The "Protocol Buffers Handbook" by Clément Jean offers an in-depth exploration of Protocol Buffers (Protobuf), a powerful data serialization format. Learn everything from syntax and schema evolution to custom validations and cross-language integrations. With practical examples in Go and Python, this guide empowers you to efficiently serialize and manage structured data across platforms. What this Book will help me do Develop advanced skills in using Protocol Buffers (Protobuf) for efficient data serialization. Master the key concepts of Protobuf syntax and schema evolution for compatibility. Learn to create custom validation plugins and tailor Protobuf processes. Integrate Protobuf with multiple programming environments, including Go and Python. Automate Protobuf projects using tools like Buf and Bazel to streamline workflows. Author(s) Clément Jean is a skilled programmer and technical writer specializing in data serialization and distributed systems. With substantial experience in developing scalable microservices, he shares valuable insights into using Protocol Buffers effectively. Through this book, Clément offers a hands-on approach to Protobuf, blending theory with practical examples derived from real-world scenarios. Who is it for? This book is perfect for software engineers, system integrators, and data architects who aim to optimize data serialization and APIs, regardless of their programming language expertise. Beginners will grasp foundational Protobuf concepts, while experienced developers will extend their knowledge to advanced, practical applications. Those working with microservices and heavily data-dependent systems will find this book especially relevant.

Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success—and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering, and clearly explains how to apply the best practices from software engineering to data science. Examples are provided in Python, drawn from popular packages such as NumPy and pandas. If you want to write better data science code, this guide covers the essential topics that are often missing from introductory data science or coding classes, including how to: Understand data structures and object-oriented programming Clearly and skillfully document your code Package and share your code Integrate data science code with a larger code base Learn how to write APIs Create secure code Apply best practices to common tasks such as testing, error handling, and logging Work more effectively with software engineers Write more efficient, maintainable, and robust code in Python Put your data science projects into production And more

"Data Observability for Data Engineering" introduces you to the foundational concepts of observing and validating data pipeline health. With real-world projects and Python code examples, you'll gain hands-on experience in improving data quality and minimizing risks, enabling you to implement strategies that ensure accuracy and reliability in your data systems. What this Book will help me do Master data observability techniques to monitor and validate data pipelines effectively. Learn to collect and analyze meaningful metrics to gauge and improve data quality. Develop skills in Python programming specific to applying data concepts such as observable data state. Address scalability challenges using state-of-the-art observability frameworks and practices. Enhance your ability to manage and optimize data workflows ensuring seamless operation from start to end. Author(s) Authors Michele Pinto and Sammy El Khammal bring a wealth of experience in data engineering and observing scalable data systems. Pinto specializes in constructing robust analytics platforms while Khammal offers insights into integrating software observability into massive pipelines. Their collaborative writing style ensures readers find both practical advice and theoretical foundations. Who is it for? This book is geared toward data engineers, architects, and scientists who seek to confidently handle pipeline challenges. Whether you're addressing specific issues or wish to introduce proactive measures in your team, this guide meets the needs of those ready to leverage observability as a key practice.

In "Redis Stack for Application Modernization," you will explore how the Redis Stack extends traditional Redis capabilities, allowing you to innovate in building real-time, scalable, multi-model applications. Through practical examples and hands-on sessions, this book equips you with skills to manage, implement, and optimize data flows and database features. What this Book will help me do Learn how to use Redis Stack for handling real-time data with JSON, hash, and other document types. Discover modern techniques for performing vector similarity searches and hybrid workflows. Become proficient in integrating Redis Stack with programming languages like Java, Python, and Node.js. Gain skills to configure Redis Stack server for scalability, security, and high availability. Master RedisInsight for data visualization, analysis, and efficient database management. Author(s) Luigi Fugaro and None Ortensi are experienced software professionals with deep expertise in database systems and application architecture. They bring years of experience working with Redis and developing real-world applications. Their hands-on approach to teaching and real-world examples make this book a valuable resource for professionals in the field. Who is it for? This book is ideal for database administrators, developers, and architects looking to leverage Redis Stack for real-time multi-model applications. It requires a basic understanding of Redis and any programming language such as Python or Java. If you wish to modernize your applications and efficiently manage databases, this book is for you.

The book "Vector Search for Practitioners with Elastic" provides a comprehensive guide to leveraging vector search technology within Elastic for applications in NLP, cybersecurity, and observability. By exploring practical examples and advanced techniques, this book teaches you how to optimize and implement vector search to address complex challenges in modern data management. What this Book will help me do Gain a deep understanding of implementing vector search with Elastic. Learn techniques to optimize vector data storage and retrieval for practical applications. Understand how to apply vector search for image similarity in Elastic. Discover methods for utilizing vector search for security and observability enhancements. Develop skills to integrate modern NLP tools with vector databases and Elastic. Author(s) Bahaaldine Azarmi, with his extensive experience in Elastic and NLP technologies, brings a practitioner's insight into the world of vector search. Co-author None Vestal contributes expertise in observability and system optimization. Together, they deliver practical and actionable knowledge in a clear and approachable manner. Who is it for? This book is designed for data professionals seeking to deepen their expertise in vector search and Elastic technologies. It is ideal for individuals in observability, search technology, or cybersecurity roles. If you have foundational knowledge in machine learning models, Python, and Elastic, this book will enable you to effectively utilize vector search in your projects.

"Cracking the Data Engineering Interview" is your essential guide to mastering the data engineering interview process. This book offers practical insights and techniques to build your resume, refine your skills in Python, SQL, data modeling, and ETL, and confidently tackle over 100 mock interview questions. Gain the knowledge and confidence to land your dream role in data engineering. What this Book will help me do Craft a compelling data engineering portfolio to stand out to employers. Refresh and deepen understanding of essential topics like Python, SQL, and ETL. Master over 100 interview questions that cover both technical and behavioral aspects. Understand data engineering concepts such as data modeling, security, and CI/CD. Develop negotiation, networking, and personal branding skills crucial for job applications. Author(s) None Bryan and None Ransome are seasoned authors with a wealth of experience in data engineering and professional development. Drawing from their extensive industry backgrounds, they provide actionable strategies for aspiring data engineers. Their approachable writing style and real-world insights make complex topics accessible to readers. Who is it for? This book is ideal for aspiring data engineers looking to navigate the job application process effectively. Readers should be familiar with data engineering fundamentals, including Python, SQL, cloud data platforms, and ETL processes. It's tailored for professionals aiming to enhance their portfolios, tackle challenging interviews, and boost their chances of landing a data engineering role.

MySQL Crash Course is a fast-paced, no-nonsense introduction to relational database development. It’s filled with practical examples and expert advice that will have you up and running quickly. You’ll learn the basics of SQL, how to create a database, craft SQL queries to extract data, and work with events, procedures, and functions. You’ll see how to add constraints to tables to enforce rules about permitted data and use indexes to accelerate data retrieval. You’ll even explore how to call MySQL from PHP, Python, and Java. Three final projects will show you how to build a weather database from scratch, use triggers to prevent errors in an election database, and use views to protect sensitive data in a salary database. You’ll also learn how to: •Query database tables for specific information, order the results, comment SQL code, and deal with null values •Define table columns to hold strings, integers, and dates, and determine what data types to use •Join multiple database tables as well as use temporary tables, common table expressions, derived tables, and subqueries •Add, change, and remove data from tables, create views based on specific queries, write reusable stored routines, and automate and schedule events The perfect quick-start resource for database developers, MySQL Crash Course will arm you with the tools you need to build and manage fast, powerful, and secure MySQL-based data storage systems.

"Graph Data Science with Neo4j" teaches you how to utilize Neo4j 5 and its Graph Data Science Library 2.0 for analyzing and making predictions with graph data. By integrating graph algorithms into actionable machine learning pipelines using Python, you'll harness the power of graph-based data models. What this Book will help me do Query and manipulate graph data using Cypher in Neo4j. Design and implement graph datasets using your data and public sources. Utilize graph-specific algorithms for tasks such as link prediction. Integrate graph data science pipelines into machine learning projects. Understand and apply predictive modeling using the GDS Library. Author(s) None Scifo, the author of "Graph Data Science with Neo4j," is an experienced data scientist with expertise in graph databases and advanced machine learning techniques. Their technical approach combines practical implementation with clear, step-by-step guidance to provide readers the skills they need to excel. Who is it for? This book is ideal for data scientists and analysts familiar with basic Neo4j concepts and Python-based data science workflows who wish to deepen their skills in graph algorithms and machine learning integration. It is particularly suited for professionals aiming to advance their expertise in graph data science for practical applications.

Dive into the world of modern search systems with 'Neural Search - From Prototype to Production with Jina.' This book introduces you to the fundamentals of neural search, exploring how machine learning revolutionizes information retrieval. You'll gain hands-on experience building versatile, scalable search engines using Jina, unraveling the complexities of AI-powered searches. What this Book will help me do Understand the basics of neural search compared to traditional search methods. Develop mastery of vector representation and its application in neural search. Learn to utilize Jina for constructing AI-powered search engines. Enhance your capabilities to handle multi-modal search systems like text, images, and audio. Acquire the skills to deploy and optimize deep learning-powered search systems effectively. Author(s) Bo Wang, Cristian Mitroi, Feng Wang, Shubham Saboo, and Susana Guzmán are experienced technologists and AI researchers passionate about simplifying complex subjects like neural search. With their expertise in Jina and deep learning, their collaborative approach ensures practical, reader-friendly content that empowers learners to excel in creating cutting-edge search systems. Who is it for? This book is perfect for machine learning, AI, or Python developers eager to advance their understanding of neural search. Whether you're building text, image, or other modality-based search systems, it caters to beginners with foundational knowledge and extends to professionals wanting to deepen their skills. Unlock the potential of Jina for your projects.

Master web development with the FARM stack in this comprehensive guide. You'll learn to harness FastAPI for a secure and efficient backend, React for a dynamic frontend, and MongoDB for flexible data storage. Gain practical experience by building fully functional projects that you can deploy and fine-tune, opening doors to enhanced proficiency in modern web technologies. What this Book will help me do Build secure and performant backends using FastAPI and understand its integration with MongoDB. Develop responsive and dynamic user interfaces with React and incorporate server-side rendering for improved SEO. Explore the intricacies of deploying full-stack applications on platforms like Heroku and Netlify. Implement robust user authentication systems with JSON Web Tokens for securing your applications. Apply caching strategies with Redis to enhance the performance and scalability of applications. Author(s) Marko Aleksendrić, the author of this book, combines years of experience in software development with a passion for teaching. Specializing in full-stack web technologies, Marko has a track record of guiding developers in mastering modern tools like FastAPI and React. His practical approach focuses on equipping readers with real-world skills through projects and best practices. Who is it for? This book is ideal for developers with foundational knowledge in Python, JavaScript, and web basics who want to expand their expertise into full-stack development. Whether you're a professional seeking to enhance your project toolkit or a beginner aiming to tackle modern web applications, this guide provides a step-by-step approach tailored to your growth.

Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.10 and pandas 1.4, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the Jupyter notebook and IPython shell for exploratory computing Learn basic and advanced features in NumPy Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples

This book will guide you through mastering Delta, a robust and versatile protocol for data engineering and analytics. You'll discover how Delta simplifies data workflows, supports both batch and streaming data, and is optimized for analytics applications in various industries. By the end, you will know how to create high-performing, analytics-ready data pipelines. What this Book will help me do Understand Delta's unique offering for unifying batch and streaming data processing. Learn approaches to address data governance, reliability, and scalability challenges. Gain technical expertise in building data pipelines optimized for analytics and machine learning use. Master core concepts like data modeling, distributed computing, and Delta's schema evolution features. Develop and deploy production-grade data engineering solutions leveraging Delta for business intelligence. Author(s) Anindita Mahapatra is an experienced data engineer and author with years of expertise in working on Delta and data-driven solutions. Her hands-on approach to explaining complex data concepts makes this book an invaluable resource for professionals in data engineering and analytics. Who is it for? Ideal for data engineers, data analysts, and anyone involved in AI/BI workflows, this book suits learners with some basic knowledge of SQL and Python. Whether you're an experienced professional or looking to upgrade your skills with Delta, this book will provide practical insights and actionable knowledge.

Discover the power of in-memory data analytics with "In-Memory Analytics with Apache Arrow." This book delves into Apache Arrow's unique capabilities, enabling you to handle vast amounts of data efficiently and effectively. Learn how Arrow improves performance, offers seamless integration, and simplifies data analysis in diverse computing environments. What this Book will help me do Gain proficiency with the datastore facilities and data types defined by Apache Arrow. Master the Arrow Flight APIs to efficiently transfer data between systems. Learn to leverage in-memory processing advantages offered by Arrow for state-of-the-art analytics. Understand how Arrow interoperates with popular tools like Pandas, Parquet, and Spark. Develop and deploy high-performance data analysis pipelines with Apache Arrow. Author(s) Matthew Topol, the author of the book, is an experienced practitioner in data analytics and Apache Arrow technology. Having contributed to the development and implementation of Arrow-powered systems, he brings a wealth of knowledge to readers. His ability to delve deep into technical concepts while keeping explanations practical makes this book an excellent guide for learners of the subject. Who is it for? This book is ideal for professionals in the data domain including developers, data analysts, and data scientists aiming to enhance their data manipulation capabilities. Beginners with some familiarity with data analysis concepts will find it beneficial, as well as engineers designing analytics utilities. Programming examples accommodate users of C, Go, and Python, making it broadly accessible.

The amount of data being generated today is staggering and growing. Apache Spark has emerged as the de facto tool to analyze big data and is now a critical part of the data science toolbox. Updated for Spark 3.0, this practical guide brings together Spark, statistical methods, and real-world datasets to teach you how to approach analytics problems using PySpark, Spark's Python API, and other best practices in Spark programming. Data scientists Akash Tandon, Sandy Ryza, Uri Laserson, Sean Owen, and Josh Wills offer an introduction to the Spark ecosystem, then dive into patterns that apply common techniques-including classification, clustering, collaborative filtering, and anomaly detection, to fields such as genomics, security, and finance. This updated edition also covers NLP and image processing. If you have a basic understanding of machine learning and statistics and you program in Python, this book will get you started with large-scale data analysis. Familiarize yourself with Spark's programming model and ecosystem Learn general approaches in data science Examine complete implementations that analyze large public datasets Discover which machine learning tools make sense for particular problems Explore code that can be adapted to many uses

"Elasticsearch 8.x Cookbook" is your go-to resource for harnessing the full potential of Elasticsearch 8. This book provides over 180 hands-on recipes to help you efficiently implement, customize, and scale Elasticsearch solutions in your enterprise. Whether you're handling complex queries, analytics, or cluster management, you'll find practical insights to enhance your capabilities. What this Book will help me do Understand the advanced features of Elasticsearch 8.x, including X-Pack, for improving functionality and security. Master advanced indexing and query techniques to perform efficient and scalable data operations. Implement and manage Elasticsearch clusters effectively including monitoring performance via Kibana. Integrate Elasticsearch seamlessly into Java, Scala, Python, and big data environments. Develop custom plugins and extend Elasticsearch to meet unique project requirements. Author(s) Alberto Paro is a seasoned Elasticsearch expert with years of experience in search technologies and enterprise solution development. As a professional developer and consultant, he has worked with numerous organizations to implement Elasticsearch at scale. Alberto brings his deep technical knowledge and hands-on approach to this book, ensuring readers gain practical insights and skills. Who is it for? This book is perfect for software engineers, data professionals, and developers working with Elasticsearch in enterprise environments. If you're seeking to advance your Elasticsearch knowledge, enhance your query-writing abilities, or seek to integrate it into big data workflows, this book will be invaluable. Regardless of whether you're deploying Elasticsearch in e-commerce, applications, or for analytics, you'll find the content purposeful and engaging.

Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career. Learn how to: Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance Manipulate vectors and matrices and perform matrix decomposition Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market

Python for ArcGIS Pro is your guide to automating geospatial tasks and maximizing your productivity using Python. Inside, you'll learn how to integrate Python scripting into ArcGIS workflows to streamline map production, data analysis, and data management. What this Book will help me do Automate map production and streamline repetitive cartography tasks. Conduct geospatial data analysis using Python libraries like pandas and NumPy. Integrate ArcPy and ArcGIS API for Python to manage geospatial data more effectively. Create script tools to improve repeatability and manage datasets. Publish and manage geospatial data to ArcGIS Online seamlessly. Author(s) None Toms and None Parker are both experienced GIS professionals and Python developers. With years of hands-on experience using Esri technology in real-world scenarios, they bring practical insights into the application's nuances. Their collaborative approach allows them to demystify technical concepts, making their teachings accessible to audiences of all skill levels. Who is it for? This book is for ArcGIS users looking to integrate Python into workflows, whether you're a GIS specialist, technician, or analyst. It's also suitable for those transitioning to roles requiring programming skills. A basic understanding of ArcGIS helps, but the book starts from the fundamentals.

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.

Simplify Big Data Analytics with Amazon EMR is a thorough guide to harnessing Amazon's EMR service for big data processing and analytics. From distributed computation pipelines to real-time streaming analytics, this book provides hands-on knowledge and actionable steps for implementing data solutions efficiently. What this Book will help me do Understand the architecture and key components of Amazon EMR and how to deploy it effectively. Learn to configure and manage distributed data processing pipelines using Amazon EMR. Implement security and data governance best practices within the Amazon EMR ecosystem. Master batch ETL and real-time analytics techniques using technologies like Apache Spark. Apply optimization and cost-saving strategies to scalable data solutions. Author(s) Sakti Mishra is a seasoned data professional with extensive expertise in deploying scalable analytics solutions on cloud platforms like AWS. With a background in big data technologies and a passion for teaching, Sakti ensures practical insights accompany every concept. Readers will find his approach thorough, hands-on, and highly informative. Who is it for? This book is perfect for data engineers, data scientists, and other professionals looking to leverage Amazon EMR for scalable analytics. If you are familiar with Python, Scala, or Java and have some exposure to Hadoop or AWS ecosystems, this book will empower you to design and implement robust data pipelines efficiently.

Think big about your data! PySpark brings the powerful Spark big data processing engine to the Python ecosystem, letting you seamlessly scale up your data tasks and create lightning-fast pipelines. In Data Analysis with Python and PySpark you will learn how to: Manage your data as it scales across multiple machines Scale up your data programs with full confidence Read and write data to and from a variety of sources and formats Deal with messy data with PySpark’s data manipulation functionality Discover new data sets and perform exploratory data analysis Build automated data pipelines that transform, summarize, and get insights from data Troubleshoot common PySpark errors Creating reliable long-running jobs Data Analysis with Python and PySpark is your guide to delivering successful Python-driven data projects. Packed with relevant examples and essential techniques, this practical book teaches you to build pipelines for reporting, machine learning, and other data-centric tasks. Quick exercises in every chapter help you practice what you’ve learned, and rapidly start implementing PySpark into your data systems. No previous knowledge of Spark is required. About the Technology The Spark data processing engine is an amazing analytics factory: raw data comes in, insight comes out. PySpark wraps Spark’s core engine with a Python-based API. It helps simplify Spark’s steep learning curve and makes this powerful tool available to anyone working in the Python data ecosystem. About the Book Data Analysis with Python and PySpark helps you solve the daily challenges of data science with PySpark. You’ll learn how to scale your processing capabilities across multiple machines while ingesting data from any source—whether that’s Hadoop clusters, cloud data storage, or local data files. Once you’ve covered the fundamentals, you’ll explore the full versatility of PySpark by building machine learning pipelines, and blending Python, pandas, and PySpark code. What's Inside Organizing your PySpark code Managing your data, no matter the size Scale up your data programs with full confidence Troubleshooting common data pipeline problems Creating reliable long-running jobs About the Reader Written for data scientists and data engineers comfortable with Python. About the Author As a ML director for a data-driven software company, Jonathan Rioux uses PySpark daily. He teaches the software to data scientists, engineers, and data-savvy business analysts. Quotes A clear and in-depth introduction for truly tackling big data with Python. - Gustavo Patino, Oakland University William Beaumont School of Medicine The perfect way to learn how to analyze and master huge datasets. - Gary Bake, Brambles Covers both basic and more advanced topics of PySpark, with a good balance between theory and hands-on. - Philippe Van Bergenl, P² Consulting For beginner to pro, a well-written book to help understand PySpark. - Raushan Kumar Jha, Microsoft