Google Cloud Certified Professional Data Engineer 2023

Published 12/2022MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 1.37 GB | Duration: 3h 28m

Pass Google Cloud Certified Professional Data Eeer Exam 2023

What you'll learn

Designing data processing systems

Building and operationalizing data processing systems

Operationalizing machine learning models

Ensuring solution quality

Designing data pipelines

Designing a data processing solution

Migrating data warehousing and data processing

Building and operationalizing storage systems

Building and operationalizing pipelines

Building and operationalizing processing infrastructure

Leveraging pre-built ML models as a service

Deploying an ML pipeline

Measuring, monitoring, and troubleshooting machine learning models

Designing for security and compliance

Ensuring scalability and efficiency

Ensuring reliability and fidelity

Ensuring flexibility and portability

Requirements

Everything that you need in order to pass Google Cloud Certified Professional Data Eeer will be covered in this course

Description

Designing data processing systemsSelecting the appropriate storage technologies. Considerations include:● Mapping storage systems to business requirements● Data modeling● Trade-offs involving latency, throughput, transactions● Distributed systems● Schema designDesigning data pipelines. Considerations include:● Data publishing and visualization (e.g., BigQuery)● Batch and streaming data (e.g., Dataflow, Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Pub/Sub, Apache Kafka)● Online (interactive) vs. batch predictions● Job automation and orchestration (e.g., Cloud Composer)Designing a data processing solution. Considerations include:● Choice of infrastructure● System availability and fault tolerance● Use of distributed systems● Capacity planning● Hybrid cloud and edge computing● Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)● At least once, in-order, and exactly once, etc., event processingMigrating data warehousing and data processing. Considerations include:● Awareness of current state and how to migrate a design to a future state● Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking)● Validating a migrationBuilding and operationalizing data processing systemsBuilding and operationalizing storage systems. Considerations include:● Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Datastore, Memorystore)● Storage costs and performance● Life cycle management of dataBuilding and operationalizing pipelines. Considerations include:● Data cleansing● Batch and streaming● Transformation● Data acquisition and import● Integrating with new data sourcesBuilding and operationalizing processing infrastructure. Considerations include:● Provisioning resources● Monitoring pipelines● Adjusting pipelines● Testing and quality controlOperationalizing machine learning modelsLeveraging pre-built ML models as a service. Considerations include:● ML APIs (e.g., Vision API, Speech API)● Customizing ML APIs (e.g., AutoML Vision, Auto ML text)● Conversational experiences (e.g., Dialogflow)Deploying an ML pipeline. Considerations include:● Ingesting appropriate data● Retraining of machine learning models (AI Platform Prediction and Training, BigQuery ML, Kubeflow, Spark ML)● Continuous evaluationChoosing the appropriate training and serving infrastructure. Considerations include:● Distributed vs. single machine● Use of edge compute● Hardware accelerators (e.g., GPU, TPU)Measuring, monitoring, and troubleshooting machine learning models. Considerations include:● Machine learning teology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)● Impact of dependencies of machine learning models● Common sources of error (e.g., assumptions about data)Ensuring solution qualityDesigning for security and compliance. Considerations include:● Identity and access management (e.g., Cloud IAM)● Data security (encryption, key management)● Ensuring privacy (e.g., Data Loss Prevention API)● Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children's Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))Ensuring scalability and efficiency. Considerations include:● Building and running test suites● Pipeline monitoring (e.g., Cloud Monitoring)● Assessing, troubleshooting, and improving data representations and data processing infrastructure● Resizing and autoscaling resourcesEnsuring reliability and fidelity. Considerations include:● Perfog data preparation and quality control (e.g., Dataprep)● Verification and monitoring● Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, perfog retrospective re-analysis)● Choosing between ACID, idempotent, eventually consistent requirementsEnsuring flexibility and portability. Considerations include:● Mapping to current and future business requirements● Designing for data and application portability (e.g., multicloud, data residency requirements)● Data staging, cataloging, and discovery

Overview

Section 1: Choosing the RIght Product

Lecture 1 Choosing the Right Product

Section 2: Google Cloud Storage

Lecture 2 Google Cloud Storage

Section 3: Cloud SQL

Lecture 3 Cloud SQL

Section 4: Cloud Dataflow

Lecture 4 Dataflow - Part 1

Lecture 5 Dataflow Lab

Section 5: Cloud Dataproc

Lecture 6 Cloud Dataproc