Google Cloud Certification Workshop: Data Engineer (GCCW-DE)

Descripcion de Curso Calendario
 

Resumen del Curso

This workshop is designed to help IT professionals prepare for the Google Certified Professional—Data Engineer Certification Exam.

In this training course, 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.

This course assumes prior knowledge of Google Cloud Platform (GCP) and is not an introduction to GCP.

Quién debería asistir

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.

Prerrequisitos

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 (DEGCP) course prior to attending this workshop.

Objetivos del curso

  • Prepare for the GCP Data Engineer certification exam
  • Choose the appropriate GCP data storage solution
  • 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
  • Analyze case studies to optimize data storage and processing solutions

Contenido del curso

This 2-day 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.

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.

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 Datastore
  • Exercise: Google Cloud Datastore Quickstart
  • Storing Massive Data Sets with BigTable
  • Choosing between Datastore and BigTable
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
  • 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
Module 10: Case Studies

* Exercises are given as homework assignments when running the 2-day, standard delivery of this course.

Classroom training
Modality: C

Duración 2 días

Fechas y Registro
Entrenamiento en línea
Modality: L
Fechas y Registro
 
Calendario

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