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      Google Cloud Certification Workshop—Data Engineer, Mountain View in Mountain View

      • Google Cloud Certification Workshop—Data Engineer, Mountain View Photo #1
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      January 24, 2019

      Thursday   9:00 AM

      883 North Shoreline Boulevard
      Mountain View, California 94043

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      Google Cloud Certification Workshop—Data Engineer, Mountain View

      Course 796:Google Cloud Certification Workshop—Data Engineer (2 days)   Course Description This course is designed to help IT professionals prepare for the Google Certified Professional—Data Engineer Certification Exam. In this 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. Learning Objectives 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 Audience 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. Exam Prep 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. Course Outline   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 Understanding Persistent Disks Storage Module 4: Storing Relational Data   Modeling Relational Data Moving Relational Databases to Cloud SQL Exploiting Spanner for Massively Scalable Relational Systems Module 5: Managed NoSQL Solutions Understanding NoSQL Storage Simplifying Structured Storage with Cloud Datastore 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 Big Data Warehousing and Analytics with BigQuery Denormalizing Data for Query Optimization in BigQuery Choosing Big Data Processing Strategies Module 7: Data Processing Pipelines Programming ETL Pipelines with 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 Module 10: Machine Learning on Google Cloud Platform   Programming Models with TensorFlow Serverless, NoOps Training with Google Cloud MLE  Module 11: Case Studies

      Categories: Science

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