Apache Spark Streaming with Python and PySpark

 What you'll learn

  • Create big data streaming pipelines with Spark using Python
  • Run analytics on live Tweet data from Twitter
  • Integrate Spark Streaming with tools like Apache Kafka, used by Fortune 500 companies
  • Work with new features of the most recent version of Spark: 2.3


  • Python Programming Skills
  • A computer with decent memory & internet connection
  • Either Ubuntu, Mac OS, or Windows as an operating system


What is this course about? 

This course covers all the fundamentals about Apache Spark streaming with Python and teaches you everything you need to know about developing Spark streaming applications using PySpark, the Python API for Spark. At the end of this course, you will gain in-depth knowledge about Spark streaming and general big data manipulation skills to help your company to adapt Spark Streaming for building big data processing pipelines and data analytics applications. This course will be absolutely critical to anyone trying to make it in data science today. 

What will you learn from this Apache Spark streaming cour? 

In this Apache Spark streaming course, you'll learn the following:

An overview of the architecture of Apache Spark.

How to develop Apache Spark streaming applications with PySpark using RDD transformations and actions and Spark SQL.

How to work with Spark's primary abstraction, resilient distributed datasets(RDDs), to process and analyze large data sets.

Advanced techniques to optimize and tune Apache Spark jobs by partitioning, caching and persisting RDDs.

Analyzing structured and semi-structured data using Datasets and DataFrames, and develop a thorough understanding of Spark SQL.

How to scale up Spark Streaming applications for both bandwidth and processing speed

How to integrate Spark Streaming with cluster computing tools like Apache Kafka

How to connect your Spark Stream to a data source like Amazon Web Services (AWS) Kinesis

Best practices of working with Apache Spark streaming in the field.

Big data ecosystem overview.

Why should you learn Apache Spark streaming? 

Spark streaming is becoming incredibly popular, and with good reason. According to IBM, Ninety percent of the data in the world today has been created in the last two years alone. Our current output of data is roughly 2.5 quintillion bytes per day. The world is being immersed in data, moreso each and every day. As such, analyzing static dataframes of non-dynamic data becomes the less practical approach to more and more problems. This is where data streaming comes in, the ability to process data almost as soon as it’s produced, recognizing the time-dependency of the data.

Apache Spark streaming gives us unlimited ability to build cutting-edge applications. It is also one of the most compelling technologies of the last decade in terms of its disruption to the big data world. Spark provides in-memory cluster computing which greatly boosts the speed of iterative algorithms and interactive data mining tasks.

Spark also is a powerful engine for streaming data as well as processing it. The synergy between them makes Spark an ideal tool for processing gargantuan data firehoses.

Tons of companies, including Fortune 500 companies, are adapting Apache Spark streaming to extract meaning from massive data streams, today you have access to that same big data technology right on your desktop.

What programming language is this Apache Spark streaming course taught in? 

This Apache Spark streaming course is taught in Python. Python is currently one of the most popular programming languages in the world! It's rich data community, offering vast amounts of toolkits and features, makes it a powerful tool for data processing. Using PySpark (the Python API for Spark) you will be able to interact with Apache Spark Streaming's main abstraction, RDDs, as well as other Spark components, such as Spark SQL and much more!

Let's learn how to write Apache Spark streaming programs with PySpark Streaming to process big data sources today!

Who this course is for:

  • Python Developers looking to get better at Data Streaming
  • Managers or Senior Engineers in Data Engineering Teams
  • Spark Developers eager to expand their skills.

  Source GDrive 

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