Google Cloud-Professional Data Engineer

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)