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Google

Professional Cloud Data Engineer

Casey Ayers, Janani Ravi, and Vitthal Srinivasan

This skill path covers all the objectives needed to be a Data Engineer on Google Cloud. You will learn in depth how to use the products and services as well as how to complete the most common tasks with your Data in GCP. Although this path aligns with the topics for the GCP Cloud Data Engineer Professional exam. It is not a cert exam prep course. This is a path to teach the practical on the job skills for Data on GCP.

What You'll Learn

Dataproc

Dataflow and Apache Bean

GCP Pub/Sub

BigQuery

GCP Cloud SQL

GCP Cloud Spanner

Cloud Datastore

Frestore

BigTable

Datalab

ML Engine

Machine Learning APIs

Data Architecture on GCP

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What's inside

14 courses

Architecting Big Data Solutions Using Google Dataproc

(2 hours)

Dataproc is Google’s managed Hadoop offering on the cloud. It teaches how the separation of storage and compute allows you to utilize clusters more efficiently purely for processing data and not for storage.

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Architecting Serverless Big Data Solutions Using Google Dataflow

(2 hours)

Dataflow represents a fundamentally different approach to Big Data processing than computing engines such as Spark. Dataflow is serverless and fully-managed, meaning that provisioning resources and scaling can be transparent to the data architect.

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Architecting Data Warehousing Solutions Using Google BigQuery

(2 hours)

BigQuery is the Google Cloud Platform’s data warehouse on the cloud. In this course, you’ll learn how to work with BigQuery on huge datasets with little to no administrative overhead.

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Building Machine Learning Models in SQL Using BigQuery ML

(1 hours)

BigQuery ML on the Google Cloud Platform democratizes machine learning by allowing data analysts and engineers to build and use machine learning models directly from SQL. This course demonstrates how to build and train machine learning models for linear and logistic regression using SQL commands on BigQuery, the Google Cloud Platform’s serverless data warehouse.

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Architecting Big Data Solutions Using Google Bigtable

(2 hours)

Google Bigtable, a NoSQL offering on Google Cloud Platform, provides extremely low latencies. This course covers the conceptual and practical aspects of working with Bigtable, including schema design, data access, and failover handling.

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Designing and Implementing Solutions Using Google Machine Learning APIs

(1 hours)

Two things are true in project leadership: no plan is ever perfect, and all projects come to an end. In this course, you’ll learn how to guide your team through inevitable change, maximize the value your team offers, and close out work effectively.

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Designing and Implementing Solutions Using Google Cloud AutoML

(1 hours)

Google Cloud AI offers a wide range of machine learning services. AutoML features cutting-edge technology which uses your training data to find the best model for your use case. In this course, you'll learn to build a custom machine learning model.

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Building Pipelines for Workflow Orchestration Using Google Composer

(1 hours)

Cloud Composer is a fully managed workflow orchestration service that allows creation, scheduling, and monitoring of pipelines. This course covers the architecture of Composer and creation of pipelines to run arbitrary shell scripts and Python code.

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Creating and Administering Google Cloud Spanner Instances

(1 hours)

This course covers practical aspects of working with Cloud Spanner, Google’s proprietary relational database management system (RDBMS) service. It is the only globally distributed, strongly consistent RDBMS with horizontal scaling.

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Architecting Schemaless Scalable NoSQL Databases Using Google Datastore

(1 hours)

This course covers Datastore, a schemaless, serverless NoSQL service that fills a specific niche on the GCP. Datastore offers fast lookups virtually independent of the dataset size and is optimized for hierarchical queries on document data.

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Leveraging Fully Managed Redis Datastores Using Google Cloud Memorystore

(1 hours)

Cloud Memorystore is a fully managed, cloud-hosted Redis service. It provides low latency and high performance by caching common responses on GCP-hosted web applications.

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Leveraging Google Cloud Firestore for Realtime Database Solutions

(2 hours)

Cloud Firestore is a cloud-hosted NoSQL, document-oriented database that allows realtime updates to keep data in sync across multiple clients. It is a flexible, scalable, and performant database that is often used for mobile and web applications where there are multiple clients who need to be kept in sync.

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Architecting Scalable Web Applications with Firebase on the Google Cloud Platform

(1 hours)

Firebase is Google's comprehensive mobile and app development platform that has features and integrations with the Google Cloud Platform, allowing developers to build applications quickly without managing infrastructure.

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Developing on the Google Cloud Using Datalab and Cloud Source Repositories

(1 hours)

In this course, you'll gain an understanding of how to begin consolidating your code, compute, and data in a Google Cloud Platform environment using developer tools combined with GCP services.

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Time

1640 hours

Via

Pluralsight

Institution

Instructors

Casey Ayers

Janani Ravi

Vitthal Srinivasan

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