Mastering Spring Boot 3.0. A comprehensive guide to building scalable and efficient backend systems with Java and Spring

Ahmet Meric

Mastering Spring Boot 3.0 is your gateway to building scalable and robust backend systems using the latest techniques. Penned by a seasoned software developer with 20+ years of experience in the tech industry, this book follows a hands-on, step-by-step approach to helping you understand Spring Boot concepts and apply them to real-world projects.You’ll start by exploring key architectural patterns such as DDD, CQRS, and event sourcing. Next, you’ll focus on the nuances of reactive REST development, delve into advanced testing strategies, and fortify your applications' security. You’ll also discover the power of containerization and orchestration with Spring Boot 3.0 and unlock its potential for smooth deployments. Additionally, by integrating Kafka, you’ll be able to build robust event-driven systems.By the end of this book, you’ll have become proficient in architectural patterns, testing strategies, and application security. Whether you’re an architect, backend developer, or DevOps engineer, this book will help you leverage the advanced features of Spring Boot 3.0 for secure and efficient backend development.

Node.js High Performance. Take your application to the next level of high performance using the extensive capabilities of Node.js

Diogo Resende

Data Exploration and Preparation with BigQuery. A practical guide to cleaning, transforming, and analyzing data for business insights

Mike Kahn

Data professionals encounter a multitude of challenges such as handling large volumes of data, dealing with data silos, and the lack of appropriate tools. Datasets often arrive in different conditions and formats, demanding considerable time from analysts, engineers, and scientists to process and uncover insights. The complexity of the data life cycle often hinders teams and organizations from extracting the desired value from their data assets. Data Exploration and Preparation with BigQuery offers a holistic solution to these challenges.The book begins with the basics of BigQuery while covering the fundamentals of data exploration and preparation. It then progresses to demonstrate how to use BigQuery for these tasks and explores the array of big data tools at your disposal within the Google Cloud ecosystem.The book doesn’t merely offer theoretical insights; it’s a hands-on companion that walks you through properly structuring your tables for query efficiency and ensures adherence to data preparation best practices. You’ll also learn when to use Dataflow, BigQuery, and Dataprep for ETL and ELT workflows. The book will skillfully guide you through various case studies, demonstrating how BigQuery can be used to solve real-world data problems.By the end of this book, you’ll have mastered the use of SQL to explore and prepare datasets in BigQuery, unlocking deeper insights from data.

Before Machine Learning Volume 1 - Linear Algebra for A.I. The Fundamental Mathematics for Data Science and Artificial Intelligence

Jorge Brasil

In this book, you'll embark on a comprehensive journey through the fundamentals of linear algebra, a critical component for any aspiring machine learning expert. Starting with an introductory overview, the course explains why linear algebra is indispensable for machine learning, setting the stage for deeper exploration. You'll then dive into the concepts of vectors and matrices, understanding their definitions, properties, and practical applications in the field.As you progress, the course takes a closer look at matrix decomposition, breaking down complex matrices into simpler, more manageable forms. This section emphasizes the importance of decomposition techniques in simplifying computations and enhancing data analysis. The final chapter focuses on principal component analysis, a powerful technique for dimensionality reduction that is widely used in machine learning and data science. By the end of the course, you will have a solid grasp of how PCA can be applied to streamline data and improve model performance.This course is designed to provide technical professionals with a thorough understanding of linear algebra's role in machine learning. By the end, you'll be well-equipped with the knowledge and skills needed to apply linear algebra in practical machine learning scenarios.

Data Engineering with AWS. Acquire the skills to design and build AWS-based data transformation pipelines like a pro - Second Edition

Gareth Eagar

This book, authored by a seasoned Senior Data Architect with 25 years of experience, aims to help you achieve proficiency in using the AWS ecosystem for data engineering. This revised edition provides updates in every chapter to cover the latest AWS services and features, takes a refreshed look at data governance, and includes a brand-new section on building modern data platforms which covers; implementing a data mesh approach, open-table formats (such as Apache Iceberg), and using DataOps for automation and observability.You'll begin by reviewing the key concepts and essential AWS tools in a data engineer's toolkit and getting acquainted with modern data management approaches. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how that transformed data is used by various data consumers. You’ll learn how to ensure strong data governance, and about populating data marts and data warehouses along with how a data lakehouse fits into the picture. After that, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. Then, you'll explore how the power of machine learning and artificial intelligence can be used to draw new insights from data. In the final chapters, you'll discover transactional data lakes, data meshes, and how to build a cutting-edge data platform on AWS.By the end of this AWS book, you'll be able to execute data engineering tasks and implement a data pipeline on AWS like a pro!

Data-Centric Machine Learning with Python. The ultimate guide to engineering and deploying high-quality models based on good data

Jonas Christensen, Nakul Bajaj, Manmohan Gosada, Kirk D. Borne

In the rapidly advancing data-driven world where data quality is pivotal to the success of machine learning and artificial intelligence projects, this critically timed guide provides a rare, end-to-end overview of data-centric machine learning (DCML), along with hands-on applications of technical and non-technical approaches to generating deeper and more accurate datasets.This book will help you understand what data-centric ML/AI is and how it can help you to realize the potential of ‘small data’. Delving into the building blocks of data-centric ML/AI, you’ll explore the human aspects of data labeling, tackle ambiguity in labeling, and understand the role of synthetic data. From strategies to improve data collection to techniques for refining and augmenting datasets, you’ll learn everything you need to elevate your data-centric practices. Through applied examples and insights for overcoming challenges, you’ll get a roadmap for implementing data-centric ML/AI in diverse applications in Python.By the end of this book, you’ll have developed a profound understanding of data-centric ML/AI and the proficiency to seamlessly integrate common data-centric approaches in the model development lifecycle to unlock the full potential of your machine learning projects by prioritizing data quality and reliability.

XGBoost for Regression Predictive Modeling and Time Series Analysis. Learn how to build, evaluate, and deploy predictive models with expert guidance

Partha Pritam Deka, Joyce Weiner, Prof. Roberto V. Zicari

XGBoost offers a powerful solution for regression and time series analysis, enabling you to build accurate and efficient predictive models. In this book, the authors draw on their combined experience of 40+ years in the semiconductor industry to help you harness the full potential of XGBoost, from understanding its core concepts to implementing real-world applications.As you progress, you'll get to grips with the XGBoost algorithm, including its mathematical underpinnings and its advantages over other ensemble methods. You'll learn when to choose XGBoost over other predictive modeling techniques, and get hands-on guidance on implementing XGBoost using both the Python API and scikit-learn API. You'll also get to grips with essential techniques for time series data, including feature engineering, handling lag features, encoding techniques, and evaluating model performance. A unique aspect of this book is the chapter on model interpretability, where you'll use tools such as SHAP, LIME, ELI5, and Partial Dependence Plots (PDP) to understand your XGBoost models. Throughout the book, you’ll work through several hands-on exercises and real-world datasets.By the end of this book, you'll not only be building accurate models but will also be able to deploy and maintain them effectively, ensuring your solutions deliver real-world impact.

Docker Deep Dive. Zero to Docker in a Single Book - Second Edition

Nigel Poulton

Most applications, even the funky cloud-native microservices ones, need high-performance, production-grade infrastructure to run on. Having impeccable knowledge of Docker will help you thrive in the modern cloud-first world. With this book, you will gain the skills you need in order to work with Docker and its containers.The book begins with an introduction to containers and explains their functionality and application in the real world. You will then get an overview of VMware, Kubernetes, and Docker and learn to install Docker on Windows, Mac, and Linux. Once you have understood the Ops and Dev perspective of Docker, you will be able to see the big picture and understand what Docker exactly does. The book then turns its attention to the more technical aspects, guiding you through practical exercises covering Docker engine, Docker images, and Docker containers. You will learn techniques for containerizing an app, deploying apps with Docker Compose, and managing cloud-native applications with Swarm. You will also build Docker networks and Docker overlay networks and handle applications that write persistent data. Finally, you will deploy apps with Docker stacks and secure your Docker environment.By the end of this book, you will be well-versed in Docker and containers and have developed the skills to create, deploy, and run applications on the cloud.