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The Data Engineer certification focuses on designing and managing data infrastructure, including data pipelines, storage solutions, and data processing frameworks. It equips professionals with skills to handle large-scale data systems, optimize data workflows, and ensure efficient data management. Ideal for those looking to advance their expertise in data engineering and analytics.

Instructor

Data Engineer Training | Master Data Pipelines & Analytics

Curriculum

Become a skilled Data Engineer! Learn data pipelines, ETL, Big Data, and cloud technologies. Get certified & boost your career. Enroll in our training today!

Ratings

( 4.2 Ratings )

Live Online Classes starting on 01 January, 1970

COURSE OVERVIEW
Data Engineer

This four-day instructor-led course offers a comprehensive hands-on introduction to designing and building data processing systems on the Google Cloud Platform (GCP). Participants will engage in a combination of presentations, demos, and labs to learn how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning solutions. The course covers handling structured, unstructured, and streaming data effectively.

 

Audience:

This course is ideal for experienced developers responsible for managing big data transformations, including:

  • Extracting, loading, transforming, cleaning, and validating data

  • Designing data processing pipelines and architectures

  • Creating and maintaining machine learning and statistical models

  • Querying datasets, visualizing results, and generating reports

 

Prerequisites:

To maximize learning, participants should have:

  • Completed the Google Cloud Fundamentals: Core Infrastructure (GCPFCI) course or have equivalent experience

  • Basic proficiency in SQL or a similar query language

  • Experience in data modeling and ETL (Extract, Transform, Load) activities

  • Experience developing applications using a common programming language such as Python

  • Familiarity with basic statistics and machine learning concepts

 

Learning Outcomes:

By the end of this course, participants will be able to:

  • Design and build data processing systems on Google Cloud Platform

  • Leverage unstructured data using Spark and ML APIs on Cloud Dataproc

  • Process batch and streaming data with autoscaling pipelines on Cloud Dataflow

  • Derive insights from large datasets using Google BigQuery

  • Train, evaluate, and deploy machine learning models with TensorFlow and Cloud ML

  • Generate instant insights from streaming data

 

Course Outline:

Module 1: Preparing for the Google Cloud Professional Data Engineer
Topics:

  • Designing Data Processing Systems

  • Building and Operationalizing Data Processing Systems

  • Operationalizing Machine Learning Models

  • Security, Policy, and Reliability

Module 2: Google Cloud Big Data and Machine Learning Fundamentals
Topics:

  • Introduction

  • Big Data and Machine Learning on Google Cloud

  • Data Engineering for Streaming Data

  • Big Data with BigQuery

  • Machine Learning Options on Google Cloud

  • The Machine Learning Workflow with Vertex AI

Hands-On:

  • Vertex AI: Qwik Start

  • Exploring a BigQuery Public Dataset

  • Vertex AI: Predicting Loan Risk with AutoML

Module 3: Modernizing Data Lakes and Data Warehouses with Google Cloud
Topics:

  • Introduction

  • Introduction to Data Engineering

  • Building a Data Lake

  • Building a Data Warehouse

Hands-On:

  • BigQuery: Qwik Start - Command Line

  • Creating a Data Warehouse Through Joins and Unions

  • Build and Execute MySQL, PostgreSQL, and SQLServer to Data Catalog Connectors

Module 4: Building Batch Data Pipelines on Google Cloud
Topics:

  • Introduction to Building Batch Data Pipelines

  • Executing Spark on Dataproc

  • Serverless Data Processing with Dataflow

  • Manage Data Pipelines with Cloud Data Fusion and Cloud Composer

Hands-On:

  • Dataflow: Qwik Start - Templates

  • Dataflow: Qwik Start - Python

  • Dataproc: Qwik Start - Console

  • Cloud Composer: Copying BigQuery Tables Across Different Locations

Module 5: Building Resilient Streaming Analytics Systems on Google Cloud
Topics:

  • Introduction to Processing Streaming Data

  • Serverless Messaging with Pub/Sub

  • Dataflow Streaming Features

  • High-Throughput BigQuery and Bigtable Streaming Features

  • Advanced BigQuery Functionality and Performance

Hands-On:

  • Building an IoT Analytics Pipeline on Google Cloud

  • ETL Processing on Google Cloud Using Dataflow and BigQuery

  • Creating Date-Partitioned Tables in BigQuery

  • Troubleshooting and Solving Data Join Pitfalls

  • Working with JSON, Arrays, and Structs in BigQuery

Module 6: Smart Analytics, Machine Learning, and AI on Google Cloud
Topics:

  • Introduction to Analytics and AI

  • Prebuilt ML Model APIs for Unstructured Data

  • Big Data Analytics with Notebooks

  • Production ML Pipelines with Kubeflow

  • Custom Model Building with SQL in BigQuery ML

  • Custom Model Building with AutoML

Hands-On:

  • Dataprep: Qwik Start

  • Creating a Data Transformation Pipeline with Cloud Dataprep

  • Predict Visitor Purchases with a Classification Model in BQML

  • Cloud Natural Language API: Qwik Start

  • Google Cloud Speech API: Qwik Start

  • Video Intelligence: Qwik Start

4.2 Ratings
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