Transform your data replication strategy into a competitive advantage with Oracle GoldenGate 23ai. This comprehensive guide delivers the practical knowledge DBAs and architects need to implement, optimize , and scale Oracle GoldenGate 23ai in production environments. Written by Oracle ACE Director Bobby Curtis, it blends deep technical expertise with real-world business insights from hundreds of implementations across manufacturing, financial services, and technology sectors. Beyond traditional replication, this book explores the groundbreaking capabilities that make GoldenGate 23ai essential for modern AI initiatives. Learn how to implement real-time vector replication for RAG systems, integrate with cloud platforms like GCP and Snowflake, and automate deployments using REST APIs and Python. Each chapter offers proven strategies to deliver measurable ROI while reducing operational risk. Whether you're upgrading from Classic GoldenGate , deploying your first cloud data pipeline, or building AI-ready data architectures, this book provides the strategic guidance and technical depth to succeed. With Bobby's signature direct approach, you'll avoid common pitfalls and implement best practices that scale with your business. What You Will Learn Master the microservices architecture and new capabilities of Oracle GoldenGate 23ai Implement secure, high-performance data replication across Oracle, PostgreSQL, and cloud databases Configure vector replication for AI and machine learning workloads, including RAG systems Design and build multi-master replication models with automatic conflict resolution Automate deployments and management using RESTful APIs and Python Optimize performance for sub-second replication lag in production environments Secure your replication environment with enterprise-grade features and compliance Upgrade from Classic to Microservices architecture with zero downtime Integrate with cloud platforms including OCI, GCP, AWS, and Azure Implement real-time data pipelines to BigQuery , Snowflake, and other cloud targets Navigate Oracle licensing models and optimize costs Who This Book Is For Database administrators, architects, and IT leaders working with Oracle GoldenGate —whether deploying for the first time, migrating from Classic architecture, or enabling AI-driven replication—will find actionable guidance on implementation, performance tuning, automation, and cloud integration. Covers unidirectional and multi-master replication and is packed with real-world use cases.

Follow the cloud journey of a fictional company Nimbus Airlines and the process it goes through to modernize its SAP systems. This book provides a detailed guide for those looking to transition their SAP systems to the cloud using Amazon Web Services (AWS). Through the lens of various characters, the book is structured in three parts — starting with an introduction to SAP and AWS fundamentals, followed by technical architecture insights, and concluding with migration strategies and case studies, the book covers technical aspects of modernizing SAP with AWS. You’ll review the partnership between SAP and AWS, highlighted by their long-standing collaboration and shared innovations. Then design an AWS architecture tailored for SAP workloads, including high availability, disaster recovery, and operations automation. The book concludes with a tour of the migration process, offering various strategies, tools, and frameworks reinforced with real-world customer case studies that showcase successful SAP migrations to AWS. Modernizing SAP with AWS equips business leaders and technical architects with the knowledge to leverage AWS for their SAP systems, ensuring a smooth transition and unlocking new opportunities for innovation. What You Will Learn Understand the fundamentals of AWS and its key components, including computing, storage, networking, and microservices, for SAP systems. Explore the technical partnership between SAP and AWS, learning how their collaboration drives innovation and delivers business value. Design an optimized AWS architecture for SAP workloads, focusing on high availability, disaster recovery, and operations automation. Discover innovative ways to enhance and extend SAP functionality using AWS tools for better system performance and automation. Who This Book Is For SAP professionals and consultants interested in learning how AWS can enhance SAP performance, security, and automation. Cloud engineers and developers involved in SAP migration projects, looking for best practices and real-world case studies for successful implementation. Enterprise architects seeking to design optimized, scalable, and secure SAP infrastructure on AWS. CIOs, CTOs, and IT managers aiming to modernize SAP systems and unlock innovation through cloud technology.

Learn how to harness the power of OpenSearch effectively with 'The Definitive Guide to OpenSearch'. This book explores installation, configuration, query building, and visualization, guiding readers through practical use cases and real-world implementations. Whether you're building search experiences or analyzing data patterns, this guide equips you thoroughly. What this Book will help me do Understand core OpenSearch principles, architecture, and the mechanics of its search and analytics capabilities. Learn how to perform data ingestion, execute advanced queries, and produce insightful visualizations on OpenSearch Dashboards. Implement scaling strategies and optimum configurations for high-performance OpenSearch clusters. Explore real-world case studies that demonstrate OpenSearch applications in diverse industries. Gain hands-on experience through practical exercises and tutorials for mastering OpenSearch functionality. Author(s) Jon Handler, Soujanya Konka, and Prashant Agrawal, celebrated experts in search technologies and big data analysis, bring their years of experience at AWS and other domains to this book. Their collective expertise ensures that readers receive both core theoretical knowledge and practical applications to implement directly. Who is it for? This book is aimed at developers, data professionals, engineers, and systems operators who work with search systems or analytics platforms. It is especially suitable for individuals in roles handling large-scale data, who want to improve their skills or deploy OpenSearch in production environments. Early learners and seasoned experts alike will find valuable insights.

There's no better time to become a data engineer. And acing the AWS Certified Data Engineer Associate (DEA-C01) exam will help you tackle the demands of modern data engineering and secure your place in the technology-driven future. Authors Sakti Mishra, Dylan Qu, and Anusha Challa equip you with the knowledge and sought-after skills necessary to effectively manage data and excel in your career. Whether you're a data engineer, data analyst, or machine learning engineer, you'll discover in-depth guidance, practical exercises, sample questions, and expert advice you need to leverage AWS services effectively and achieve certification. By reading, you'll learn how to: Ingest, transform, and orchestrate data pipelines effectively Select the ideal data store, design efficient data models, and manage data lifecycles Analyze data rigorously and maintain high data quality standards Implement robust authentication, authorization, and data governance protocols Prepare thoroughly for the DEA-C01 exam with targeted strategies and practices

Amazon Redshift Cookbook provides practical techniques for utilizing AWS's managed data warehousing service effectively. With this book, you'll learn to create scalable and secure data analytics solutions, tackle data integration challenges, and leverage Redshift's advanced features like data sharing and generative AI capabilities. What this Book will help me do Create end-to-end data analytics solutions from ingestion to reporting using Amazon Redshift. Optimize the performance and security of Redshift implementations to meet enterprise standards. Leverage Amazon Redshift for zero-ETL ingestion and advanced concurrency scaling. Integrate Redshift with data lakes for enhanced data processing versatility. Implement generative AI and machine learning solutions directly within Redshift environments. Author(s) Shruti Worlikar, Harshida Patel, and Anusha Challa are seasoned data experts who bring together years of experience with Amazon Web Services and data analytics. Their combined expertise enables them to offer actionable insights, hands-on recipes, and proven strategies for implementing and optimizing Amazon Redshift-based solutions. Who is it for? This book is best suited for data analysts, data engineers, and architects who are keen on mastering modern data warehouse solutions using Redshift. Readers should have some knowledge of data warehousing and familiarity with cloud concepts. Ideal for professionals looking to migrate on-premises systems or build cloud-native analytics pipelines leveraging Redshift.

Your complete Guide to preparing for the AWS® Certified Data Engineer: Associate exam The AWS® Certified Data Engineer Study Guide is your one-stop resource for complete coverage of the challenging DEA-C01 Associate exam. This Sybex Study Guide covers 100% of the DEA-C01 objectives. Prepare for the exam faster and smarter with Sybex thanks to accurate content including, an assessment test that validates and measures exam readiness, real-world examples and scenarios, practical exercises, and challenging chapter review questions. Reinforce and retain what you’ve learned with the Sybex online learning environment and test bank, accessible across multiple devices. Get ready for the AWS Certified Data Engineer exam – quickly and efficiently – with Sybex. Coverage of 100% of all exam objectives in this Study Guide means you’ll be ready for: Data Ingestion and Transformation Data Store Management Data Operations and Support Data Security and Governance ABOUT THE AWS DATA ENGINEER – ASSOCIATE CERTIFICATION The AWS Data Engineer – Associate certification validates skills and knowledge in core data-related Amazon Web Services. It recognizes your ability to implement data pipelines and to monitor, troubleshoot, and optimize cost and performance issues in accordance with best practices Interactive learning environment Take your exam prep to the next level with Sybex’s superior interactive online study tools. To access our learning environment, simply visit www.wiley.com/go/sybextestprep, register your book to receive your unique PIN, and instantly gain one year of FREE access after activation to: • Interactive test bank with 5 practice exams to help you identify areas where further review is needed. Get more than 90% of the answers correct, and you’re ready to take the certification exam. • 100 electronic flashcards to reinforce learning and last-minute prep before the exam • Comprehensive glossary in PDF format gives you instant access to the key terms so you are fully prepared

Explore Generative AI and understand its key concepts, architecture, and tangible business use cases. This book will help you develop the skills needed to use SAP AI Core service features available in the SAP Business Technology Platform. You’ll examine large language model (LLM) concepts and gain the practical knowledge to unleash the best use of Gen AI. As you progress, you’ll learn how to get started with your own LLM models and work with Generative AI use cases. Additionally, you’ll see how to take advantage Amazon Bedrock stack using AWS SDK for ABAP. To fully leverage your knowledge, Generative AI with SAP and Amazon Bedrock offers practical step-by-step instructions for how to establish a cloud SAP BTP account model and create your first GenAIartifacts. This work is an important prerequisite for those who want to take full advantage of generative AI with SAP. What You Will Learn Master the concepts and terminology of artificial intelligence and GenAI Understand opportunities and impacts for different industries with GenAI Become familiar with SAP AI Core, Amazon Bedrock, AWS SDK for ABAP and develop your firsts GenAI projects Accelerate your development skills Gain more productivity and time implementing GenAI use cases Who this Book Is For Anyone who wants to learn about Generative AI for Enterprise and SAP practitioners who want to take advantage of AI within the SAP ecosystem to support their systems and workflows.

Explore Snowflake’s core concepts and unique features that differentiates it from industry competitors, such as, Azure Synapse and Google BigQuery. This book provides recipes for architecting and developing modern data pipelines on the Snowflake data platform by employing progressive techniques, agile practices, and repeatable strategies. You’ll walk through step-by-step instructions on ready-to-use recipes covering a wide range of the latest development topics. Then build scalable development pipelines and solve specific scenarios common to all modern data platforms, such as, data masking, object tagging, data monetization, and security best practices. Throughout the book you’ll work with code samples for Amazon Web Services, Microsoft Azure, and Google Cloud Platform. There’s also a chapter devoted to solving machine learning problems with Snowflake. Authors Dillon Dayton and John Eipe are both Snowflake SnowPro Core certified, specializing in data and digital services, and understand the challenges of finding the right solution to complex problems. The recipes in this book are based on real world use cases and examples designed to help you provide quality, performant, and secured data to solve business initiatives. What You’ll Learn Handle structured and un- structured data in Snowflake. Apply best practices and different options for data transformation. Understand data application development. Implement data sharing, data governance and security. Who This book Is For Data engineers, scientists and analysts moving into Snowflake, looking to build data apps. This book expects basic knowledge in Cloud (AWS or Azure or GCP), SQL and Python

This book provides a comprehensive approach to manage PostgreSQL cluster databases on Amazon Web Services and Azure Web Services on the cloud, as well as in Docker and container environments on a Red Hat operating system. Furthermore, detailed references for managing PostgreSQL on both Windows and Mac are provided. This book condenses all the fundamental and essential concepts you need to manage a PostgreSQL cluster into a one-stop guide that is perfect for newcomers to Postgres database administration. Each chapter of the book provides historical context and documents version changes of the PostgreSQL cluster, elucidates practical "how-to" methods, and includes illustrations and key word definitions, practices for application, a summary of key learnings, and questions to reinforce understanding. The book also outlines a clear study objective with a weekly learning schedule and hundreds of practice exercises, along with questions and answers. With its comprehensive and practical approach, this book will help you gain the confidence to manage all aspects of a PostgreSQL cluster in critical production environments so you can better support your organization's database infrastructure on the cloud and in containers. What You Will Learn Install and configure Postgres clusters on the cloud and in containers, monitor database logs, start and stop databases, troubleshoot, tune performance, backup and recover, and integrate with Amazon S3 and Azure Data Blob Manage Postgres databases on Amazon Web Services and Azure Web Services on the cloud, as well as in Docker and container environments on a Red Hat operating system Access sample references to scripting solutions and database management tools for working with Postgres, Redshift (based on Postgres 8.2), and Docker Create Amazon Machine Images (AMI) and Azure Images for managing a fleet of Postgres clusters on the cloud Reinforce knowledge with a weekly learning schedule and hundreds of practice exercises, along with questions and answers Progress from simple concepts, such as how to choose the correct instance type, to creating complex machine images Gain access to an Amazon AMI with a DBA admin tool, allowing you to learn Postgres, Redshift, and Docker in a cloud environment Refer to a comprehensive summary of documentations of Postgres, Amazon Web services, Azure Web services, and Red Hat Linux for managing all aspects of Postgres cluster management on the cloud Who This Book Is For Newcomers to PostgreSQL database administration and cross-platform support DBAs looking to master PostgreSQL on the cloud.

Data Engineering with AWS Cookbook serves as a comprehensive practical guide for building scalable and efficient data engineering solutions using AWS. With this book, you will master implementing data lakes, orchestrating data pipelines, and creating serving layers using AWS's robust services, such as Glue, EMR, Redshift, and Athena. With hands-on exercises and practical recipes, you will enhance your AWS-based data engineering projects. What this Book will help me do Gain the skills to design centralized data lake solutions and manage them securely at scale. Develop expertise in crafting data pipelines with AWS's ETL technologies like Glue and EMR. Learn to implement and automate governance, orchestration, and monitoring for data platforms. Build high-performance data serving layers using AWS analytics tools like Redshift and QuickSight. Effectively plan and execute data migrations to AWS from on-premises infrastructure. Author(s) Trâm Ngọc Phạm, Gonzalo Herreros González, Viquar Khan, and Huda Nofal bring together years of collective experience in data engineering and AWS cloud solutions. Each author's deep knowledge and passion for cloud technology have shaped this book into a valuable resource, geared towards practical learning and real-world application. Their approach ensures readers are not just learning but building tangible, impactful solutions. Who is it for? This book is geared towards data engineers and big data professionals engaged in or transitioning to cloud-based environments, specifically on AWS. Ideal readers are those looking to optimize workflows and master AWS tools to create scalable, efficient solutions. The content assumes a basic familiarity with AWS concepts like IAM roles and a command-line interface, ensuring all examples are accessible yet meaningful for those seeking advancement in AWS data engineering.

"Apache Airflow Best Practices" is your go-to guide for mastering data workflow orchestration using Apache Airflow. This book introduces you to core concepts and features of Airflow and helps you efficiently design, deploy, and manage workflows. With detailed examples and hands-on tutorials, you'll learn how to tackle real-world challenges in data engineering. What this Book will help me do Understand and utilize the features and updates introduced in Apache Airflow 2.x. Design and implement robust, scalable, and efficient data pipelines and workflows. Learn best practices for deploying Apache Airflow in cloud environments such as AWS and GCP. Extend Airflow's functionality with custom plugins and advanced configuration. Monitor, maintain, and scale your Airflow deployment effectively for high availability. Author(s) Dylan Intorf, Dylan Storey, and Kendrick van Doorn are seasoned professionals in data engineering, data strategy, and software development. Between them, they bring decades of experience working in diverse industries like finance, tech, and life sciences. They bring their expertise into this practical guide to help practitioners understand and master Apache Airflow. Who is it for? This book is tailored for data professionals such as data engineers, scientists, and system administrators, offering valuable insights for new learners and experienced users. If you're starting with workflow orchestration, seeking to optimize your current Airflow implementation, or scaling efforts, this book aligns with your goals. Readers should have a basic knowledge of Python programming and data engineering principles.

The "LLM Engineer's Handbook" is your comprehensive guide to mastering Large Language Models from concept to deployment. Written by leading experts, it combines theoretical foundations with practical examples to help you build, refine, and deploy LLM-powered solutions that solve real-world problems effectively and efficiently. What this Book will help me do Understand the principles and approaches for training and fine-tuning Large Language Models (LLMs). Apply MLOps practices to design, deploy, and monitor your LLM applications effectively. Implement advanced techniques such as retrieval-augmented generation (RAG) and preference alignment. Optimize inference for high performance, addressing low-latency and high availability for production systems. Develop robust data pipelines and scalable architectures for building modular LLM systems. Author(s) Paul Iusztin and Maxime Labonne are experienced AI professionals specializing in natural language processing and machine learning. With years of industry and academic experience, they are dedicated to making complex AI concepts accessible and actionable. Their collaborative authorship ensures a blend of theoretical rigor and practical insights tailored for modern AI practitioners. Who is it for? This book is tailored for AI engineers, NLP professionals, and LLM practitioners who wish to deepen their understanding of Large Language Models. Ideal readers possess some familiarity with Python, AWS, and general AI concepts. If you aim to apply LLMs to real-world scenarios or enhance your expertise in AI-driven systems, this handbook is designed for you.

This book covers modern data engineering functions and important Python libraries, to help you develop state-of-the-art ML pipelines and integration code. The book begins by explaining data analytics and transformation, delving into the Pandas library, its capabilities, and nuances. It then explores emerging libraries such as Polars and CuDF, providing insights into GPU-based computing and cutting-edge data manipulation techniques. The text discusses the importance of data validation in engineering processes, introducing tools such as Great Expectations and Pandera to ensure data quality and reliability. The book delves into API design and development, with a specific focus on leveraging the power of FastAPI. It covers authentication, authorization, and real-world applications, enabling you to construct efficient and secure APIs using FastAPI. Also explored is concurrency in data engineering, examining Dask's capabilities from basic setup to crafting advanced machine learning pipelines. The book includes development and delivery of data engineering pipelines using leading cloud platforms such as AWS, Google Cloud, and Microsoft Azure. The concluding chapters concentrate on real-time and streaming data engineering pipelines, emphasizing Apache Kafka and workflow orchestration in data engineering. Workflow tools such as Airflow and Prefect are introduced to seamlessly manage and automate complex data workflows. What sets this book apart is its blend of theoretical knowledge and practical application, a structured path from basic to advanced concepts, and insights into using state-of-the-art tools. With this book, you gain access to cutting-edge techniques and insights that are reshaping the industry. This book is not just an educational tool. It is a career catalyst, and an investment in your future as a data engineering expert, poised to meet the challenges of today's data-driven world. What You Will Learn Elevate your data wrangling jobs by utilizing the power of both CPU and GPU computing, and learn to process data using Pandas 2.0, Polars, and CuDF at unprecedented speeds Design data validation pipelines, construct efficient data service APIs, develop real-time streaming pipelines and master the art of workflow orchestration to streamline your engineering projects Leverage concurrent programming to develop machine learning pipelines and get hands-on experience in development and deployment of machine learning pipelines across AWS, GCP, and Azure Who This Book Is For Data analysts, data engineers, data scientists, machine learning engineers, and MLOps specialists

Master Amazon DynamoDB, the serverless NoSQL database designed for lightning-fast performance and scalability, with this definitive guide. You'll delve into its features, learn advanced concepts, and acquire practical skills to harness DynamoDB for modern application development. What this Book will help me do Understand AWS DynamoDB fundamentals for real-world applications. Model and optimize NoSQL databases with advanced techniques. Integrate DynamoDB into scalable, high-performance architectures. Utilize DynamoDB indexing, caching, and analytical features effectively. Plan and execute RDBMS to NoSQL data migrations successfully. Author(s) None Dhingra, an AWS DynamoDB solutions expert, and None Mackay, a seasoned NoSQL architect, bring their combined expertise straight from Amazon Web Services to guide you step-by-step in mastering DynamoDB. Combining comprehensive technical knowledge with approachable explanations, they empower readers to implement practical and efficient data strategies. Who is it for? This book is ideal for software developers and architects seeking to deepen their knowledge about AWS solutions like DynamoDB, engineering managers aiming to incorporate scalable NoSQL solutions into their projects, and data professionals transitioning from RDBMS towards a serverless data approach. Individuals with basic knowledge in cloud computing or database systems and those ready to advance in DynamoDB will find this book particularly beneficial.

When you look at operational analytics and business data analysis activities—such as log analytics, real-time application monitoring, website search, observability, and more—effective search functionality is key to identifying issues, improving customers experience, and increasing operational effectiveness. How can you support your business needs by leveraging ML-driven advancements in search relevance? In this report, authors Jon Handler, Milind Shyani, Karen Kilroy help executives and data scientists explore how ML can enable ecommerce firms to generate more pertinent search results to drive better sales. You'll learn how personalized search helps you quickly find relevant data within applications, websites, and data lake catalogs. You'll also discover how to locate the content available in CRM systems and document stores. This report helps you: Address the challenges of traditional document search, including data preparation and ingestion Leverage ML techniques to improve search outcomes and the relevance of documents you retrieve Discover what makes a good search solution that's reliable, scalable, and can drive your business forward Learn how to choose a search solution to improve your decision-making process With advancements in ML-driven search, businesses can realize even more benefits and improvements in their data and document search capabilities to better support their own business needs and the needs of their customers. About the authors: Jon Handler is a senior principal solutions architect at Amazon Web Services. Milind Shyani is an applied scientist at Amazon Web Services working on large language models, information retrieval and machine learning algorithms. Karen Kilroy, CEO of Kilroy Blockchain, is a lifelong technologist, full stack software engineer, speaker, and author living in Northwest Arkansas.

Design and architect new generation cloud-based data warehouses using Azure and AWS. This book provides an in-depth understanding of how to build modern cloud-native data warehouses, as well as their history and evolution. The book starts by covering foundational data warehouse concepts, and introduces modern features such as distributed processing, big data storage, data streaming, and processing data on the cloud. You will gain an understanding of the synergy, relevance, and usage data warehousing standard practices in the modern world of distributed data processing. The authors walk you through the essential concepts of Data Mesh, Data Lake, Lakehouse, and Delta Lake. And they demonstrate the services and offerings available on Azure and AWS that deal with data orchestration, data democratization, data governance, data security, and business intelligence. After completing this book, you will be ready to design and architect enterprise-grade, cloud-based modern data warehouses using industry best practices and guidelines. What You Will Learn Understand the core concepts underlying modern data warehouses Design and build cloud-native data warehousesGain a practical approach to architecting and building data warehouses on Azure and AWS Implement modern data warehousing components such as Data Mesh, Data Lake, Delta Lake, and Lakehouse Process data through pandas and evaluate your model’s performance using metrics such as F1-score, precision, and recall Apply deep learning to supervised, semi-supervised, and unsupervised anomaly detection tasks for tabular datasets and time series applications Who This Book Is For Experienced developers, cloud architects, and technology enthusiasts looking to build cloud-based modern data warehouses using Azure and AWS

Learn data engineering and modern data pipeline design with AWS in this comprehensive guide! You will explore key AWS services like S3, Glue, Redshift, and QuickSight to ingest, transform, and analyze data, and you'll gain hands-on experience creating robust, scalable solutions. What this Book will help me do Understand and implement data ingestion and transformation processes using AWS tools. Optimize data for analytics with advanced AWS-powered workflows. Build end-to-end modern data pipelines leveraging cutting-edge AWS technologies. Design data governance strategies using AWS services for security and compliance. Visualize data and extract insights using Amazon QuickSight and other tools. Author(s) Gareth Eagar is a Senior Data Architect with over 25 years of experience in designing and implementing data solutions across various industries. He combines his deep technical expertise with a passion for teaching, aiming to make complex concepts approachable for learners at all levels. Who is it for? This book is intended for current or aspiring data engineers, data architects, and analysts seeking to leverage AWS for data engineering. It suits beginners with a basic understanding of data concepts who want to gain practical experience as well as intermediate professionals aiming to expand into AWS-based systems.

Amazon Redshift powers analytic cloud data warehouses worldwide, from startups to some of the largest enterprise data warehouses available today. This practical guide thoroughly examines this managed service and demonstrates how you can use it to extract value from your data immediately, rather than go through the heavy lifting required to run a typical data warehouse. Analytic specialists Rajesh Francis, Rajiv Gupta, and Milind Oke detail Amazon Redshift's underlying mechanisms and options to help you explore out-of-the box automation. Whether you're a data engineer who wants to learn the art of the possible or a DBA looking to take advantage of machine learning-based auto-tuning, this book helps you get the most value from Amazon Redshift. By understanding Amazon Redshift features, you'll achieve excellent analytic performance at the best price, with the least effort. This book helps you: Build a cloud data strategy around Amazon Redshift as foundational data warehouse Get started with Amazon Redshift with simple-to-use data models and design best practices Understand how and when to use Redshift Serverless and Redshift provisioned clusters Take advantage of auto-tuning options inherent in Amazon Redshift and understand manual tuning options Transform your data platform for predictive analytics using Redshift ML and break silos using data sharing Learn best practices for security, monitoring, resilience, and disaster recovery Leverage Amazon Redshift integration with other AWS services to unlock additional value

Gain deep insight into real-time analytics, including the features of these systems and the problems they solve. With this practical book, data engineers at organizations that use event-processing systems such as Kafka, Google Pub/Sub, and AWS Kinesis will learn how to analyze data streams in real time. The faster you derive insights, the quicker you can spot changes in your business and act accordingly. Author Mark Needham from StarTree provides an overview of the real-time analytics space and an understanding of what goes into building real-time applications. The book's second part offers a series of hands-on tutorials that show you how to combine multiple software products to build real-time analytics applications for an imaginary pizza delivery service. You will: Learn common architectures for real-time analytics Discover how event processing differs from real-time analytics Ingest event data from Apache Kafka into Apache Pinot Combine event streams with OLTP data using Debezium and Kafka Streams Write real-time queries against event data stored in Apache Pinot Build a real-time dashboard and order tracking app Learn how Uber, Stripe, and Just Eat use real-time analytics

In "Geospatial Data Analytics on AWS," you will learn how to store, manage, and analyze geospatial data effectively using various AWS services. This book provides insight into building geospatial data lakes, leveraging AWS databases, and applying best practices to derive insights from spatial data in the cloud. What this Book will help me do Design and manage geospatial data lakes on AWS leveraging S3 and other storage solutions. Analyze geospatial data using AWS services such as Athena and Redshift. Utilize machine learning models for geospatial data processing and analytics using SageMaker. Visualize geospatial data through services like Amazon QuickSight and OpenStreetMap integration. Avoid common pitfalls when managing geospatial data in the cloud. Author(s) Scott Bateman, Janahan Gnanachandran, and Jeff DeMuth bring their extensive experience in cloud computing and geospatial analytics to this book. With backgrounds in cloud architecture, data science, and geospatial applications, they aim to make complex topics accessible. Their collaborative approach ensures readers can practically apply concepts to real-world challenges. Who is it for? This book is ideal for GIS and data professionals, including developers, analysts, and scientists. It suits readers with a basic understanding of geographical concepts but no prior AWS experience. If you're aiming to enhance your cloud-based geospatial data management and analytics skills, this is the guide for you.