Learn Apache Spark with Python

Learn Apache Spark with Python, available at $44.99, has an average rating of 3.5, with 74 lectures, based on 30 reviews, and has 273 subscribers.

You will learn about Introduction to Pyspark Filtering RDDs Install and run Apache Spark on a desktop computer or on a cluster Understand how Spark SQL lets you work with structured data Understanding Spark with Examples and many more This course is ideal for individuals who are Java Developers who want to upgrade their skills to light weight language python to handle Big data. or Hadoop developers who want to learn a fast processing engine SPARK or Python developers who want to upgrade their skills to handle and process Big data using Apache Spark. or Any professionals or students who want to learn Big data. It is particularly useful for Java Developers who want to upgrade their skills to light weight language python to handle Big data. or Hadoop developers who want to learn a fast processing engine SPARK or Python developers who want to upgrade their skills to handle and process Big data using Apache Spark. or Any professionals or students who want to learn Big data.

Enroll now: Learn Apache Spark with Python

Summary

Title: Learn Apache Spark with Python

Price: $44.99

Average Rating: 3.5

Number of Lectures: 74

Number of Published Lectures: 74

Number of Curriculum Items: 74

Number of Published Curriculum Objects: 74

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Introduction to Pyspark

Filtering RDDs

Install and run Apache Spark on a desktop computer or on a cluster

Understand how Spark SQL lets you work with structured data

Understanding Spark with Examples and many more

Who Should Attend

Java Developers who want to upgrade their skills to light weight language python to handle Big data.

Hadoop developers who want to learn a fast processing engine SPARK

Python developers who want to upgrade their skills to handle and process Big data using Apache Spark.

Any professionals or students who want to learn Big data.

Target Audiences

Java Developers who want to upgrade their skills to light weight language python to handle Big data.

Hadoop developers who want to learn a fast processing engine SPARK

Python developers who want to upgrade their skills to handle and process Big data using Apache Spark.

Any professionals or students who want to learn Big data.

Apache Spark is the hottest Big Data skill today. More and more organizations are adapting Apache Spark for building their big data processing and analytics applications and the demand for Apache Spark professionals is sky rocketing. Learning Apache Spark is a great vehicle to?good jobs, better quality of work and the best remuneration packages.?

You might already know Apache Spark as a fast and general engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing. It’s well-known for its speed, ease of use, generality and the ability to run virtually everywhere. And even though Spark is one of the most asked tools for data engineers, also data scientists can benefit from Spark when doing exploratory data analysis, feature extraction, supervised learning and model evaluation.?

The course will cover many more topics of Apache Spark with Python including-

What makes Spark a power tool of Big Data and Data Science?

Learn the fundamentals of Spark including Resilient Distributed Datasets, Spark Actions and Transformations

Explore Spark SQL with CSV, JSON and mySQL (JDBC) data sources

Convenient links to download all source code

Course Curriculum

Chapter 1: Module 1 Introduction to Spark with Python

Lecture 1: Introduction to the Module

Lecture 2: What is PySpark

Lecture 3: PySpark in Industry

Lecture 4: Why to go for Python

Chapter 2: Module 2 Introduction to Big Data and Hadoop

Lecture 1: Big Data Overview

Lecture 2: Facts about Big Data

Lecture 3: Big Data Scenarios

Lecture 4: Apache Hadoop Framework

Lecture 5: Top Hadoop Users

Lecture 6: HISTORY OF HADOOP

Lecture 7: Difference between RDBMS and Hadoop

Lecture 8: Cluster Modes in Hadoop

Lecture 9: Hadoop Ecosystem

Lecture 10: HDFS Daemons and Mapreduce daemons

Lecture 11: HADOOP CLUSTER ARCHITECTURE

Lecture 12: Top Reasons Why you should Learn Hadoop

Lecture 13: Hadoop distributions and compatibilities

Lecture 14: Hadoop Ecosystem in Detail

Lecture 15: Hadoop Distributed File System

Lecture 16: HDFS Files and Blocks

Lecture 17: HDFS components and architecture

Lecture 18: HDFS File Read and Write

Chapter 3: Module 3 Apache Spark Framework

Lecture 1: Batch and Real Time Analytics

Lecture 2: Why Spark when Hadoop is Already there

Lecture 3: Introduction to Apache Spark

Lecture 4: Features of Apache Spark

Lecture 5: Users and Use Cases of Apache Spark

Lecture 6: Job Execution Flow and Spark Execution

Lecture 7: Spark Unified Stack

Lecture 8: Complete Picture of Apache Spark

Lecture 9: Apache spark Architecture

Lecture 10: Top Companies Using Spark

Chapter 4: Module 4 Python Programming Language

Lecture 1: Getting Started with Python

Lecture 2: Introduction to Python

Lecture 3: Advantages and facts about python

Lecture 4: First python program

Lecture 5: Program execution and python IDE

Lecture 6: Built in types in python

Lecture 7: Numbers Data Type in Python

Lecture 8: String and List Data Type

Lecture 9: Dictionary, Tuples and Sets

Lecture 10: Variables and assignment

Lecture 11: Hands-On

Lecture 12: Hands-On

Lecture 13: Hands-On

Lecture 14: Hands-On

Lecture 15: Hands-On

Chapter 5: Module 5 Advanced Part of Apache Spark with Python

Lecture 1: Downloading and Installing Enthought Canopy

Lecture 2: Downloading and Installing jdk

Lecture 3: Downloading and Installing Spark

Lecture 4: Downloading and Setup of winutils

Lecture 5: Setting up Environment Variables

Lecture 6: Running the first Spark Program

Lecture 7: Downloading and Extracting movie ratings datasets

Lecture 8: Running Ratings Counter Spark Program

Lecture 9: Understanding key value pairs with an example

Lecture 10: Filtering RDD using an example

Lecture 11: Finding maximum temperature by location

Lecture 12: Map vs FlatMap

Lecture 13: Understanding FlatMap using Word Count example

Lecture 14: Sorting the word count results

Lecture 15: Total Amount Spent Example

Lecture 16: Sorting the Total Amount Spent Example result

Chapter 6: Module 6 Deep Dive Into Spark with Python

Lecture 1: Most popular movie example

Lecture 2: Understanding Broadcast Variables with an example

Lecture 3: Finding Similar Movies Example

Lecture 4: Finding Most Popular Superhero example

Lecture 5: Superhero Degrees of Separation Part1

Lecture 6: Superhero Degrees of Separation Part 2

Chapter 7: Module 7 SparkSQL in Apache Spark with Python

Lecture 1: Executing SQL commands

Lecture 2: Using SQL style functions instead of queries

Lecture 3: Using DataFrames instead of RDDs

Chapter 8: Module 8 MLib in Apache Spark with Python

Lecture 1: Using MLlib to produce movie recommendations

Lecture 2: Using Dataframe with MLlib using an example

Instructors

Edulearners Technologies
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Rating Distribution

1 stars: 2 votes

2 stars: 5 votes

3 stars: 8 votes

4 stars: 6 votes

5 stars: 9 votes

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