Google Professional Data Cloud Engineer Training Program

Data publishing and visualization

Online (interactive) vs. batch predictions

Batch and streaming data (e.g., Cloud Dataflow, Cloud Dataproc, Apache Spark and Hadoop ecosystem, Cloud Pub/Sub, Apache Kafka)

Big Data Ecosystem

• MapReduce

• Hadoop & HDFS

• Apache Pig

• Apache Spark

• Apache Kafka

Real-time Messaging with Pub/Sub

• Pub/sub basics

• pub/Sub Terminologies

• Advanced Pub/Sub Concepts

• Working with Pub/Sub

Cloud Data Flow Pipelining

• Introduction to Data flow

• Pipeline Lifecycle

• Dataflow pipeline concepts

• Advanced Dataflow concepts

• Dataflow security and access

• Using Dataflow

Cloud Dataproc

• Dataproc Basics

• Working with Dataproc

• Advanced Dataproc

NoSQL Data with Cloud Big Table

• Big Table Concepts

• Big Table Architecture

• Big Table Data Model

• Big Table Schema Design

• Big Table Advanced Concepts

Data Analytics using BigQuery

• BigQuery Basics

• Using BigQuery

• Partitioning and Clustering

• Best Practices

• Securing BigQuery

• BigQuery Monitoring and Logging

• Machine Learning with BigQuery ML

• Working with BigQuery

• Advanced BigQuery Concepts

Data Exploration with Cloud Datalab

• Datalab Concepts

• Working with Datalab

Visualization with Cloud Data Studio

• Reporting & Business intelligence

• Data Distribution

• Introduction to Cloud Data Studio

• Charts and Filters

Job automation and orchestration (e.g., Cloud Composer)

• Orchestration with Cloud Composer

• Cloud Composer Overview

• Cloud Composer Architecture

• Working with Cloud Composer

• Advanced Cloud Composer Concepts

Steps for Designing

• 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 processing

Migrating data warehousing and data processing

• 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 migration

Building and operationalizing Storage Solutions

• Cloud Managed Services

• Effectives Use of Managed Services

• Storage Cost and performance

• Lifecycle Management of Data

Building and operationalizing Pipelines

• Data cleansing

• Batch and streaming

• Transformation

• Data acquisition and import

• Integrating with new data sources

Building and operationalizing processing infrastructure

• Provisioning resources

• Monitoring pipelines

• Adjusting pipelines

• Testing and quality control

Introduction to Machine Learning

• Machine Learning Introduction

• Machine Learning Basics

• Machine Learning Types and Models

• Overfitting

• Hyperparameters

• Feature Engineering

Machine Learning with TesnorFlow

• Deep Learning with TensorFlow

• Introduction to Artificial Neural Networks

• Neural Network Architectures

• Building a Neural Network

Leveraging 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

• Ingesting appropriate data

• Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)

• Continuous evaluation

Choosing the appropriate training and serving infrastructure

• Distributed vs. single machine

• Use of edge compute

• Hardware accelerators (e.g., GPU, TPU)

Measuring, monitoring, and troubleshooting machine learning models

• Machine learning terminology (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)