Windowing and Join Operations on Streaming Data with Apache Spark on Databricks from Pluralsight

This course will teach you how to leverage windowing, watermarking, and join operations on streaming data in Spark for your specific use cases.

Structured Streaming in Apache Spark treats real-time data as a table that is being constantly appended. In such a stream processing model the burden of stream processing shifts from the user to the system, making it very easy and intuitive to process streaming data with Spark. Apache Spark supports a range of windowing and join operations on streaming data using processing time and event time.

In this course, Windowing and Join Operations on Streaming Data with Apache Spark on Databricks, you will learn the difference between stateless operations that operate on a single streaming entity and stateful operations that operate on multiple entities accumulated in a stream. Then, you will explore the different kinds of windows supported by Apache Spark which includes tumbling windows, sliding windows, and global windows.

Next, you will understand the differences between event time, ingestion time, and processing time and see how you can perform windowing operations using both processing time as well as event time. Along the way, you will connect to an HDInsight Kafka cluster to read records for your input stream. You will then use watermarking to deal with late-arriving data and see how you can use watermarks to limit the state that Apache Spark stores.

Finally, you will perform join operations using streams and explore the types of joins that Spark supports for static-stream joins and stream-stream joins. You will also see how you can connect to Azure Event Hubs to read records.

When you are finished with this course, you will have the skills and knowledge of windowing and join operations needed to identify when these powerful transformations should be performed and how they are performed.

What's inside

Syllabus

Course Overview

Performing Windowing Operations on Data

Exploring Aggregations Using Watermarks

Performing Join Operations on Data

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores windowing, which is standard in data engineering

Develops essential streaming data processing skills for data engineers

Teaches skills that are relevant to data analysts

This course is taught by Janani Ravi, who is recognized for their work in data engineering

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 Windowing and Join Operations on Streaming Data with Apache Spark on Databricks with these activities:

Review Course Materials

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Prepare for learning by understanding the course structure and content dependencies.

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Review the syllabus to understand the order of lecture topics.
Read any preparatory materials provided by the instructor.
Set up a system to organize assignments and workload.
Connect with classmates through online forums or social media.

Organize and review course notes, assignments, and quizzes

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Enhance your retention of key concepts by regularly reviewing and organizing course materials, ensuring a solid foundation for building your knowledge.

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Gather and organize course notes, assignments, and quizzes.
Review the materials regularly to reinforce concepts.
Identify areas for further study or clarification.

Organize and review course materials

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Enhance your learning experience by organizing and reviewing course materials, ensuring you have a comprehensive understanding of the key concepts and resources provided.

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Gather all the lecture notes, assignments, quizzes, and other course materials.
Review and整理 the materials, identifying key points and areas where you need further clarification.

18 other activities

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Read 'The Data Warehouse Toolkit' by Ralph Kimball

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Familiarize yourself with the concepts of data warehouse design and dimensional modeling, which are essential for understanding Apache Spark's structured streaming capabilities.

View The Data Warehouse Toolkit on Amazon

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Read Chapters 1-3 to understand the basics of data warehousing.
Review the section on dimensional modeling in Chapter 4.
Complete the exercises at the end of each chapter.

Review core concepts of stream processing

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By reviewing the fundamentals of stream processing and its key concepts, you'll reinforce your understanding and strengthen your foundation for the course.

Browse courses on Streaming Data Processing

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Go through your notes or textbooks to refresh your memory on topics like real-time data processing, stream processing architectures, and event-driven systems.
Consider taking an online tutorial or watching videos to supplement your review and cover any gaps in your understanding.

Attend meetups or online events on Apache Spark and streaming data

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Connect with other professionals in the field, expand your knowledge, and gain different perspectives on windowing and streaming techniques.

Browse courses on Apache Spark

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Identify relevant meetups or online events.
Attend the events and engage with speakers and attendees.
Share your own experiences and insights.

Form a study group with classmates

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Enhance your understanding and retention of course material by forming a study group with classmates to discuss concepts, work on assignments together, and provide mutual support.

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Identify classmates who share similar goals and interests and invite them to join your study group.
Establish regular meeting times and create a structured plan for your study sessions, including topic discussions, problem-solving, and knowledge sharing.

Timed Coding Assignments

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Sharpen programming skills and improve fluency in writing streaming data processing code.

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Set a timer for 15-30 minutes to practice coding challenges.
Focus on implementing specific concepts covered in class.
Review solutions and identify areas for improvement.
Repeat the process regularly to build proficiency.

Explore Apache Spark documentation and tutorials on windowing and streaming

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Supplement your learning by exploring official Spark documentation and tutorials, deepening your understanding of windowing and streaming capabilities.

Browse courses on Apache Spark

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Browse the Apache Spark documentation on windowing operations.
Follow tutorials on how to use Spark for streaming data processing.
Experiment with different windowing techniques and streaming scenarios.

Practice windowing operations in Apache Spark

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Reinforce your understanding of windowing operations by working through practice problems and examples, ensuring you're comfortable applying them to real-world scenarios.

Browse courses on Windowing Functions

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Set up a practice environment with Apache Spark and run sample code to experiment with different windowing functions.
Create your own streaming dataset and apply windowing operations to it, experimenting with various window types and parameters.

Build a simple Spark streaming application that performs windowing operations

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Gain hands-on experience with windowing operations in Spark, allowing you to apply these concepts to your own streaming data use cases.

Browse courses on Windowing Operations

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Set up a Spark streaming environment.
Create a simple data stream.
Apply a windowing operation to the data stream.
Output the results of the windowing operation.

Develop a presentation on windowing and join operations

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深化 your understanding of windowing and join operations by creating a presentation that explains these concepts and their applications to real-world scenarios.

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Research and gather information on windowing and join operations in Apache Spark.
Structure your presentation logically, including an introduction, explanation of concepts, examples, and a conclusion.
Practice delivering your presentation and incorporate visual aids and examples to enhance audience engagement.

Solve exercises and problems related to windowing and streaming

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Reinforce your understanding and develop your problem-solving skills through dedicated practice, ensuring better retention and application of windowing and streaming concepts.

Browse courses on Windowing Operations

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Identify practice problems and exercises.
Attempt to solve the problems on your own.
Review your solutions and identify areas for improvement.

Attend a workshop on Apache Spark streaming

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Enhance your knowledge and connect with experts by attending a workshop focused on Apache Spark streaming, enabling you to learn from industry professionals and gain practical insights.

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Research and identify upcoming Apache Spark streaming workshops in your area or online.
Register for the workshop and actively participate in the sessions, engaging with instructors and fellow attendees.

Windowing Operations Exercises

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Reinforce your understanding of windowing operations by working through practice exercises.

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Solve problems using tumbling windows
Solve problems using sliding windows
Solve problems using global windows

Practice solving LeetCode problems related to windowing and streaming

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Sharpen your coding skills and reinforce your understanding of windowing operations by solving challenging LeetCode problems.

Browse courses on Windowing Operations

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Identify LeetCode problems that focus on windowing operations.
Solve the problems using the concepts learned in the course.
Review your solutions and identify areas for improvement.

Join Operations Exercises

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Sharpen your skills on join operations through guided, hands-on exercises.

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Perform static-stream joins
Perform stream-stream joins

Explore advanced join operations in Apache Spark

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Gain deeper insights into advanced join operations supported by Apache Spark, enabling you to effectively combine data from multiple sources and handle complex data scenarios.

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Find online tutorials or documentation that cover advanced join types and techniques in Apache Spark, such as broadcast joins, shuffled hash joins, and sort-merge joins.
Experiment with these advanced join operations using sample datasets and analyze their performance characteristics.

Create a Visual Explanation

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Deepen your understanding by creating a visual representation explaining windowing and join operations in Spark.

Browse courses on Windowing

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Choose a specific windowing or join concept
Develop a visual representation (e.g., diagram, animation, infographic)
Annotate the visual with clear descriptions and explanations

Create a collection of resources on windowing and streaming in Apache Spark

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Consolidate and expand your knowledge by compiling a comprehensive collection of resources on windowing and streaming, serving as a valuable reference for future use.

Browse courses on Windowing Operations

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Gather resources from various sources, including articles, tutorials, and documentation.
Organize the resources into a logical structure.
Annotate the resources with your own notes and insights.

Build a Spark Streaming Application

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Demonstrate your proficiency by building a real-world Spark Streaming application that implements windowing and join operations.

Browse courses on Spark Streaming

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Define the problem statement and requirements
Design the streaming architecture
Implement windowing and join operations in Spark
Test and validate the application

Career center

Learners who complete Windowing and Join Operations on Streaming Data with Apache Spark on Databricks will develop knowledge and skills that may be useful to these careers:

Data Engineer

In the realm of Big Data, Data Engineers are the architects of data pipelines and streaming systems. These pipelines process massive amounts of data in real-time, providing crucial insights for businesses to make informed decisions. The course, 'Windowing and Join Operations on Streaming Data with Apache Spark on Databricks,' equips you with the essential skills to excel in this role. By mastering windowing operations, watermarking, and join techniques, you'll gain the expertise to craft efficient and reliable data pipelines for streaming data.

See salaries and explore the career path for Data Engineer

Data Scientist

Data Scientists leverage statistical models and machine learning algorithms to extract meaningful insights from data. This course provides a solid foundation for Data Scientists who wish to specialize in streaming data analysis. The course delves into event time processing, watermarking, and join operations on streaming data, equipping you to develop real-time data pipelines for applications such as fraud detection, anomaly detection, and predictive analytics.

See salaries and explore the career path for Data Scientist

Software Engineer

Software Engineers design, develop, and maintain software systems. By taking this course, Software Engineers can enhance their skillset in working with streaming data. They'll learn how to perform windowing operations, apply watermarking techniques, and execute join operations on streaming data. This knowledge enables them to build robust and scalable software applications that can handle real-time data processing.

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Data Analyst

Data Analysts play a crucial role in transforming raw data into actionable insights. This course is tailored to equip Data Analysts with the skills to handle streaming data. By mastering windowing operations and join techniques, they can extract meaningful insights from real-time data streams. This expertise empowers them to provide timely and valuable insights to businesses, enabling them to make informed decisions.

See salaries and explore the career path for Data Analyst

Big Data Architect

Big Data Architects design and manage large-scale data systems. This course provides them with a deep understanding of windowing operations, watermarking, and join techniques in Apache Spark. These skills are essential for building scalable and efficient data pipelines for real-time data processing. By mastering these techniques, Big Data Architects can create systems that can handle the challenges of high-volume, high-velocity data streams.

See salaries and explore the career path for Big Data Architect

Machine Learning Engineer

Machine Learning Engineers specialize in developing and deploying machine learning models. This course provides them with a foundation in working with streaming data. It covers windowing operations, watermarking, and join techniques, which are critical for processing and analyzing real-time data. By gaining expertise in these techniques, Machine Learning Engineers can develop more accurate and responsive machine learning models for various applications, such as fraud detection and anomaly detection.

See salaries and explore the career path for Machine Learning Engineer

Cloud Architect

Cloud Architects design and manage cloud computing systems. This course provides them with the knowledge to handle streaming data in the cloud. It covers techniques for windowing operations, watermarking, and join operations on streaming data. By mastering these techniques, Cloud Architects can create scalable and reliable data pipelines for real-time data processing in the cloud.

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Business Intelligence Analyst

Business Intelligence Analysts provide valuable insights to businesses by analyzing data. This course equips them with the skills to work with streaming data. It covers windowing operations, watermarking, and join techniques, which are essential for extracting meaningful insights from real-time data streams. By gaining proficiency in these techniques, Business Intelligence Analysts can provide timely and actionable insights to help businesses make informed decisions.

See salaries and explore the career path for Business Intelligence Analyst

Data Warehouse Engineer

Data Warehouse Engineers design and manage data warehouses for storing and analyzing large volumes of data. This course provides them with the skills to handle streaming data in a data warehouse environment. It covers techniques for windowing operations, watermarking, and join operations on streaming data. By mastering these techniques, Data Warehouse Engineers can create scalable and efficient data pipelines for real-time data processing in a data warehouse.

See salaries and explore the career path for Data Warehouse Engineer

Database Administrator

Database Administrators manage and maintain databases. This course provides them with the knowledge to handle streaming data in a database environment. It covers techniques for windowing operations, watermarking, and join operations on streaming data. By mastering these techniques, Database Administrators can create scalable and reliable data pipelines for real-time data processing in a database.

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Systems Administrator

Systems Administrators manage and maintain computer systems. This course provides them with the knowledge to handle streaming data in a system environment. It covers techniques for windowing operations, watermarking, and join operations on streaming data. By mastering these techniques, Systems Administrators can create scalable and reliable data pipelines for real-time data processing in a system.

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Software Developer

Software Developers design, develop, and maintain software applications. This course provides them with the skills to handle streaming data in software applications. It covers techniques for windowing operations, watermarking, and join operations on streaming data. By mastering these techniques, Software Developers can create scalable and reliable data pipelines for real-time data processing in software applications.

See salaries and explore the career path for Software Developer

DevOps Engineer

DevOps Engineers bridge the gap between development and operations teams. This course provides them with the knowledge to handle streaming data in a DevOps environment. It covers techniques for windowing operations, watermarking, and join operations on streaming data. By mastering these techniques, DevOps Engineers can create scalable and reliable data pipelines for real-time data processing in a DevOps environment.

See salaries and explore the career path for DevOps Engineer

Project Manager

Project Managers plan, execute, and deliver projects. This course provides them with the knowledge to manage projects related to streaming data. It covers the principles of windowing operations, watermarking, and join operations on streaming data. By understanding these techniques, Project Managers can effectively manage projects that involve real-time data processing.

See salaries and explore the career path for Project Manager

Business Analyst

Business Analysts analyze business processes and identify opportunities for improvement. This course provides them with the knowledge to analyze streaming data. It covers the concepts of windowing operations, watermarking, and join operations on streaming data. By understanding these techniques, Business Analysts can effectively analyze streaming data to identify trends and opportunities for improvement.

See salaries and explore the career path for Business Analyst

Windowing and Join Operations on Streaming Data with Apache Spark on Databricks

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