About this course: 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

Who is this class for: This class is intended for data analysts, data scientists and programmers who want to migrate Hadoop and Spark workloads onto Google Cloud Platform and also learn how leverage Google’s infrastructure and machine learning API’s to enhance the power of big data processing applications.

Created by: Google Cloud

Taught by: Google Cloud Training

Basic Info	Course 2 of 5 in the Data Engineering on Google Cloud Platform Specialization.
Level	Intermediate
Commitment	1 week of study, 5-7 hours/week
Language	English
How To Pass	Pass all graded assignments to complete the course.
User Ratings	4.5 stars Average User Rating 4.5See what learners said

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Syllabus

WEEK 1

Module 1: Introduction to Cloud Dataproc

35 videos, 1 reading

Graded: Module 1 Quiz

Module 2: Running Dataproc jobs

32 videos, 2 readings

Graded: Module 2 Quiz

Module 3: Leveraging GCP

26 videos, 1 reading

Graded: Module 3 Quiz

Module 4: Analyzing Unstructured Data

9 videos, 1 reading

Graded: Module 4 Quiz

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