Google Cloud

Google Cloud Data Engineering - Professional Data Engineer Certification

34 hours
2300,00 €
Classroom or Live Virtual Class
Classroom or Live Virtual Class
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CERTIFICACIÓN INCLUIDA

Para miembros del colectivo  PUE Alumni

Description

This course is oriented towards helping students to obtain the needed skills and knowledge to be a professional Google Cloud Data Engineer, a job profile which enables data-based decision-making through the collection, transformation and publication of data. This profile is also capable of designing, building, operating, securing, and monitoring data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; flexibility and portability. Furthermore, it is able to continuously leverage, deploy and implement pre-existing machine learning models.

The first module of this course provides students with an introduction to the design and construction of data processing systems on the Google Cloud Platform. Through the combination of demos and laboratories, students will learn how to design data processing systems, build end-to-end channels, analyse data and perform machine learning. This module also covers structured, unstructured and continuous transfer data.

The second and last module of the course focuses on the preparation of the official Professional Data Engineer certification exam, whose passing verifies that a professional is fully qualified to work as a professional Google Cloud data engineer in a business environment.

The complete preparation offered by this module is achieved by testing useful skills, which include reasoning for exam questions and case understanding, useful tips, reviewing topics in the Infrastructure curriculum and taking a Practice Test, a simulation of the certification exam associated to this course.

PUE is a Google Cloud Official Training Partner authorized by that multinational to provide official training in its technologies.

Audience and prerequisites

This class is intended for experienced developers who are responsible for managing big data transformations including:

  • Extracting, Loading, Transforming, cleaning, and validating data.
  • Designing pipelines and architectures for data processing.
  • Creating and maintaining machine learning and statistical models.
  • Querying datasets, visualizing query results and creating reports.

This training is also aimed at Cloud professionals who intend to take the Professional Data Engineer certification exam. To get the most out of the course the attendants should have:

  • Complete Google Cloud Fundamentals: Big Data & Machine Learning or have equivalent experience.
  • Basic proficiency with common query language such as SQL
  • Experience with data modelling, extract, transform, load activities
  • Developing applications using a common programming language such as Python
  • Familiarity with Machine Learning and/or statistics

Objectives

This course teaches participants the following skills:

  • Design and build data processing systems on Google Cloud Platform.
  • Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow.
  • Derive business insights from extremely large datasets using Google BigQuery.
  • Train, evaluate and predict using machine learning models using Tensorflow and Cloud ML.
  • Leverage unstructured data using Spark and ML APIs on Cloud Dataproc.
  • Enable instant insights from streaming data.
  • Provide information, tips, and advice on taking the exam
  • In-depth review of sample case studies
  • Review each section of the exam covering highest-level concepts sufficient to build confidence in what is known by the candidate
  • Indicate skill gaps/areas of study if not known by the candidate

Certification included

This course is recommended for preparing the following official certification exam valued at 222,45€ (VAT included), the cost of which (one chance) is included in the course price for all members of the PUE Alumni program:

Passing this exam is an essential requirement to obtain the Professional Data Engineer Certification.

PUE is an Official Kryterion Certification Center facilitating the management of the exam for the candidate. The student will be able to carry out their certification, either in our facilities or, if they prefer, through the Kryterion’s online-proctoring (OLP) solution option, which allows them to attend the official Google Cloud certification from any location with just an internet connection.

Topics

Module 1: Data Engineering on Google Cloud Platform

Introduction to Data Engineering

  • Explore the role of a data engineer.
  • Analyze data engineering challenges.
  • Intro to BigQuery.
  • Data Lakes and Data Warehouses.
  • Demo: Federated Queries with BigQuery.
  • Transactional Databases vs Data Warehouses.
  • Website Demo: Finding PII in your dataset with DLP API.
  • Partner effectively with other data teams.
  • Manage data access and governance.
  • Build production-ready pipelines.
  • Review GCP customer case study.
  • Lab: Analyzing Data with BigQuery.

Building a Data Lake

  • Introduction to Data Lakes.
  • Data Storage and ETL options on GCP.
  • Building a Data Lake using Cloud Storage.
  • Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions.
  • Securing Cloud Storage.
  • Storing All Sorts of Data Types.
  • Video Demo: Running federated queries on Parquet and ORC files in BigQuery.
  • Cloud SQL as a relational Data Lake.
  • Lab: Loading Taxi Data into Cloud SQL.

Building a Data Warehouse

  • The modern data warehouse.
  • Intro to BigQuery.
  • Demo: Query TB+ of data in seconds.
  • Getting Started.
  • Loading Data.
  • Video Demo: Querying Cloud SQL from BigQuery.
  • Lab: Loading Data into BigQuery.
  • Exploring Schemas.
  • Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA.
  • Schema Design.
  • Nested and Repeated Fields.
  • Demo: Nested and repeated fields in BigQuery.
  • Lab: Working with JSON and Array data in BigQuery.
  • Optimizing with Partitioning and Clustering.
  • Demo: Partitioned and Clustered Tables in BigQuery.
  • Preview: Transforming Batch and Streaming Data.

Introduction to Building Batch Data Pipelines

  • EL, ELT, ETL.
  • Quality considerations.
  • How to carry out operations in BigQuery.
  • Demo: ELT to improve data quality in BigQuery.
  • ETL to solve data quality issues.

Executing Spark on Cloud Dataproc

  • The Hadoop ecosystem.
  • Running Hadoop on Cloud Dataproc.
  • GCS instead of HDFS.
  • Optimizing Dataproc.
  • Lab: Running Apache Spark jobs on Cloud Dataproc.

Serverless Data Processing with Cloud Dataflow

  • Cloud Dataflow.
  • Why customers value Dataflow.
  • Dataflow Pipelines.
  • Lab: A Simple Dataflow Pipeline (Python/Java).
  • Lab: MapReduce in Dataflow (Python/Java).
  • Lab: Side Inputs (Python/Java).
  • Dataflow Templates.
  • Dataflow SQL.

Manage Data Pipelines with Cloud Data Fusion and Cloud Composer

  • Building Batch Data Pipelines visually with Cloud Data Fusion.
  • UI Overview.
  • Building a Pipeline.
  • Exploring Data using Wrangler.
  • Lab: Building and executing a pipeline graph in Cloud Data Fusion.
  • Orchestrating work between GCP services with Cloud Composer.
  • Apache Airflow Environment.
  • DAGs and Operators.
  • Workflow Scheduling.
  • Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery.
  • Monitoring and Logging.
  • Lab: An Introduction to Cloud Composer.

Introduction to Processing Streaming Data

  • Processing Streaming Data.

Serverless Messaging with Cloud Pub/Sub

  • Cloud Pub/Sub.
  • Lab: Publish Streaming Data into Pub/Sub.

Cloud Dataflow Streaming Features

  • Cloud Dataflow Streaming Features.
  • Lab: Streaming Data Pipelines.

High-Throughput BigQuery and Bigtable Streaming Features

  • BigQuery Streaming Features.
  • Lab: Streaming Analytics and Dashboards.
  • Cloud Bigtable.
  • Lab: Streaming Data Pipelines into Bigtable.

Advanced BigQuery Functionality and Performance

  • Analytic Window Functions.
  • Using With Clauses.
  • GIS Functions.
  • Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz.
  • Performance Considerations.
  • Lab: Optimizing your BigQuery Queries for Performance.
  • Optional Lab: Creating Date-Partitioned Tables in BigQuery.

Introduction to Analytics and AI

  • What is AI?.
  • From Ad-hoc Data Analysis to Data Driven Decisions.
  • Options for ML models on GCP.

 

Prebuilt ML model APIs for Unstructured Data

  • Unstructured Data is Hard.
  • ML APIs for Enriching Data.
  • Lab: Using the Natural Language API to Classify Unstructured Text.

Big Data Analytics with Cloud AI Platform Notebooks

  • Whats a Notebook.
  • BigQuery Magic and Ties to Pandas.
  • Lab: BigQuery in Jupyter Labs on AI Platform.

Production ML Pipelines with Kubeflow

  • Ways to do ML on GCP.
  • AI Hub.
  • Lab: Running AI models on Kubeflow.

Custom Model building with SQL in BigQuery ML

  • BigQuery ML for Quick Model Building.
  • Demo: Train a model with BigQuery ML to predict NYC taxi fares.
  • Supported Models.
  • Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML.
  • Lab Option 2: Movie Recommendations in BigQuery ML.

Custom Model building with Cloud AutoML

  • Why Auto ML?
  • Auto ML Vision.
  • Auto ML NLP.
  • Auto ML Tables.

Module 2: Preparing for the Professional Data Engineer Examination

Understanding the Professional Data Engineer Certification

  • Establish basic knowledge about the certification exam and eliminate any confusion or misunderstandings about the process and nature of the exam itself.

Sample Case Studies for the Professional Data Engineer Exam

  • In-depth review of the Case Studies provided for exam preparation

Designing and Building (Review and preparation tips)

  • Tips and examples covering data processing systems design skills, data structures, and database skills that could be tested on the exam.

Analyzing and Modeling (Review and preparation tips)

  • Tips and examples covering data analysis, analysis and optimization of business processes, and machine learning skills that could be tested on the exam.

Reliability, Policy, and Security (Review and preparation tips)

  • Tips and examples covering reliability, policies, security, and compliance skills that could be tested on the exam.

Resources and next steps

  • Resources for learning more about identified subjects that could be tested on the exam.

The end of the course contains an ungraded practice exam quiz, followed by a graded practice exam quiz that simulates the exam-taking experience.

Open calls