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Google Cloud Advanced Skills & Certification Workshop: Professional Data Engineer
GOOGLE PROFESSIONAL PROFESSIONAL DATA ENGINEER CLOUD, IOT, artificialintelligence, machinelearning, bigdata, virtualmachines, deeplearning, virtualcontainers, dataengineer
NewCondition € 20000.00
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DESCRIPTION
The workshop is designed to help IT professionals prepare for the Google Certified Professional—Data Engineer Certification Exam.
In this workshop, we review the exam guidelines and product strategies for the major Google Cloud Platform storage, big data, and analytics services covered by the exam. We examine concepts related to data transformation, real-time processing, visualization, and machine learning and best practices to solve common problems.
The workshop assumes prior knowledge of Google Cloud Platform (GCP) and is not an introduction to GCP.
OBJECTIVES
Prepare for the GCP Data Engineer certification exam
Choose the appropriate GCP data storage solution
Store binary, relational, and NoSQL data using GCP services
Secure data using IAM and encryption
Architect batch and streaming data processing pipelines on GCP
Leverage GCP tools for data manipulation, analysis, and visualization
Build machine learning models with GCP tools
The workshop includes instructor lecture, group activities, case study discussions, practice exams and links to recommended study, videos, and tutorials. Homework assignments are also included to help students further prepare for the exam.
WHO SHOULD ATTEND
IT professionals interested in obtaining the Google Certified Professional—Data Engineer certification. Data scientists and machine learning practitioners who want to learn more about taking optimal advantage of the big data services provided by Google Cloud Platform will also benefit from this course.
PREREQUISITES
Prior to taking the Google Cloud Data Engineer Professional exam, students should have prior experience working with Google Cloud Platform big data services. The exam tests one’s understanding of architecting secure and reliable business solutions that leverage Google Cloud Platform for storing, analyzing, and visualizing data. We strongly recommend taking the Data Engineering on Google Cloud Platform course prior to attending this workshop.
Practice Quizzes and Case Study Examples
Included with this course are sample quizzes and numerous case study examples that will help you both prepare for the exam, and have a greater level of understanding of how to build data analytics and machine learning systems on Google Cloud Platform.
TOPICS
Module 1: Data Engineer Certification Overview
Module 2: Google Big Data Fundamentals
Google Big Data History and Overview
Choosing the Right Storage Option
Securing Your Data on Google Cloud Platform
Architecting Data Processing Solutions on GCP
Module 3: Storing Binary Data
Storing Binary Data with Google Cloud Storage
Exercise: Google Cloud Storage
Understanding Persistent Disks Storage
Exercise: Disks and Snapshots
Module 4: Storing Relational Data
Modeling Relational Data
Moving Relational Databases to Cloud SQL
Exercise: Google Cloud SQL Quickstart
Exploiting Spanner for Massively Scalable Relational Systems
Exercise: Google Cloud Spanner Quickstart
Module 5: Managed NoSQL Solutions
Understanding NoSQL Storage
Simplifying Structured Storage with Cloud Firestore and Datastore
Exercise: Google Cloud Datastore/Firestore Quickstart
Storing Massive Data Sets with Bigtable
Choosing between Firestore and Bigtable
Caching Data using Memorystore
Module 6: Big Data Processing and Analytics
Migrating Hadoop and Spark Jobs to Google Cloud Dataproc
Exercise: Creating Dataproc Clusters
Big Data Warehousing and Analytics with BigQuery
Denormalizing Data for Query Optimization in BigQuery
Exercise: Querying Data with BigQuery
Choosing Big Data Processing Strategies
Module 7: Data Processing Pipelines
Programming ETL Pipelines with Google Cloud Dataflow
Simplify Dataflow coding using Templates
Exercise: Google Cloud Dataflow
Designing Real-time Data Processing Systems
Leveraging Pub/Sub for Scalable, Asynchronous Messaging
Preparing Data for Analysis with Cloud DataPrep
Module 8: Visualization and Analytics
Manipulating and Analyzing Data with Cloud Datalab
Building Dashboards with Data Studio
Module 9: Machine Learning Fundamentals
Machine Learning Use Cases and Algorithms
Training and Evaluating Models
Feature Engineering
Analyzing Machine Learning Case Studies
Programming Models with TensorFlow
Exercise: Getting Started with TensorFlow
Serverless, NoOps Training with Google Cloud MLE
Exercise: GCP Machine Learning
Automating machine Learning with AutoML and BigQuery ML
The workshop is designed to help IT professionals prepare for the Google Certified Professional—Data Engineer Certification Exam.
In this workshop, we review the exam guidelines and product strategies for the major Google Cloud Platform storage, big data, and analytics services covered by the exam. We examine concepts related to data transformation, real-time processing, visualization, and machine learning and best practices to solve common problems.
The workshop assumes prior knowledge of Google Cloud Platform (GCP) and is not an introduction to GCP.
OBJECTIVES
Prepare for the GCP Data Engineer certification exam
Choose the appropriate GCP data storage solution
Store binary, relational, and NoSQL data using GCP services
Secure data using IAM and encryption
Architect batch and streaming data processing pipelines on GCP
Leverage GCP tools for data manipulation, analysis, and visualization
Build machine learning models with GCP tools
The workshop includes instructor lecture, group activities, case study discussions, practice exams and links to recommended study, videos, and tutorials. Homework assignments are also included to help students further prepare for the exam.
WHO SHOULD ATTEND
IT professionals interested in obtaining the Google Certified Professional—Data Engineer certification. Data scientists and machine learning practitioners who want to learn more about taking optimal advantage of the big data services provided by Google Cloud Platform will also benefit from this course.
PREREQUISITES
Prior to taking the Google Cloud Data Engineer Professional exam, students should have prior experience working with Google Cloud Platform big data services. The exam tests one’s understanding of architecting secure and reliable business solutions that leverage Google Cloud Platform for storing, analyzing, and visualizing data. We strongly recommend taking the Data Engineering on Google Cloud Platform course prior to attending this workshop.
Practice Quizzes and Case Study Examples
Included with this course are sample quizzes and numerous case study examples that will help you both prepare for the exam, and have a greater level of understanding of how to build data analytics and machine learning systems on Google Cloud Platform.
TOPICS
Module 1: Data Engineer Certification Overview
Module 2: Google Big Data Fundamentals
Google Big Data History and Overview
Choosing the Right Storage Option
Securing Your Data on Google Cloud Platform
Architecting Data Processing Solutions on GCP
Module 3: Storing Binary Data
Storing Binary Data with Google Cloud Storage
Exercise: Google Cloud Storage
Understanding Persistent Disks Storage
Exercise: Disks and Snapshots
Module 4: Storing Relational Data
Modeling Relational Data
Moving Relational Databases to Cloud SQL
Exercise: Google Cloud SQL Quickstart
Exploiting Spanner for Massively Scalable Relational Systems
Exercise: Google Cloud Spanner Quickstart
Module 5: Managed NoSQL Solutions
Understanding NoSQL Storage
Simplifying Structured Storage with Cloud Firestore and Datastore
Exercise: Google Cloud Datastore/Firestore Quickstart
Storing Massive Data Sets with Bigtable
Choosing between Firestore and Bigtable
Caching Data using Memorystore
Module 6: Big Data Processing and Analytics
Migrating Hadoop and Spark Jobs to Google Cloud Dataproc
Exercise: Creating Dataproc Clusters
Big Data Warehousing and Analytics with BigQuery
Denormalizing Data for Query Optimization in BigQuery
Exercise: Querying Data with BigQuery
Choosing Big Data Processing Strategies
Module 7: Data Processing Pipelines
Programming ETL Pipelines with Google Cloud Dataflow
Simplify Dataflow coding using Templates
Exercise: Google Cloud Dataflow
Designing Real-time Data Processing Systems
Leveraging Pub/Sub for Scalable, Asynchronous Messaging
Preparing Data for Analysis with Cloud DataPrep
Module 8: Visualization and Analytics
Manipulating and Analyzing Data with Cloud Datalab
Building Dashboards with Data Studio
Module 9: Machine Learning Fundamentals
Machine Learning Use Cases and Algorithms
Training and Evaluating Models
Feature Engineering
Analyzing Machine Learning Case Studies
Programming Models with TensorFlow
Exercise: Getting Started with TensorFlow
Serverless, NoOps Training with Google Cloud MLE
Exercise: GCP Machine Learning
Automating machine Learning with AutoML and BigQuery ML
