Apache Flink with Scala 3 from Udemy

As the latest Flink course in Scala 3 released in 2024, it covers Flink's basic and advanced concepts each followed by hands-on exercises. This course cuts through all the complexities of integrating the very recent Flink version 1.17 with Scala 3 and Kubernetes. Starting with version 1.15 of Flink, Scala support was removed to make it easier to integrate with multiple versions of Scala, including Scala 3. This course includes step by step video demonstrations on how to resolve the problem of classpath collision when deploying Flink applications in Scala 3 on Kubernetes.

Concretely, here's what you will learn to either ace your next Flink interview or be ready to write your production level Flink application:

1. Flink and Streaming Fundamentals

You'll understand how Flink handles stream processing, distributed and stateful computation; You'll learn Flink's architecture including Flink cluster's components; You'll know how to deploy and manage the lifecycle of a Flink application.

2. Flink Data Pipeline

You'll understand different levels of abstraction for developing streaming applications; You'll be able to process big data in real time any way you want to by mastering fundamental Flink concepts including: data ingestion, efficient data transformation, controlling your applications with lower level APIs, producing output streams to data sinks.

3. Integration with Apache Kafka

You'll learn configuration of Kafka Source and Kafka Sink; You'll master how to set up Kafka dependencies in built.sbt and how to integrate Kafka with Flink as a data source or data sink.

4. Time Handling, Watermarks and Windows

You'll be able to handle event time processing using Flink's watermarks mechanism and window operation including tumbling window, sliding window and global window.

5. Fault Tolerant

You'll be able to write stateful applications using Flink's key concepts including checkpoint mechanism, map state, list state and value state.

6. Integration with Kubernetes

You'll learn Flink's deployment modes and deploy your own Flink application on Kubernetes by following along the video demonstration of every deployment step and deployment configurations.

What you'll get from this course:

You will get 30+ total Flink video lessons with slides and illustrative diagrams plus access to Github Repo with all the code in the course;

You can practice by writing more than 20 Flink applications for common use cases and following along the hands-on video lessons;

You will quickly master the configurations of all the dependencies and steps to deploy Flink 1.17 applications on Kubernetes by following the video demonstration;

You will learn transferrable principles of big data streaming and distributed systems that you can apply on other streaming systems.

What's inside

Syllabus

Introduction

Prerequisites

Why Flink

Flink Cluster and Life Cycle

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Addresses the complexities of integrating Flink 1.17 with Scala 3 and Kubernetes, which is a common challenge for developers in the field

Covers Flink's architecture, including cluster components, and how to deploy and manage the lifecycle of a Flink application, which are essential skills

Explores different levels of abstraction for developing streaming applications, enabling real-time big data processing with fundamental Flink concepts

Includes step-by-step video demonstrations on resolving classpath collision issues when deploying Flink applications in Scala 3 on Kubernetes

Requires learners to set up Kafka dependencies in built.sbt, which may require familiarity with Scala build tools and dependency management

Focuses on Flink version 1.17, so learners should be aware that features and configurations may differ in other versions of Flink

Reviews summary

Apache flink with scala 3 and kubernetes

According to learners, this course provides a solid foundation in Apache Flink using Scala 3, particularly valued for its practical, hands-on approach and focus on real-world deployment scenarios. Many highlight the detailed steps for integrating Flink with Scala 3 and the comprehensive coverage of Kubernetes deployment as particularly useful. While the course content is generally well-received and covers core Flink concepts effectively, some students note that setting up the environment can be challenging and the pace might be fast for beginners without prior experience. Overall, it is seen as a valuable resource, especially for those looking to deploy Flink applications in a modern environment.

Moves quickly, assumes prior knowledge.

"The course moves quite fast; recommend having some prior experience with Scala and possibly Flink or streaming concepts."

"If you're entirely new to Flink or Scala, you might find the pace challenging. Some background helps immensely."

"Assumes familiarity with big data concepts, Scala, and potentially Docker/Kubernetes."

Addresses Flink-Scala 3 compatibility.

"This course unique focuses on integrating Flink with the very recent Scala 3, which is hard to find elsewhere."

"Solving the classpath collision problem when deploying Flink apps in Scala 3 on Kubernetes was exactly what I needed."

"The specific attention paid to the Flink 1.17 and Scala 3 integration details was very helpful."

Step-by-step guide to deploying Flink.

"The section on deploying Flink applications on Kubernetes was incredibly useful and covered the steps needed for production."

"Mastering the configurations and steps to deploy Flink 1.17 apps on Kubernetes following the video demo was a major plus."

"Seeing the Flink deployment process on K8s demonstrated visually made it much easier to grasp."

Hands-on coding activities are key.

"The hands-on coding and projects are the strongest part of the course for me, helping solidify theoretical concepts."

"I really appreciated the practical exercises for each topic; they made learning Flink much more concrete."

"Applying Flink concepts through the provided code examples was very helpful in understanding how things work in practice."

Environment setup can be difficult.

"Getting the local environment set up correctly according to the course requirements was a bit frustrating."

"I spent a significant amount of time resolving dependency conflicts and setup issues before I could even start coding."

"Some initial hurdles with the development environment setup."

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 Apache Flink with Scala 3 with these activities:

Review Scala 3 Fundamentals

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Solidify your understanding of Scala 3 syntax and core concepts before diving into Flink. This will make it easier to grasp the Flink-specific code examples and exercises.

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Review Scala 3 documentation and tutorials.
Practice writing basic Scala 3 programs.
Familiarize yourself with Scala 3's collection API.

Review 'Programming in Scala'

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Strengthen your Scala foundation with a comprehensive guide. This will help you better understand the Flink code examples and write your own Flink applications in Scala 3.

View Programming in Scala on Amazon

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Read the chapters relevant to functional programming and collections.
Work through the examples and exercises in the book.
Relate the concepts to the Flink course syllabus.

Kafka Setup and Configuration Practice

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Practice setting up and configuring Kafka to reinforce your understanding of the Kafka Source and Sink integrations covered in the course. This will help you troubleshoot issues and build robust Flink applications.

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Install and configure a local Kafka cluster.
Create Kafka topics and produce/consume messages.
Experiment with different Kafka configurations.

Four other activities

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Show all seven activities

Blog Post: Flink Windowing Strategies

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Write a blog post explaining different Flink windowing strategies (tumbling, sliding, global) with code examples. This will help you solidify your understanding of windowing and share your knowledge with others.

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Research different Flink windowing strategies.
Write clear and concise explanations of each strategy.
Include code examples to illustrate each strategy.

Real-time Data Pipeline with Flink and Kafka

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Build a complete real-time data pipeline using Flink and Kafka to solidify your understanding of the course material. This project will allow you to apply the concepts you've learned to a practical problem.

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Design a data pipeline for a specific use case.
Implement the pipeline using Flink and Kafka.
Test and deploy the pipeline.

Review 'Flink in Action'

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Deepen your understanding of Flink with a practical guide to building real-world applications. This book will provide you with valuable insights and techniques for building production-ready Flink pipelines.

View Putting Knowledge to Work on Amazon

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Read the chapters relevant to your project or areas of interest.
Experiment with the code examples in the book.
Apply the concepts to your own Flink applications.

Contribute to a Flink Open Source Project

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Contribute to an open-source Flink project to gain hands-on experience and learn from experienced developers. This will help you deepen your understanding of Flink and contribute to the Flink community.

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Identify a Flink open-source project that interests you.
Find a bug or feature to work on.
Submit a pull request with your changes.

Career center

Learners who complete Apache Flink with Scala 3 will develop knowledge and skills that may be useful to these careers:

Stream Processing Engineer

A stream processing engineer specializes in building and maintaining systems that process data in real time. This Apache Flink with Scala 3 course is practically tailored for this person. The course's primary focus is on stream processing using Flink. The core components of the curriculum, including Flink's architecture, data pipeline construction, and integration with Kafka, are necessary skills for a stream processing engineer. The modules on time handling, watermarks, windows, and fault tolerance directly address the challenges of real-time data processing. Hands-on exercises with different window types and stateful streaming further enhance the engineer's ability to build robust and efficient stream processing applications. The deployment lessons on Kubernetes are invaluable, as they mirror the production environment.

See salaries and explore the career path for Stream Processing Engineer

Data Engineer

A data engineer designs, builds, and manages the infrastructure for data generation, storage, and processing. This course on Apache Flink with Scala 3 directly aligns with the responsibilities of a data engineer working with real-time data streams. The section on Flink fundamentals, data pipelines, and integration with Apache Kafka provides a strong foundation for handling data ingestion, transformation, and output. Furthermore, the practical exercises on deploying Flink applications on Kubernetes directly translate to real-world deployment scenarios encountered by data engineers. The course's focus on resolving classpath collisions in Scala 3 within Kubernetes environments is specifically relevant, as it equips data engineers with the skills to navigate deployment challenges.

See salaries and explore the career path for Data Engineer

Big Data Developer

A big data developer builds and maintains scalable data processing systems capable of handling large volumes of data. This course provides training in Apache Flink with Scala 3 equipping learners with the skills to excel. The course content provides a solid foundation in stream processing and distributed systems, which are essential for a big data developer. Lessons on Flink data pipelines, Kafka integration, and fault tolerance are crucial for handling data ingestion, transformation, and ensuring reliability. The hands-on exercises and code examples enable learners to apply these concepts in practical scenarios. The coverage of Flink deployment on Kubernetes is also beneficial, as it prepares developers for deploying big data applications in production environments.

See salaries and explore the career path for Big Data Developer

Software Engineer

A software engineer designs, develops, and tests software applications and systems. This course on Apache Flink with Scala 3 provides specialized knowledge that can enhance a software engineer's capabilities, especially when working with data-intensive applications. The course equips software engineers with skills in stream processing, distributed systems, and real-time data handling. Software engineers who want to expand their expertise into big data processing will find lessons on Flink's architecture, data pipelines, Kafka integration, and deployment on Kubernetes particularly interesting. The ability to write stateful applications using Flink's state management features further enriches their skill set.

See salaries and explore the career path for Software Engineer

Machine Learning Engineer

Machine learning engineers design, build, and deploy machine learning models. Real-time data is essential for many machine learning applications, and this course on Apache Flink with Scala 3 provides the skills needed to process and prepare data streams for machine learning models. You will see that the course will help you understand you how to ingest data from sources like Kafka, transform it using Flink's data pipeline capabilities, and feed it into machine learning algorithms. The lessons on time handling, watermarks, and windows are particularly relevant for processing time-series data and creating features for machine learning models. The deployment strategies on Kubernetes are valuable for deploying machine learning pipelines in production environments.

See salaries and explore the career path for Machine Learning Engineer

Data Architect

A data architect designs and oversees the implementation of data management systems within an organization. While a data architect may not directly write code, a solid understanding of data processing technologies is essential for making informed design decisions, in particular, Apache Flink with Scala 3. This course provides valuable insights into the capabilities and complexities of Flink, a popular stream processing framework. The lessons on Flink's architecture, data pipelines, fault tolerance, and deployment on Kubernetes enable a data architect to understand the implications of choosing Flink for real-time data processing. The knowledge gained from this course can inform decisions about system design, scalability, and maintainability.

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Backend Developer

A backend developer builds and maintains the server-side logic and databases that power applications. The Apache Flink with Scala 3 course can be beneficial for backend developers working with data-intensive applications that require real-time processing, transformation, and fault tolerance. The backend developer can use the course to learn about Flink's architecture, data pipelines, and integration with other systems such as Apache Kafka. The course's content on state management and fault tolerance can be helpful for building reliable and scalable backend systems. The deployment strategies on Kubernetes are also valuable for deploying backend applications in production environments.

See salaries and explore the career path for Backend Developer

Solutions Architect

A solutions architect designs and implements complex IT solutions to meet business needs. A solutions architect benefits from understanding various technologies and how they can be integrated to solve specific problems, including Apache Flink with Scala 3. This course offers a deep dive into Flink's capabilities for stream processing, data transformation, and integration with other systems such as Apache Kafka and Kubernetes. The course is useful in understanding how Flink can be incorporated into a larger data processing architecture. The deployment strategies and configuration details covered in the course help the solutions architect make informed decisions about infrastructure and deployment options.

See salaries and explore the career path for Solutions Architect

DevOps Engineer

A DevOps engineer is responsible for automating and streamlining the software development and deployment process. This course on Apache Flink with Scala 3 can provide valuable knowledge for DevOps engineers, as it covers the deployment of Flink applications on Kubernetes. This person will learn about the configurations, dependencies, and steps required to deploy Flink applications in a containerized environment. The course may be useful in understanding how to automate the deployment and scaling of Flink applications using Kubernetes. The focus on resolving classpath collisions in Scala 3 within Kubernetes environments is also highly relevant for DevOps engineers who manage complex application deployments.

See salaries and explore the career path for DevOps Engineer

Technical Lead

A technical lead is responsible for guiding a team of developers and making technical decisions on projects. Gaining a solid understanding of stream processing technologies like Apache Flink with Scala 3 is helpful. This course offers a deep dive into Flink's capabilities. It also gives you insight into Flink and Kubernetes integration. This technical lead gains valuable insights into the capabilities and complexities of Flink, so they can make informed decisions about technology selection, system design, and resource allocation. It can also assist the technical lead when discussing deployment to Kubernetes and other containerized environments. A technical lead may find this course helpful.

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Analytics Engineer

An analytics engineer transforms raw data into a format suitable for analysis and reporting. This course on Apache Flink with Scala 3 provides skills for processing and transforming real-time data streams. As an Analytics Engineer, you will appreciate that you can use the knowledge from the course to ingest data from various sources, clean and transform it using Flink's data pipeline capabilities, and load it into data warehouses or data lakes for analysis. This course's sections on data transformation, time handling, and state management will be particularly relevant for preparing data for analytical purposes. The course may be useful in understanding how to process and aggregate data in real time for dashboards and reports.

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Cloud Engineer

A cloud engineer manages and maintains cloud infrastructure and services. This course on Apache Flink with Scala 3 provides skills that are directly applicable to deploying and managing data processing applications in the cloud. The cloud engineer can use the Apache Flink with Scala 3 course to learn about the different deployment modes of Flink and how to deploy Flink applications on Kubernetes, a popular container orchestration platform in the cloud. You can also benefit from the practical video demonstrations that guide the cloud engineer through the deployment process. It may be helpful to understand how to configure, scale, and monitor Flink applications in a cloud environment.

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Data Science Consultant

Data science consultants advise organizations on how to leverage data to solve business problems; a knowledge of Apache Flink with Scala 3 can be beneficial. This course provides a foundation in stream processing; the data science consultant can assist clients who need to process data in motion. A data science consultant can leverage the integration lessons with Apache Kafka and Kubernetes as part of the responsibilities. The consultant may find the course may be useful for understanding the capabilities of Flink and recommending it as a solution for real-time analytics.

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Data Analyst

A data analyst examines data to identify trends and insights that can inform business decisions. While this is not a primary skill for data analysts, a basic understanding of Apache Flink with Scala 3, particularly stream processing, can be valuable. The course provides a foundation in how real-time data is processed and transformed, giving the data analyst context for the data they are analyzing. The data analyst will appreciate the basic knowledge of how event time processing with watermarks, windowing, and fault tolerance work. This could be useful for understanding the origins of a data set.

See salaries and explore the career path for Data Analyst

Business Intelligence Analyst

A business intelligence analyst gathers and analyzes data to identify trends and insights that can improve business performance. While not directly related, a basic understanding of Apache Flink with Scala 3 can provide valuable context for the data they are working with. The course provides an overview of how real-time data is processed and transformed. The business intelligence analyst may find this useful because they will better understand the source of the final data they are working with. Also, it could be helpful to understand the lessons on integration with Kubernetes.

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Apache Flink with Scala 3

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Apache flink with scala 3 and kubernetes

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