Streaming Big Data with Spark Streaming, Scala, and Spark 3!

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Details

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Provider

Coursera
Pricing

Free Online Course (Audit)
Languages

English
Certificate

Paid Certificate Available
Duration & workload

8 hours 16 minutes
Sessions

On-Demand
Level

Advanced
Subtitles

English

Found in

Overview

Class Central Tips

In the fast-evolving world of big data, the ability to process streaming data in real time is essential. This course is meticulously designed to take you from the basics of Spark and Scala to advanced real-time data processing with Spark Streaming. We begin with a foundational setup of your development environment, ensuring you are equipped to run Spark and Scala on your desktop. A hands-on activity will introduce you to the excitement of live data by streaming and analyzing real-time Tweets. As we move forward, you’ll gain a solid understanding of Scala, a language integral to working with Spark. This crash course in Scala covers the essentials: variables, data structures, and flow control, with practical exercises to cement your understanding. With a firm grip on Scala, you’ll delve into the core concepts of Spark, including the Resilient Distributed Dataset (RDD), which forms the backbone of Spark Streaming applications. We will then explore Spark Streaming in detail, from its architecture to fault tolerance mechanisms, using engaging examples like tracking Twitter hashtags and analyzing Apache logs. Finally, the course pushes the boundaries of your knowledge with advanced topics such as integrating Spark Streaming with Kafka, Flume, and Cassandra. You'll also tackle stateful information tracking, real-time machine learning with K-Means clustering, and deploying your applications on a real Hadoop cluster. By the end of this course, you’ll not only understand the theory behind Spark Streaming but will have the practical experience to apply it effectively in production environments. This course is ideal for software developers, data engineers, and data scientists with a basic understanding of programming concepts. Prior experience with Java, Python, or any object-oriented programming language is recommended but not required. Familiarity with big data concepts will be helpful but is not mandatory.

Syllabus

Getting Started

In this module, we will introduce you to the course, guide you through setting up your development environment, and ensure that Spark and Scala are installed correctly on your system. You will also dive into a hands-on activity where you will stream live Tweets using Spark Streaming.

A Crash Course in Scala

In this module, we will dive into the fundamentals of Scala, starting with the basics like variables and flow control. You’ll then progress to functions and essential data structures, equipping you with the Scala knowledge necessary to work effectively with Spark.

Spark Streaming Concepts

In this module, we will build upon your knowledge of Spark and introduce you to Spark Streaming in detail. You'll explore key concepts like RDDs, windowing, and fault tolerance while running hands-on activities that solidify your understanding of real-time data processing.

Spark Streaming Examples with Twitter

In this module, we will apply Spark Streaming to real-world scenarios using Twitter data. You'll progress from saving tweets to disk, to computing tweet statistics like average length, and finally, tracking trending hashtags in real-time, providing practical insights into Spark Streaming's capabilities.

Spark Streaming Examples with Clickstream / Apache Access Log Data

In this module, we will dive into practical examples of Spark Streaming with Apache access logs and clickstream data. You'll learn to track popular URLs, monitor errors, integrate Spark Streaming with SQL, and explore Structured Streaming to analyze logs in real time, giving you a robust toolkit for handling streaming data.

Integrating with Other Systems

In this module, we will explore how to integrate Spark Streaming with various external systems like Apache Kafka, Apache Flume, Amazon Kinesis, and Cassandra. You’ll also learn how to create custom data receivers for proprietary systems, giving you the skills to connect Spark Streaming with almost any data source.

Advanced Spark Streaming Examples

In this module, we will delve into advanced Spark Streaming concepts, focusing on stateful information processing and the integration of machine learning techniques like K-means clustering and linear regression. You'll gain hands-on experience with these powerful tools, enabling you to build sophisticated real-time data processing applications.

Spark Streaming in Production

In this module, we will take your Spark Streaming applications to production, covering the essentials of packaging, deploying, and running your code on real clusters. You'll learn how to manage dependencies with SBT, deploy on Amazon EMR, and troubleshoot and optimize your jobs for reliable, high-performance operation in a production environment.

You Made It!

In this module, we will offer you guidance on how to continue your learning journey with Spark Streaming, including valuable resources and next steps. You'll also reflect on your progress and plan how to integrate your newfound knowledge into real-world applications.