Course Overview

This Google Professional Data Engineer training course from CloudThat teaches candidates how to design, build and operate powerful machine learning and big data solutions using the Google Cloud Platform. The candidate also learns how to deploy, leverage, and train pre-existing machine learning models. A Google Professional Data Engineer is responsible for making data-driven decisions by collecting, publishing, and transforming data.

Beginning with how to ingest data, create data processing pipelines in Cloud Dataflow, deploy relational databases, design highly performant Bigtable, BigQuery, and Cloud Spanner databases, query Firestore databases, and create a Spark and Hadoop cluster using Cloud Dataproc, this course will fully prepare you to be a Google certified data engineer.​

After completing this course, students will be able to:

  • Design a data processing system
  • Build and maintain data structures and databases
  • Analyze data and enable machine learning​
  • Optimize data representations, data infrastructure performance, and cost
  • Ensure reliability of data processing infrastructure​
  • Visualize data
  • Design secure data processing systems​ ​

Upcoming Batches

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Key Features

  • Our Google Cloud Professional Data Engineer training modules have 50% - 60% hands-on lab sessions to encourage Thinking-Based Learning (TBL).
  • Interactive-rich virtual and face-to-face classroom teaching to inculcate Problem-Based Learning (PBL).
  • Industry certified instructor-led training and mentoring sessions to develop Competency-Based Learning (CBL).
  • Well-structured use-cases to simulate challenges encountered in a Real-World environment.
  • Integrated teaching assistance and support through experts designed Learning Management System (LMS) and ExamReady platform.
  • Being an authorized GCP Learning Partner gives us the edge over competition.

Who Should Attend

  • This Google Data Engineer certification course is for Data Scientists, DevOps Engineers, Solution Architects, and anyone willing to learn about data engineering and machine learning in the context of the Google Cloud Platform.

Prerequisites

  • There is no prerequisite for attending this GCP Data Engineer training course.
  • Course Outline Download Course Outline

    Theory

    • Introduction to Object Storage​
    • Options for loading data
    • Access Controls for Cloud Storage​
    • Lifecycle Policy Management​
    • Using Cloud Storage Console​

    Hands-On Labs

    • Create and Interact with first GCS bucket using the UI and Command Line tool​

    Theory

    • Introduction to Relational Databases​
    • When to use Cloud SQL, creating and monitoring a Cloud SQL Database​
    • When to use Cloud Spanner, creating a cloud spanner database and its performance considerations​

    Hands-On Labs

    • Creating and configuring your own Cloud SQL database​
    • You are migrating a database to Cloud Spanner. The current model uses auto-increment values as a primary key. You need to keep those values but don’t want to use them as a primary key. How could you create a primary key that does not use an auto-increment value?​

    Theory

    • Introduction to Cloud Firestore & Document Databases
    • Creating and querying Entities, Kinds, Namespaces, working with transactions​
    • Introduction to Bigtable and Wide-Column Databases​
    • Creating Bigtable instance, querying patterns​

    Hands-On Labs

    • Creating and configuring Kinds and Entities​
    • Design a time series database for Cloud Bigtable

    Theory

    • Introduction to BigQuery and Analytical Databases​
    • BigQuery Scalar Datatypes​
    • BigQuery Nested and Repeated Fields​
    • Access Controls, Partitioning tables and loading data into BigQuery​

    Hands-On Labs

    • Designing, creating and querying BigQuery Public Datasets

    Theory

    • Introduction to Assessing the Current State of a Data Warehouse​
    • Schema and Data Transfer, Data Pipelines
    • Reporting and Analysis, Data Governance​
    • Using Caching to Improve Performance​
    • Cloud Memorystore Data Structures​

    Hands-On Labs

    • Understanding different kinds of data warehouse processes​
    • Redesign a distributed application to take advantage of a cache​

    Theory

    • Introduction to Cloud Pub/Sub​
    • Creating and reading Topics and Subscriptions, Messages​
    • Stream and Batch Processing with Cloud Dataflow​
    • Running and monitoring a Job in Cloud Dataflow and analysing a failed job​
    • Creating and monitoring a Cloud Dataproc Cluster​

    Hands-On Labs

    • Create a Topic, Publish Messages, Read Messages​
    • Determine possible causes for a Cloud Dataflow Pipeline error​

    Theory

    • Monitoring and Alerting with Cloud Monitoring​
    • Logging with Cloud Logging​
    • Creating an Alert​
    • Install the Monitoring Agent on a Virtual Machine​ ​

    Hands-On Labs

    • Exposure to various tools on monitoring and logging

    Theory

    • Introduction to Identity Access Management​
    • Resource Hierarchy in an organization​
    • Predefined Roles, Custom Roles, Primitive Roles
    • IAM Best Practices​
    • Ensuring Privacy with Data Loss Prevention API and Legal Compliance​
    • Encryption At Rest and In Motion​
    • Key Management​

    Hands-On Labs

    • Grant and revoke roles to change access. Use Cloud IAM to implement access control, restrict access to specific features and resources, and use the service account user role. ​

    Theory

    • Categories of Machine Learning Problems​
    • Approaches to Building ML Models​
    • Symbolic Machine Learning
    • Feature Engineering

    Hands-On Labs

    • Feature Engineering using BigQuery datasets​

    Theory

    • Model Building and Evaluation​
    • Building, deploying and monitoring models in GCP​
    • Using Pre-built ML Models​

    Hands-On Labs

    • Creating and evaluating a regression model in BigQuery​

    Certification

    • By earning GCP Data Engineer certification, you can be competent Google certified professional.
    • Demonstrate skills and knowledge to build, monitor, and tune high performance data engineering systems.
    • On successful completion of Google Data Engineer course, aspirants receive a Course Completion Certificate from us.
    • By successfully clearing the Google Data Engineer exam, aspirants earn Google Certification.

    Our Top Trainers

    Srivani Bhompally

    Srivani works as Research Associate in CloudThat technologies and delivers training GCP, Docker, Kubernetes and Terraform. She has certification on GCP and works on various Devops and Cloud consulting projects for various clients

    Vivek Kumar

    Vivek has been involved in various large and complex projects with global clients. He has experience in AWS, GCP and Azure Cloud Platforms. He has experience in various software development fields like Image Processing, Web designing, Networking etc.

    Lakhan Kriplani

    He had involved in various client projects to set up infrastructure on Cloud for various Analytics applications, E-Commerce, setup CICD Pipeline using AWS services. He has experience in developing highly secure, scalable web applications using MVC architecture. etc.

    Ajay Kumar Lodha

    Ajay is cloud obsessed and cloud addict, that's how he describes himself. Ajay has been working with all the major cloud computing platforms like AWS, Azure, and GCP for more than 5 years now. He is into etc.

    Course Fee

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      Course ID: 11542

      Course Price at

      ₹ 39900 + 18% GST

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