课程信息
4.5
1,583 个评分
209 个审阅
This 1-week, accelerated course builds upon previous courses in the Data Engineering on Google Cloud Platform specialization. Through a combination of video lectures, demonstrations, and hands-on labs, you'll learn how to create and manage computing clusters to run Hadoop, Spark, Pig and/or Hive jobs on Google Cloud Platform. You will also learn how to access various cloud storage options from their compute clusters and integrate Google’s machine learning capabilities into their analytics programs. In the hands-on labs, you will create and manage Dataproc Clusters using the Web Console and the CLI, and use cluster to run Spark and Pig jobs. You will then create iPython notebooks that integrate with BigQuery and storage and utilize Spark. Finally, you integrate the machine learning APIs into your data analysis. Pre-requisites • Google Cloud Platform Big Data & Machine Learning Fundamentals (or equivalent experience) • Some knowledge of Python...
Globe

100% 在线课程

立即开始,按照自己的计划学习。
Calendar

可灵活调整截止日期

根据您的日程表重置截止日期。
Intermediate Level

中级

Clock

Approx. 6 hours to complete

建议:1 week of study, 5-7 hours/week...
Comment Dots

English

字幕:English...

您将获得的技能

Google Cloud DataprocApplication Programming Interfaces (API)Machine Learning
Globe

100% 在线课程

立即开始,按照自己的计划学习。
Calendar

可灵活调整截止日期

根据您的日程表重置截止日期。
Intermediate Level

中级

Clock

Approx. 6 hours to complete

建议:1 week of study, 5-7 hours/week...
Comment Dots

English

字幕:English...

教学大纲 - 您将从这门课程中学到什么

Week
1
Clock
完成时间为 2 小时

Module 1: Introduction to Cloud Dataproc

...
Reading
16 个视频(共 52 分钟), 2 个测验
Video16 个视频
Introducing Cloud Dataproc1分钟
Defining unstructured data?4分钟
Deriving value from unstructured data7分钟
Approaches to working with Big Data4分钟
MapReduce and Hadoop origins5分钟
On prem Hadoop has a lot of overhead1分钟
Cloud Dataproc versus Hadoop alternatives2分钟
Creating a Dataproc cluster4分钟
Dataproc customization3分钟
Dataproc and the CLI分钟
Lab 1: Overview分钟
Lab 1: Demo and Review7分钟
Custom Machine Types3分钟
Preemptible VMs3分钟
Wrap up分钟
Quiz1 个练习
Module 1 Quiz4分钟
Clock
完成时间为 3 小时

Module 2: Running Dataproc jobs

...
Reading
13 个视频(共 51 分钟), 3 个测验
Video13 个视频
Methods for submitting jobs1分钟
Lab 2 Overview1分钟
Lab 2: Demo and Review11分钟
Separation of Storage and Compute6分钟
Evolution of data processing5分钟
The importance of networking in data processing3分钟
Separating storage and compute with Spark1分钟
Submitting Spark jobs3分钟
Overview of Spark concepts2分钟
Lab Overview分钟
Lab 3: Demo and Review8分钟
Module Wrap Up分钟
Quiz1 个练习
Module 2 Quiz2分钟
Clock
完成时间为 3 小时

Module 3: Leveraging GCP

...
Reading
10 个视频(共 37 分钟), 3 个测验
Video10 个视频
BigQuery Support8分钟
Lab 4: Overview分钟
Lab 4: Demo and Review4分钟
Cluster customization4分钟
Installing software on a Dataproc7分钟
Lab 5: Overview分钟
Lab 5: Demo and Review8分钟
Wrap up分钟
Review分钟
Quiz1 个练习
Module 3 Quiz2分钟
Clock
完成时间为 1 小时

Module 4: Analyzing Unstructured Data

...
Reading
7 个视频(共 24 分钟), 2 个测验
Video7 个视频
A closer look at Machine Learning3分钟
Examples of applied ML3分钟
Natural Language Processing close-up2分钟
Lab 6: Overview1分钟
Lab 6: Demo and Review10分钟
Wrap up分钟
Quiz1 个练习
Module 4 Quiz2分钟
4.5
Direction Signs

60%

完成这些课程后已开始新的职业生涯
Briefcase

83%

通过此课程获得实实在在的工作福利
Money

25%

加薪或升职

热门审阅

创建者 CPDec 29th 2017

Really enjoyed it, woudl have liked to spend more time with the APIs and integrate with real time web downloads. There are a few bugs and misprints, but wasn't too hard to find them.

创建者 PGAug 8th 2018

The course was really helpful to understand how to use google bigdata offering - dataproc for creating and managing Hadoop/hive/spark/pig and many more opensource bigdata products.

关于 Google Cloud

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....

关于 Data Engineering on Google Cloud Platform 专项课程

>>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<< This five-week, accelerated online specialization provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning. The course covers structured, unstructured, and streaming data. This course teaches the following skills: • Design and build data processing systems on Google Cloud Platform • Leverage unstructured data using Spark and ML APIs on Cloud Dataproc • Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow • Derive business insights from extremely large datasets using Google BigQuery • Train, evaluate and predict using machine learning models using Tensorflow and Cloud ML • Enable instant insights from streaming data This class is intended for developers who are responsible for: • Extracting, Loading, Transforming, cleaning, and validating data • Designing pipelines and architectures for data processing • Creating and maintaining machine learning and statistical models • Querying datasets, visualizing query results and creating reports...
Data Engineering on Google Cloud Platform

常见问题

  • Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

  • If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

  • Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.

  • If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

  • This course is one of a few offered on Coursera that are currently available only to learners who have paid or received financial aid. If you’d like to take this course, but can’t afford the course fee, we encourage you to submit a financial aid application.

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