Best Pyspark Online Courses, Training with Certification-2019 Updated

Here Best Pyspark Online Courses

#1. The Complete PySpark Developer Course

The Complete PySpark Developer Course. Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. The Spark Python API (PySpark) exposes the apache-spark programming model to Python. This course will show you how to leverage the power of Python and put it to use in the Spark ecosystem. You will start by getting a firm understanding of the Spark architecture and how to set up a Python environment for Spark.

In this course, you’ll learn:

  • Build machine learning models with MLlib and ML
  • Learn about Apache Spark and the Spark architecture
  • Deploy locally built applications to a cluster
  • Build and interact with Spark DataFrames using Spark SQL
  • Learn how to submit your applications programmatically using spark-submit
  • Read, transform, and understand data and use it to train machine learning models

At the end of this course, you will have established a firm understanding of the Spark Python API and how it can be used to build data-intensive applications. So let’s get started!

  • Build machine learning models with MLlib and ML
  • Learn about Apache Spark and the Spark architecture
  • Deploy locally built applications to a cluster
  • Build and interact with Spark DataFrames using Spark SQL
  • Learn how to submit your applications programmatically using spark-submit
  • Read, transform, and understand data and use it to train machine learning models

#2. Spark and Python for Big Data with PySpark

This course will teach the basics with a crash course in Python, continuing on to learning how to use Spark DataFrames with the latest Spark 2.0 syntax! Once we’ve done that we’ll go through how to use the MLlib Machine Library with the DataFrame syntax and Spark. All along the way you’ll have exercises and Mock Consulting Projects that put you right into a real world situation where you need to use your new skills to solve a real problem!

One of the most valuable technology skills is the ability to analyze huge data sets, and this course is specifically designed to bring you up to speed on one of the best technologies for this task, Apache Spark! The top technology companies like Google, Facebook, Netflix, Airbnb, Amazon, NASA, and more are all using Spark to solve their big data problems!

and also..

  • Use Python and Spark together to analyze Big Data
  • Learn how to use the new Spark 2.0 DataFrame Syntax
  • Work on Consulting Projects that mimic real world situations!
  • Classify Customer Churn with Logisitic Regression
  • Use Spark with Random Forests for Classification
  • Learn how to use Spark’s Gradient Boosted Trees
  • Use Spark’s MLlib to create Powerful Machine Learning Models
  • Learn about the DataBricks Platform!
  • Get set up on Amazon Web Services EC2 for Big Data Analysis
  • Learn how to use AWS Elastic MapReduce Service!
  • Learn how to leverage the power of Linux with a Spark Environment!
  • Create a Spam filter using Spark and Natural Language Processing!
  • Use Spark Streaming to Analyze Tweets in Real Time!

#3. HDPCD:Spark using Python (pyspark)

Course cover the overall syllabus of HDPCD:Spark Certification.

  • Python Fundamentals – Basic Python programming required using REPL
  • Getting Started with Spark – Different setup options, setup process
  • Core Spark – Transformations and Actions to process the data
  • Data Frames and Spark SQL – Leverage SQL skills on top of Data Frames created from Hive tables or RDD
  • One month complementary lab access
  • Exercises – A set of self evaluated exercises to test skills for certification purpose

After the course one will gain enough confidence to give the certification and crack it.

and also..

Basics of Python, Spark and required skills to give HDPCD:Spark certification using Python/pyspark with confidence

#4. Apache Spark with Python – Big Data with PySpark and Spark

This course covers all the fundamentals about Apache Spark with Python and teaches you everything you need to know about developing Spark applications using PySpark, the Python API for Spark. At the end of this course, you will gain in-depth knowledge about Apache Spark and general big data analysis and manipulations skills to help your company to adapt Apache Spark for building big data processing pipeline and data analytics applications.

This course covers 10+ hands-on big data examples. You will learn valuable knowledge about how to frame data analysis problems as Spark problems. Together we will learn examples such as aggregating NASA Apache web logs from different sources; we will explore the price trend by looking at the real estate data in California; we will write Spark applications to find out the median salary of developers in different countries through the Stack Overflow survey data; we will develop a system to analyze how maker spaces are distributed across different regions in the United Kingdom.  And much much more.

  • An overview of the architecture of Apache Spark.
  • Develop Apache Spark 2.0 applications using RDD transformations and actions and Spark SQL.
  • Work with Apache Spark’s primary abstraction, resilient distributed datasets (RDDs) to process and analyze large data sets.
  • Analyze structured and semi-structured data using DataFrames, and develop a thorough understanding about Spark SQL.
  • Advanced techniques to optimize and tune Apache Spark jobs by partitioning, caching and persisting RDDs.
  • Scale up Spark applications on a Hadoop YARN cluster through Amazon’s Elastic MapReduce service.
  • Share information across different nodes on a Apache Spark cluster by broadcast variables and accumulators.
  • Write Spark applications using the Python API – PySpark
We Advice you to learn via Online Courses, Rather than Books, But We Suggest you use Books Only for reference purpose

Best Pyspark Books:

#1 PySpark Recipes: A Problem-Solution Approach with PySpark2 by Raju Kumar Mishra

#2 PySpark Cookbook: Over 60 recipes for implementing big data processing and analytics using Apache Spark and Python by Denny Lee

#3 PySpark SQL Recipes: With HiveQL, Dataframe and Graphframes by Raju Kumar Mishra

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