Basic understanding of Java or Python programming language
Serverless Data Processing with Dataflow 특화 과정
Building Big Data Applications that Scale
제공자:
배울 내용
Demonstrate how Apache Beam and Cloud Dataflow work together to fulfill your organization’s data processing needs
Write pipelines and advanced components such as utility functions, schemas, and watermarks.
Perform monitoring, troubleshooting, testing and CI/CD on Dataflow pipelines.
Deploy Dataflow pipelines with reliability in mind to maximize stability for your data processing platform
이 전문 분야 정보
최근 조회 2,478회
It is becoming harder and harder to maintain a technology stack that can keep up with the growing demands of a data-driven business. Every Big Data practitioner is familiar with the three V’s of Big Data: volume, velocity, and variety. What if there was a scale-proof technology that was designed to meet these demands?
Enter Google Cloud Dataflow. Google Cloud Dataflow simplifies data processing by unifying batch & stream processing and providing a serverless experience that allows users to focus on analytics, not infrastructure. This specialization is intended for customers & partners that are looking to further their understanding of Dataflow to advance their data processing applications.
This specialization contains three courses:
Foundations, which explains how Apache Beam and Dataflow work together to meet your data processing needs without the risk of vendor lock-in
Develop Pipelines, which covers how you convert our business logic into data processing applications that can run on Dataflow
Operations, which reviews the most important lessons for operating a data application on Dataflow, including monitoring, troubleshooting, testing, and reliability.
응용 학습 프로젝트
This specialization incorporates hands-on labs using Qwiklabs platform. The labs build on the concepts covered in the course modules. Where applicable, we have provided Java and Python versions of the labs. For labs that require adding/updating code, we have provided a recommended solution for your reference.
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인 강좌
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
유연한 일정
유연한 마감을 설정하고 유지 관리합니다.
중급 단계
완료하는 데 약 2개월이 걸립니다
권장 학습 속도: 주 3시간
영어
자막: 영어
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인 강좌
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
유연한 일정
유연한 마감을 설정하고 유지 관리합니다.
중급 단계
Basic understanding of Java or Python programming language
완료하는 데 약 2개월이 걸립니다
권장 학습 속도: 주 3시간
영어
자막: 영어
특화 과정 이용 방법
강좌 수강
Coursera 특화 과정은 한 가지 기술을 완벽하게 습득하는 데 도움이 되는 일련의 강좌입니다. 시작하려면 특화 과정에 직접 등록하거나 강좌를 둘러보고 원하는 강좌를 선택하세요. 특화 과정에 속하는 강좌에 등록하면 해당 특화 과정 전체에 자동으로 등록됩니다. 단 하나의 강좌만 수료할 수도 있으며, 학습을 일시 중지하거나 언제든 구독을 종료할 수 있습니다. 학습자 대시보드를 방문하여 강좌 등록 상태와 진도를 추적해 보세요.
실습 프로젝트
모든 특화 과정에는 실습 프로젝트가 포함되어 있습니다. 특화 과정을 완료하고 수료증을 받으려면 프로젝트를 성공적으로 마쳐야 합니다. 특화 과정에 별도의 실습 프로젝트 강좌가 포함되어 있는 경우, 다른 모든 강좌를 완료해야 프로젝트 강좌를 시작할 수 있습니다.
수료증 취득
모든 강좌를 마치고 실습 프로젝트를 완료하면 취업할 때나 전문가 네트워크에 진입할 때 제시할 수 있는 수료증을 취득할 수 있습니다.
이 전문 분야에는 3개의 강좌가 있습니다.
Serverless Data Processing with Dataflow: Foundations
35개의 평가
This course is part 1 of a 3-course series on Serverless Data Processing with Dataflow. In this first course, we start with a refresher of what Apache Beam is and its relationship with Dataflow. Next, we talk about the Apache Beam vision and the benefits of the Beam Portability framework. The Beam Portability framework achieves the vision that a developer can use their favorite programming language with their preferred execution backend. We then show you how Dataflow allows you to separate compute and storage while saving money, and how identity, access, and management tools interact with your Dataflow pipelines. Lastly, we look at how to implement the right security model for your use case on Dataflow.
Serverless Data Processing with Dataflow: Develop Pipelines
19개의 평가
In this second installment of the Dataflow course series, we are going to be diving deeper on developing pipelines using the Beam SDK. We start with a review of Apache Beam concepts. Next, we discuss processing streaming data using windows, watermarks and triggers. We then cover options for sources and sinks in your pipelines, schemas to express your structured data, and how to do stateful transformations using State and Timer APIs. We move onto reviewing best practices that help maximize your pipeline performance. Towards the end of the course, we introduce SQL and Dataframes to represent your business logic in Beam and how to iteratively develop pipelines using Beam notebooks.
Serverless Data Processing with Dataflow: Operations
In the last installment of the Dataflow course series, we will introduce the components of the Dataflow operational model. We will examine tools and techniques for troubleshooting and optimizing pipeline performance. We will then review testing, deployment, and reliability best practices for Dataflow pipelines. We will conclude with a review of Templates, which makes it easy to scale Dataflow pipelines to organizations with hundreds of users. These lessons will help ensure that your data platform is stable and resilient to unanticipated circumstances.
제공자:
Google 클라우드
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
자주 묻는 질문
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전문 분야를 완료하면 대학 학점을 받을 수 있나요?
전문 분야를 완료하는 데 얼마나 걸리나요?
What background knowledge is necessary?
Do I need to take the courses in a specific order?
궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.
