop labs …

Chapter 3: Setup the Environment

Lecture 1: Resource files for the course

Lecture 2: Spark setup

Lecture 3: Walking through the Base Vagrant Spark Box

Lecture 4: Upgrade and Package the Vagrant Box to Spark 2

Lecture 5: Register the updated Vagrant Spark Box

Chapter 4: Interact with Spark Core (Python)

Lecture 1: Boot up and Walkthrough of the pyspark Python Environment

Lecture 2: Configure and Startup a Spark Environment for Distributed Computing

Lecture 3: Python Spark RDD, Transformations, Actions and Monitoring I

Lecture 4: Python Spark RDD, Transformations, Actions and Monitoring II

Lecture 5: Python Spark RDD, Transformations, Actions and Monitoring III

Lecture 6: Python Spark RDD, Transformations, Actions and Monitoring IV

Lecture 7: Python Spark RDD, Transformations, Actions and Monitoring V

Lecture 8: Python Spark RDD, Transformations, Actions and Monitoring VI

Lecture 9: Python Spark RDD, Transformations, Actions and Monitoring VII

Chapter 5: Conclusion

Lecture 1: Conclusion

Instructors

Toyin Akin
Big Data Engineer, Capital Markets FinTech Developer

Rating Distribution

1 stars: 1 votes
2 stars: 1 votes
3 stars: 0 votes
4 stars: 1 votes
5 stars: 1 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

Menu