Getting Started with Stream Processing with Spark Streaming from Pluralsight

The Spark Streaming module lets you to work with large scale streaming data using familiar batch processing abstractions. This course starts with how standard transformations and operations are performed on streams, and moves to more advanced topics.

Traditional distributed systems like Hadoop work on data stored in a file system. Jobs can run for hours, sometimes days. This is a major limitation in processing real-time data such as trends and breaking news. The Spark Streaming module extends the Spark batch infrastructure to deal with data for real-time analysis. In this course, Getting Started with Stream Processing with Spark Streaming, you'll learn the nuances of dealing with streaming data using the same basic Spark transformations and actions that work with batch processing. Next, you'll explore you how you can extend machine learning algorithms to work with streams. Finally, you'll learn the subtle details of how the streaming K-means clustering algorithm helps find patterns in data. By the end of this course, you'll feel confident in your knowledge, and you can start integrating what you've learned into your own projects.

What's inside

Syllabus

Course Overview

Getting Started with Discretized Streams

Transforming Blocks of Data with DStreams

Applying ML Algorithms on DStreams

Building a Robust Spark Streaming Application

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Know what's good

, what to watch for

, and possible dealbreakers

Demonstrates how familiar batch processing tools and concepts can be used with streaming data

Covers foundational topics such as transforming blocks of data with DStreams and applying ML algorithms on DStreams

Taught by industry experienced instructors

Focuses on building a robust Spark Streaming application

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Getting Started with Stream Processing with Spark Streaming with these activities:

Review basic data structures

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Refresh your knowledge of fundamental data structures before the course starts. This will make learning Spark Streaming's abstractions easier.

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Review arrays, linked lists, and hash tables
Practice implementing these data structures in your preferred programming language

Solve Spark Streaming coding challenges

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Practice applying Spark Streaming transformations and actions by solving coding challenges. This will reinforce your understanding of the concepts.

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Find coding challenges online or in books
Solve the challenges using Spark Streaming APIs
Review your solutions and identify areas for improvement

Follow tutorials on advanced Spark Streaming topics

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Expand your knowledge by exploring advanced Spark Streaming topics through tutorials. This will expose you to real-world use cases and techniques.

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Identify specific advanced topics you want to learn
Find reputable tutorials on those topics
Follow the tutorials and complete the exercises

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Summarize a research paper on Spark Streaming

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Deepen your understanding of Spark Streaming by summarizing a research paper. This will expose you to advanced concepts and applications.

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Find a recent research paper on Spark Streaming
Read and understand the paper
Write a summary that captures the main findings and contributions

Contribute to a Spark Streaming open-source project

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Get hands-on experience with Spark Streaming by contributing to an open-source project. This will allow you to apply your skills and learn from others.

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Find a Spark Streaming open-source project
Identify a way to contribute
Submit a pull request with your contribution

Build a real-time streaming application using Spark Streaming

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Apply your learning by building a project that solves a real-world problem using Spark Streaming. This will solidify your understanding and build your portfolio.

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Define the problem you want to solve
Design the architecture of your application
Develop the application using Spark Streaming
Deploy and test your application

Career center

Learners who complete Getting Started with Stream Processing with Spark Streaming will develop knowledge and skills that may be useful to these careers:

Data Analyst

Data Analysts work with structured and unstructured datasets to understand, clean, model, and analyze data. They leverage machine learning and statistics to find patterns and trends from which businesses can derive insights. Candidates who want to apply Spark Streaming techniques such as discretization, transforming, and building machine-learning algorithms may consider pursuing a profession as a Data Analyst.

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Data Engineer

Data Engineers build and maintain data storage and management systems for organizations. Their role may also involve data transfer, data modeling, data integration, and data security. Those who want to learn how to apply Spark Streaming and DStreams to build robust applications may find work as a Data Engineer suitable.

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Machine Learning Engineer

Machine Learning Engineers design, develop, and deploy machine-learning models to automate complex tasks, predict outcomes, and solve real-world problems. They may specialize in supervised learning, unsupervised learning, or reinforcement learning. This course on Spark Streaming may be useful to Machine Learning Engineers who wish to apply machine-learning algorithms on streaming data.

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Data Scientist

Data Scientists use data to make informed decisions that help businesses optimize their processes and maximize profits. They develop and implement machine-learning algorithms, build and tune predictive models, and create actionable insights based on patterns and trends in data. This course may be useful to Data Scientists who plan to work on streaming data as Spark Streaming gives a methodological guide to data processing, especially in use cases with time-series, sensor data, or online analytics.

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Software Engineer

Software Engineers design, develop, and maintain software systems. They work with end-users, product managers, and other stakeholders to gather requirements, develop code, test it, and deploy it in production. This course may be useful to Software Engineers who plan to use Spark Streaming in their projects.

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Statistician

Statisticians apply mathematical and statistical techniques to collect, analyze, interpret, and present data. They work in a variety of industries, including healthcare, finance, and manufacturing. This course may be useful to Statisticians who plan to use Spark Streaming to process large-scale streaming datasets.

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Data Architect

Data Architects design and build scalable data management systems for organizations. They work with stakeholders to understand data requirements, and design and implement data models and data pipelines. This course may be useful to Data Architects who plan to use Spark Streaming in their projects.

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Data Science Manager

Data Science Managers lead teams of data scientists and other data professionals. They work with stakeholders to understand business needs, and develop and implement data science strategies. This course may be useful to Data Science Managers who plan to use Spark Streaming in their projects.

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Business Analyst

Business Analysts work with stakeholders to understand business needs, and design and implement solutions to improve business processes. They may specialize in a particular industry, such as healthcare, finance, or manufacturing. This course may be useful to Business Analysts who plan to use Spark Streaming to analyze streaming data.

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Database Administrator

Database Administrators manage and maintain databases. They work with database software, such as MySQL, PostgreSQL, and Oracle, to ensure that data is stored, managed, and backed up properly. This course may be useful to Database Administrators who plan to use Spark Streaming to process streaming data.

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Data Warehouse Engineer

Data Warehouse Engineers design and build data warehouses for organizations. They work with data architects and other stakeholders to understand data requirements, and design and implement data models and data pipelines. This course may be useful to Data Warehouse Engineers who plan to use Spark Streaming in their projects.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical techniques to analyze financial data. They work with investment banks, hedge funds, and other financial institutions to develop trading strategies and make investment decisions. This course may be useful to Quantitative Analysts who plan to use Spark Streaming to analyze streaming financial data.

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Research Scientist

Research Scientists conduct research in a variety of fields, such as computer science, biology, and physics. They use scientific methods to develop new knowledge and theories. This course may be useful to Research Scientists who plan to use Spark Streaming in their research.

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Actuary

Actuaries use mathematical and statistical techniques to assess risk and uncertainty. They work in a variety of industries, including insurance, finance, and healthcare. This course may be useful to Actuaries who plan to use Spark Streaming to analyze streaming data.

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Financial Analyst

Financial Analysts analyze financial data to make investment recommendations. They work with investment banks, hedge funds, and other financial institutions to help clients make informed investment decisions. This course may be useful to Financial Analysts who plan to use Spark Streaming to analyze streaming financial data.

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Getting Started with Stream Processing with Spark Streaming

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