Data Engineering with Google Cloud Platform is your ultimate guide to building scalable data platforms using Google Cloud technologies. In this book, you will learn how to leverage products such as BigQuery, Cloud Composer, and Dataplex for efficient data engineering. Expand your expertise and gain practical knowledge to excel in managing data pipelines within the Google Cloud ecosystem. What this Book will help me do Understand foundational data engineering concepts using Google Cloud Platform. Learn to build and manage scalable data pipelines with tools such as Dataform and Dataflow. Explore advanced topics like data governance and secure data handling in Google Cloud. Boost readiness for Google Cloud data engineering certification with real-world exam guidance. Master cost-effective strategies and CI/CD practices for data engineering on Google Cloud. Author(s) Adi Wijaya, the author of this book, is a Data Strategic Cloud Engineer at Google with extensive experience in data engineering and the Google Cloud ecosystem. With his hands-on expertise, he emphasizes practical solutions and in-depth knowledge sharing, guiding readers through the intricacies of Google Cloud for data engineering success. Who is it for? This book is ideal for data analysts, IT practitioners, software engineers, and data enthusiasts aiming to excel in data engineering. Whether you're a beginner tackling fundamental concepts or an experienced professional exploring Google Cloud's advanced capabilities, this book is designed for you. It bridges your current skills with modern data engineering practices on Google Cloud, making it a valuable resource at any stage of your career.

This book is your practical and comprehensive guide to learning Google Cloud Platform (GCP) for data science, using only the free tier services offered by the platform. Data science and machine learning are increasingly becoming critical to businesses of all sizes, and the cloud provides a powerful platform for these applications. GCP offers a range of data science services that can be used to store, process, and analyze large datasets, and train and deploy machine learning models. The book is organized into seven chapters covering various topics such as GCP account setup, Google Colaboratory, Big Data and Machine Learning, Data Visualization and Business Intelligence, Data Processing and Transformation, Data Analytics and Storage, and Advanced Topics. Each chapter provides step-by-step instructions and examples illustrating how to use GCP services for data science and big data projects. Readers will learn how to set up a Google Colaboratory account and run Jupyternotebooks, access GCP services and data from Colaboratory, use BigQuery for data analytics, and deploy machine learning models using Vertex AI. The book also covers how to visualize data using Looker Data Studio, run data processing pipelines using Google Cloud Dataflow and Dataprep, and store data using Google Cloud Storage and SQL. What You Will Learn Set up a GCP account and project Explore BigQuery and its use cases, including machine learning Understand Google Cloud AI Platform and its capabilities Use Vertex AI for training and deploying machine learning models Explore Google Cloud Dataproc and its use cases for big data processing Create and share data visualizations and reports with Looker Data Studio Explore Google Cloud Dataflow and its use cases for batch and stream data processing Run data processing pipelines on Cloud Dataflow Explore Google Cloud Storageand its use cases for data storage Get an introduction to Google Cloud SQL and its use cases for relational databases Get an introduction to Google Cloud Pub/Sub and its use cases for real-time data streaming Who This Book Is For Data scientists, machine learning engineers, and analysts who want to learn how to use Google Cloud Platform (GCP) for their data science and big data projects

This book provides detailed guidance around architecting and deploying Power BI reporting solutions, including help and best practices for sharing and security. You’ll find chapters on dataflows, shared datasets, composite model and DirectQuery connections to Power BI datasets, deployment pipelines, XMLA endpoints, and many other important features related to the overall Power BI architecture that are new since the first edition. You will gain an understanding of what functionality each of the Power BI components provide (such as Dataflow, Shared Dataset, Datamart, thin reports, and paginated reports), so that you can make an informed decision about what components to use in your solution. You will get to know the pros and cons of each component, and how they all work together within the larger Power BI architecture. Commonly encountered problems you will learn to handle include content unexpectedly changing while users are in the process of creating reports and building analyses, methods of sharing analyses that don’t cover all the requirements of your business or organization, and inconsistent security models. Detailed examples help you to understand and choose from among the different methods available for sharing and securing Power BI content so that only intended recipients can see it. The knowledge provided in this book will allow you to choose an architecture and deployment model that suits the needs of your organization. It will also help ensure that you do not spend your time maintaining your solution, but on using it for its intended purpose: gaining business value from mining and analyzing your organization’s data. What You Will Learn Architect Power BI solutions that are reliable and easy to maintain Create development templates and structures in support of reusability Set up and configure the Power BI gateway as a bridge between on-premises data sourcesand the Power BI cloud service Select a suitable connection type—Live Connection, DirectQuery, Scheduled Refresh, or Composite Model—for your use case Choose the right sharing method for how you are using Power BI in your organization Create and manage environments for development, testing, and production Secure your data using row-level and object-level security Save money by choosing the right licensing plan Who This Book Is For Data analysts and developers who are building reporting solutions around Power BI, as well as architects and managers who are responsible for the big picture of how Power BI meshes with an organization’s other systems, including database and data warehouse systems.

In 'Data Engineering with Google Cloud Platform', you'll explore how to construct efficient, scalable data pipelines using GCP services. This hands-on guide covers everything from building data warehouses to deploying machine learning pipelines, helping you master GCP's ecosystem. What this Book will help me do Build comprehensive data ingestion and transformation pipelines using BigQuery, Cloud Storage, and Dataflow. Design end-to-end orchestration flows with Airflow and Cloud Composer for automated data processing. Leverage Pub/Sub for building real-time event-driven systems and streaming architectures. Gain skills to design and manage secure data systems with IAM and governance strategies. Prepare for and pass the Professional Data Engineer certification exam to elevate your career. Author(s) Adi Wijaya is a seasoned data engineer with significant experience in Google Cloud Platform products and services. His expertise in building data systems has equipped him with insights into the real-world challenges data engineers face. Adi aims to demystify technical topics and deliver practical knowledge through his writing, helping tech professionals excel. Who is it for? This book is tailored for data engineers and data analysts who want to leverage GCP for building efficient and scalable data systems. Readers should have a beginner-level understanding of topics like data science, Python, and Linux to fully benefit from the material. It is also suitable for individuals preparing for the Google Professional Data Engineer exam. The book is a practical companion for enhancing cloud and data engineering skills.

Learn how easy it is to apply sophisticated statistical and machine learning methods to real-world problems when you build using Google Cloud Platform (GCP). This hands-on guide shows data engineers and data scientists how to implement an end-to-end data pipeline with cloud native tools on GCP. Throughout this updated second edition, you'll work through a sample business decision by employing a variety of data science approaches. Follow along by building a data pipeline in your own project on GCP, and discover how to solve data science problems in a transformative and more collaborative way. You'll learn how to: Employ best practices in building highly scalable data and ML pipelines on Google Cloud Automate and schedule data ingest using Cloud Run Create and populate a dashboard in Data Studio Build a real-time analytics pipeline using Pub/Sub, Dataflow, and BigQuery Conduct interactive data exploration with BigQuery Create a Bayesian model with Spark on Cloud Dataproc Forecast time series and do anomaly detection with BigQuery ML Aggregate within time windows with Dataflow Train explainable machine learning models with Vertex AI Operationalize ML with Vertex AI Pipelines

Abstract As big data becomes more ubiquitous, businesses are wondering how they can best leverage it to gain insight into their most important business questions. Using machine learning (ML) and deep learning (DL) in big data environments can identify historical patterns and build artificial intelligence (AI) models that can help businesses to improve customer experience, add services and offerings, identify new revenue streams or lines of business (LOBs), and optimize business or manufacturing operations. The power of AI for predictive analytics is being harnessed across all industries, so it is important that businesses familiarize themselves with all of the tools and techniques that are available for integration with their data lake environments. In this IBM® Redbooks® publication, we cover the best practices for deploying and integrating some of the best AI solutions on the market, including: IBM Watson Machine Learning Accelerator (see note for product naming) IBM Watson Studio Local IBM Power Systems™ IBM Spectrum™ Scale IBM Data Science Experience (IBM DSX) IBM Elastic Storage™ Server Hortonworks Data Platform (HDP) Hortonworks DataFlow (HDF) H2O Driverless AI We map out all the integrations that are possible with our different AI solutions and how they can integrate with your existing or new data lake. We also walk you through some of our client use cases and show you how some of the industry leaders are using Hortonworks, IBM PowerAI, and IBM Watson Studio Local to drive decision making. We also advise you on your deployment options, when to use a GPU, and why you should use the IBM Elastic Storage Server (IBM ESS) to improve storage management. Lastly, we describe how to integrate IBM Watson Machine Learning Accelerator and Hortonworks with or without IBM Watson Studio Local, how to access real-time data, and security. Note: IBM Watson Machine Learning Accelerator is the new product name for IBM PowerAI Enterprise. Note: Hortonworks merged with Cloudera in January 2019. The new company is called Cloudera. References to Hortonworks as a business entity in this publication are now referring to the merged company. Product names beginning with Hortonworks continue to be marketed and sold under their original names.

Since its first volume in 1960, Advances in Computers has presented detailed coverage of innovations in computer hardware, software, theory, design, and applications. It has also provided contributors with a medium in which they can explore their subjects in greater depth and breadth than journal articles usually allow. As a result, many articles have become standard references that continue to be of significant, lasting value in this rapidly expanding field. In-depth surveys and tutorials on new computer technology Well-known authors and researchers in the field Extensive bibliographies with most chapters Many of the volumes are devoted to single themes or subfields of computer science