Streaming Big Data with Spark Streaming & Scala - Hands On!

Course Overview

This course provides an overview of Spark Streaming and Scala, and how to use them to process massive streams of real-time data. Participants will learn how to integrate Spark Streaming with data sources such as Kafka, Flume, and Kinesis, and use Spark 2's Structured Streaming API. They will also learn how to create Spark applications using the Scala programming language, and output transformed real-time data to Cassandra or file systems. Additionally, participants will learn how to integrate Spark Streaming with Spark SQL to query streaming data in real time, train machine learning models with streaming data, and use those models for real-time predictions. They will also learn how to ingest Apache access log data and transform streams of it, receive real-time streams of Twitter feeds, maintain stateful data across a continuous stream of input data, and query streaming data across sliding windows of time.

[Applications]
The application of this course can be seen in many areas. It can be used to process massive streams of real-time data using Spark Streaming, integrate Spark Streaming with data sources, create Spark applications using the Scala programming language, output transformed real-time data to Cassandra or file systems, integrate Spark Streaming with Spark SQL to query streaming data in real time, train machine learning models with streaming data, ingest Apache access log data and transform streams of it, receive real-time streams of Twitter feeds, maintain stateful data across a continuous stream of input data, and query streaming data across sliding windows of time.

[Career Path]
One job position path that could be recommended to learners of this course is a Big Data Streaming Engineer. This role involves designing, developing, and maintaining streaming data pipelines to process massive streams of real-time data. The engineer would be responsible for integrating Spark Streaming with data sources such as Kafka, Flume, and Kinesis, as well as using Spark 2's Structured Streaming API to create Spark applications using the Scala programming language. Additionally, the engineer would be responsible for outputting transformed real-time data to Cassandra or file systems, integrating Spark Streaming with Spark SQL to query streaming data in real time, and training machine learning models with streaming data.

The development trend for this role is increasing demand for Big Data Streaming Engineers as more and more companies are looking to leverage streaming data to gain insights and make decisions in real time. Companies are also looking for engineers who are well-versed in the latest technologies and tools, such as Spark Streaming, Kafka, and Scala, to ensure that their streaming data pipelines are efficient and reliable. As such, engineers who are knowledgeable in these technologies and have experience in developing streaming data pipelines will be in high demand.

[Education Path]
The recommended educational path for learners of this course is to pursue a Bachelor's degree in Computer Science or a related field. This degree will provide learners with the foundational knowledge and skills needed to understand and work with streaming big data using Spark Streaming and Scala.

The Bachelor's degree in Computer Science will cover topics such as programming languages, software engineering, computer architecture, operating systems, databases, and computer networks. Learners will also learn about data structures, algorithms, artificial intelligence, and machine learning.

In addition to the core topics, learners will also learn about streaming big data with Spark Streaming and Scala. This will include topics such as data sources, Spark 2's Structured Streaming API, creating Spark applications using Scala, outputting transformed real-time data, integrating Spark Streaming with Spark SQL, training machine learning models with streaming data, ingesting Apache access log data, receiving real-time streams of Twitter feeds, maintaining stateful data, and querying streaming data across sliding windows of time.

The development trend for this educational path is to focus on the use of big data and machine learning technologies. As the demand for data-driven insights and decisions increases, the need for professionals who can work with streaming big data and machine learning technologies will also increase. Therefore, learners should focus on developing their skills in these areas in order to stay ahead of the curve.