Get a fundamental understanding of how Google BigQuery works by analyzing and querying large datasets About This Book Get started with BigQuery API and write custom applications using it Learn how BigQuery API can be used for storing, managing, and query massive datasets with ease A practical guide with examples and use-cases to teach you everything you need to know about Google BigQuery Who This Book Is For If you are a developer, data analyst, or a data scientist looking to run complex queries over thousands of records in seconds, this book will help you. No prior experience of working with BigQuery is assumed. What You Will Learn Get a hands-on introduction to Google Cloud Platform and its services Understand the different data types supported by Google BigQuery Migrate your enterprise data to BigQuery and query it using the legacy and standard SQL techniques Use partition tables in your project and query external data sources and wild card tables Create tables and data sets dynamically using the BigQuery API Perform real-time inserting of records for analytics using Python and C# Visualize your BigQuery data by connecting it to third party tools such as Tableau and R Master the Google Cloud Pub/Sub for implementing real-time reporting and analytics of your Big Data In Detail Google BigQuery is a popular cloud data warehouse for large-scale data analytics. This book will serve as a comprehensive guide to mastering BigQuery, and how you can utilize it to quickly and efficiently get useful insights from your Big Data. You will begin with getting a quick overview of the Google Cloud Platform and the various services it supports. Then, you will be introduced to the Google BigQuery API and how it fits within in the framework of GCP. The book covers useful techniques to migrate your existing data from your enterprise to Google BigQuery, as well as readying and optimizing it for analysis. You will perform basic as well as advanced data querying using BigQuery, and connect the results to various third party tools for reporting and visualization purposes such as R and Tableau. If you're looking to implement real-time reporting of your streaming data running in your enterprise, this book will also help you. This book also provides tips, best practices and mistakes to avoid while working with Google BigQuery and services that interact with it. By the time you're done with it, you will have set a solid foundation in working with BigQuery to solve even the trickiest of data problems. Style and Approach This book follows a step-by-step approach to teach readers the concepts of Google BigQuery using SQL. To explain various data querying processes, large-scale datasets are used wherever required.

Get a fundamental understanding of how Google BigQuery works by analyzing and querying large datasets About This Book Get started with BigQuery API and write custom applications using it Learn how BigQuery API can be used for storing, managing, and query massive datasets with ease A practical guide with examples and use-cases to teach you everything you need to know about Google BigQuery Who This Book Is For If you are a developer, data analyst, or a data scientist looking to run complex queries over thousands of records in seconds, this book will help you. No prior experience of working with BigQuery is assumed. What You Will Learn Get a hands-on introduction to Google Cloud Platform and its services Understand the different data types supported by Google BigQuery Migrate your enterprise data to BigQuery and query it using the legacy and standard SQL techniques Use partition tables in your project and query external data sources and wild card tables Create tables and data sets dynamically using the BigQuery API Perform real-time inserting of records for analytics using Python and C# Visualize your BigQuery data by connecting it to third party tools such as Tableau and R Master the Google Cloud Pub/Sub for implementing real-time reporting and analytics of your Big Data In Detail Google BigQuery is a popular cloud data warehouse for large-scale data analytics. This book will serve as a comprehensive guide to mastering BigQuery, and how you can utilize it to quickly and efficiently get useful insights from your Big Data. You will begin with getting a quick overview of the Google Cloud Platform and the various services it supports. Then, you will be introduced to the Google BigQuery API and how it fits within in the framework of GCP. The book covers useful techniques to migrate your existing data from your enterprise to Google BigQuery, as well as readying and optimizing it for analysis. You will perform basic as well as advanced data querying using BigQuery, and connect the results to various third party tools for reporting and visualization purposes such as R and Tableau. If you're looking to implement real-time reporting of your streaming data running in your enterprise, this book will also help you. This book also provides tips, best practices and mistakes to avoid while working with Google BigQuery and services that interact with it. By the time you're done with it, you will have set a solid foundation in working with BigQuery to solve even the trickiest of data problems. Style and Approach This book follows a step-by-step approach to teach readers the concepts of Google BigQuery using SQL. To explain various data querying processes, large-scale datasets are used wherever required.

Work with petabyte-scale datasets while building a collaborative, agile workplace in the process. This practical book is the canonical reference to Google BigQuery, the query engine that lets you conduct interactive analysis of large datasets. BigQuery enables enterprises to efficiently store, query, ingest, and learn from their data in a convenient framework. With this book, you’ll examine how to analyze data at scale to derive insights from large datasets efficiently. Valliappa Lakshmanan, tech lead for Google Cloud Platform, and Jordan Tigani, engineering director for the BigQuery team, provide best practices for modern data warehousing within an autoscaled, serverless public cloud. Whether you want to explore parts of BigQuery you’re not familiar with or prefer to focus on specific tasks, this reference is indispensable.

Why is Google Analytics 4 the most modern data model available for digital marketing analytics? Because rather than simply report what has happened, GA4's new cloud integrations enable more data activation—linking online and offline data across all your streams to provide end-to-end marketing data. This practical book prepares you for the future of digital marketing by demonstrating how GA4 supports these additional cloud integrations. Author Mark Edmondson, Google Developer Expert for Google Analytics and Google Cloud, provides a concise yet comprehensive overview of GA4 and its cloud integrations. Data, business, and marketing analysts will learn major facets of GA4's powerful new analytics model, with topics including data architecture and strategy, and data ingestion, storage, and modeling. You'll explore common data activation use cases and get guidance on how to implement them. You'll learn: How Google Cloud integrates with GA4 The potential use cases that GA4 integrations can enable Skills and resources needed to create GA4 integrations How much GA4 data capture is necessary to enable use cases The process of designing dataflows from strategy though data storage, modeling, and activation

Manage different business scenarios with the right machine learning technique using Google's highly scalable BigQuery ML Key FeaturesGain a clear understanding of AI and machine learning services on GCP, learn when to use these, and find out how to integrate them with BigQuery MLLeverage SQL syntax to train, evaluate, test, and use ML modelsDiscover how BigQuery works and understand the capabilities of BigQuery ML using examplesBook Description BigQuery ML enables you to easily build machine learning (ML) models with SQL without much coding. This book will help you to accelerate the development and deployment of ML models with BigQuery ML. The book starts with a quick overview of Google Cloud and BigQuery architecture. You'll then learn how to configure a Google Cloud project, understand the architectural components and capabilities of BigQuery, and find out how to build ML models with BigQuery ML. The book teaches you how to use ML using SQL on BigQuery. You'll analyze the key phases of a ML model's lifecycle and get to grips with the SQL statements used to train, evaluate, test, and use a model. As you advance, you'll build a series of use cases by applying different ML techniques such as linear regression, binary and multiclass logistic regression, k-means, ARIMA time series, deep neural networks, and XGBoost using practical use cases. Moving on, you'll cover matrix factorization and deep neural networks using BigQuery ML's capabilities. Finally, you'll explore the integration of BigQuery ML with other Google Cloud Platform components such as AI Platform Notebooks and TensorFlow along with discovering best practices and tips and tricks for hyperparameter tuning and performance enhancement. By the end of this BigQuery book, you'll be able to build and evaluate your own ML models with BigQuery ML. What you will learnDiscover how to prepare datasets to build an effective ML modelForecast business KPIs by leveraging various ML models and BigQuery MLBuild and train a recommendation engine to suggest the best products for your customers using BigQuery MLDevelop, train, and share a BigQuery ML model from previous parts with AI Platform NotebooksFind out how to invoke a trained TensorFlow model directly from BigQueryGet to grips with BigQuery ML best practices to maximize your ML performanceWho this book is for This book is for data scientists, data analysts, data engineers, and anyone looking to get started with Google's BigQuery ML. You'll also find this book useful if you want to accelerate the development of ML models or if you are a business user who wants to apply ML in an easy way using SQL. Basic knowledge of BigQuery and SQL is required.

Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models. Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start. You'll discover how to: Apply DevOps best practices to machine learning Build production machine learning systems and maintain them Monitor, instrument, load-test, and operationalize machine learning systems Choose the correct MLOps tools for a given machine learning task Run machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware

Learn how to build an end-to-end data to AI solution on Google Cloud using Vertex AI KEY FEATURES ● Harness the power of AutoML capabilities to build machine learning models. ● Learn how to train custom machine learning models on the Google Cloud Platform. ● Accelerate your career in data analytics by leveraging the capabilities of GCP. DESCRIPTION Google Cloud Vertex AI is a platform for machine learning (ML) offered by Google Cloud, with the objective of making the creation, deployment, and administration of ML models on a large scale easier. If you are seeking a unified and collaborative environment for your ML projects, this book is a valuable resource for you. This comprehensive guide is designed to help data enthusiasts effectively utilize Google Cloud Platform's Vertex AI for a wide range of machine learning operations. It covers the basics of the Google Cloud Platform, encompassing cloud storage, big query, and IAM. Subsequently, it delves into the specifics of Vertex AI, including AutoML, custom model training, model deployment on endpoints, development of Vertex AI pipelines, and the Explainable AI feature store. By the time you finish reading this book, you will be able to navigate Vertex AI proficiently, even if you lack prior experience with cloud platforms. With the inclusion of numerous code examples throughout the book, you will be equipped with the necessary skills and confidence to create machine learning solutions using Vertex AI. WHAT YOU WILL LEARN ● Learn how to create projects, store data in GCP, and manage access permissions effectively. ● Discover how AutoML can be utilized for streamlining workflows. ● Learn how to construct pipelines using TFX (TensorFlow Extended) and Kubeflow components. ● Gain an overview of the purpose and significance of the Feature Store. ● Explore the concept of explainable AI and its role in understanding machine learning models. WHO THIS BOOK IS FOR This book is designed for data scientists and advanced AI practitioners who are interested in learning how to perform machine learning tasks on the Google Cloud Platform. Having prior knowledge of machine learning concepts and proficiency in Python programming would greatly benefit readers. TABLE OF CONTENTS 1. Basics of Google Cloud Platform 2. Introduction to Vertex AI and AutoML Tabular 3. AutoML Image, Text, and Pre-built Models 4. Vertex AI Workbench and Custom Model Training 5. Vertex AI Custom Model Hyperparameter and Deployment 6. Introduction to Pipelines and Kubeflow 7. Pipelines using Kubeflow for Custom Models 8. Pipelines using TensorFlow Extended 9. Vertex AI Feature Store 10. Explainable AI