Apache Spark Streaming with Python and PySpark: Add Spark Streaming to your data science and machine learning Python projects [Video]

Spark Streaming is becoming incredibly popular, and with good reason. According to IBM, 90% of the data in the World today was 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, more so each and every day. As such, analyzing static DataFrames for non-dynamic data is becoming less and less of a 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 an 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 in 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 fire hoses. 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. This Apache Spark Streaming course is taught in Python. Python is currently one of the most popular programming languages in the World! Its 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!

An overview of the Apache Spark architecture. How to develop Apache Spark Streaming applications with PySpark using RDD transformations and actions and Spark SQL, 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 and to integrate Spark Streaming with cluster computing tools such as Apache Kafka, to connect your Spark stream to a data source such as Amazon Web Services (AWS) Kinesis Best practices for working with Apache Spark Streaming in the field and Big data ecosystem overview.