Building Python Web APIs with FastAPI. A fast-paced guide to building high-performance, robust web APIs with very little boilerplate code

Abdulazeez Abdulazeez Adeshina

RESTful web services are commonly used to create APIs for web-based applications owing to their light weight and high scalability. This book will show you how FastAPI, a high-performance web framework for building RESTful APIs in Python, allows you to build robust web APIs that are simple and intuitive and makes it easy to build quickly with very little boilerplate code.This book will help you set up a FastAPI application in no time and show you how to use FastAPI to build a REST API that receives and responds to user requests. You’ll go on to learn how to handle routing and authentication while working with databases in a FastAPI application. The book walks you through the four key areas: building and using routes for create, read, update, and delete (CRUD) operations; connecting the application to SQL and NoSQL databases; securing the application built; and deploying your application locally or to a cloud environment.By the end of this book, you’ll have developed a solid understanding of the FastAPI framework and be able to build and deploy robust REST APIs.

Building Serverless Applications with Python. Develop fast, scalable, and cost-effective web applications that are always available

Jalem Raj Rohit

Serverless architectures allow you to build and run applications and services without having to manage the infrastructure. Many companies have adopted this architecture to save cost and improve scalability. This book will help you design serverless architectures for your applications with AWS and Python.The book is divided into three modules. The first module explains the fundamentals of serverless architecture and how AWS lambda functions work. In the next module, you will learn to build, release, and deploy your application to production. You will also learn to log and test your application. In the third module, we will take you through advanced topics such as building a serverless API for your application. You will also learn to troubleshoot and monitor your app and master AWS lambda programming concepts with API references. Moving on, you will also learn how to scale up serverless applications and handle distributed serverless systems in production.By the end of the book, you will be equipped with the knowledge required to build scalable and cost-efficient Python applications with a serverless framework.

Building Serverless Python Web Services with Zappa. Build and deploy serverless applications on AWS using Zappa

Abdulwahid Abdulhaque Barguzar

Serverless applications are becoming very popular these days, not just because they save developers the trouble of managing the servers, but also because they provide several other benefits such as cutting heavy costs and improving the overall performance of the application.This book will help you build serverless applications in a quick and efficient way. We begin with an introduction to AWS and the API gateway, the environment for serverless development, and Zappa. We then look at building, testing, and deploying apps in AWS with three different frameworks--Flask, Django, and Pyramid. Setting up a custom domain along with SSL certificates and configuring them with Zappa is also covered. A few advanced Zappa settings are also covered along with securing Zappa with AWS VPC.By the end of the book you will have mastered using three frameworks to build robust and cost-efficient serverless apps in Python.

Clean Code in Python. Develop maintainable and efficient code - Second Edition

Mariano Anaya

Experienced professionals in every field face several instances of disorganization, poor readability, and testability due to unstructured code.With updated code and revised content aligned to the new features of Python 3.9, this second edition of Clean Code in Python will provide you with all the tools you need to overcome these obstacles and manage your projects successfully.The book begins by describing the basic elements of writing clean code and how it plays a key role in Python programming. You will learn about writing efficient and readable code using the Python standard library and best practices for software design.The book discusses object-oriented programming in Python and shows you how to use objects with descriptors and generators. It will also show you the design principles of software testing and how to resolve problems by implementing software design patterns in your code. In the concluding chapter, we break down a monolithic application into a microservices-based one starting from the code as the basis for a solid platform.By the end of this clean code book, you will be proficient in applying industry-approved coding practices to design clean, sustainable, and readable real-world Python code.

Clean Code in Python. Refactor your legacy code base

Mariano Anaya

Python is currently used in many different areas such as software construction, systems administration, and data processing. In all of these areas, experienced professionals can find examples of inefficiency, problems, and other perils, as a result of bad code. After reading this book, readers will understand these problems, and more importantly, how to correct them. The book begins by describing the basic elements of writing clean code and how it plays an important role in Python programming. You will learn about writing efficient and readable code using the Python standard library and best practices for software design. You will learn to implement the SOLID principles in Python and use decorators to improve your code. The book delves more deeply into object oriented programming in Python and shows you how to use objects with descriptors and generators. It will also show you the design principles of software testing and how to resolve software problems by implementing design patterns in your code. In the final chapter we break down a monolithic application to a microservice one, starting from the code as the basis for a solid platform. By the end of the book, you will be proficient in applying industry approved coding practices to design clean, sustainable and readable Python code.

Cloud Native Python. Build and deploy resilent applications on the cloud using microservices, AWS, Azure and more

Manish Sethi

Businesses today are evolving so rapidly that having their own infrastructure to support their expansion is not feasible. As a result, they have been resorting to the elasticity of the cloud to provide a platform to build and deploy their highly scalable applications. This book will be the one stop for you to learn all about building cloud-native architectures in Python. It will begin by introducing you to cloud-native architecture and will help break it down for you. Then you’ll learn how to build microservices in Python using REST APIs in an event driven approach and you will build the web layer. Next, you’ll learn about Interacting data services and building Web views with React, after which we will take a detailed look at application security and performance. Then, you’ll also learn how to Dockerize your services. And finally, you’ll learn how to deploy the application on the AWS and Azure platforms. We will end the book by discussing some concepts and techniques around troubleshooting problems that might occur with your applications after you’ve deployed them. This book will teach you how to craft applications that are built as small standard units, using all the proven best practices and avoiding the usual traps. It's a practical book: we're going to build everything using Python 3 and its amazing tooling ecosystem. The book will take you on a journey, the destination of which, is the creation of a complete Python application based on microservices over the cloud platform

Cloud Observability with Azure Monitor. A practical guide to monitoring your Azure infrastructure and applications using industry best practices

José Ángel Fernández, Manuel Lázaro Ramírez

Cloud observability is complex and costly due to the use of hybrid and multi-cloud infrastructure as well as various Azure tools, hampering IT teams’ ability to monitor and analyze issues. The authors distill their years of experience with Microsoft to share the strategic insights and practical skills needed to optimize performance, ensure reliability, and navigate the dynamic landscape of observability on Azure.You’ll get an in-depth understanding of cloud observability and Azure Monitor basics, before getting to grips with the configuration and optimization of data sources and pipelines for effective monitoring. You’ll learn about advanced data analysis techniques using metrics and the Kusto Query Language (KQL) for your logs, design proactive incident response strategies with automated alerts, and visualize reports via dashboards. Using hands-on examples and best practices, you’ll explore the integration of Azure Monitor with Azure Arc and third-party tools, such as Datadog, Elastic Stack, or Dynatrace. You’ll also implement artificial intelligence for IT Operations (AIOps) and secure monitoring for hybrid and multi-cloud environments, aligned with emerging trends.By the end of this book, you’ll be able to develop robust and cost-optimized observability solutions for monitoring your Azure infrastructure and apps using Azure Monitor.

Codeless Deep Learning with KNIME. Build, train, and deploy various deep neural network architectures using KNIME Analytics Platform

KNIME AG, Kathrin Melcher, Rosaria Silipo

KNIME Analytics Platform is an open source software used to create and design data science workflows. This book is a comprehensive guide to the KNIME GUI and KNIME deep learning integration, helping you build neural network models without writing any code. It’ll guide you in building simple and complex neural networks through practical and creative solutions for solving real-world data problems.Starting with an introduction to KNIME Analytics Platform, you’ll get an overview of simple feed-forward networks for solving simple classification problems on relatively small datasets. You’ll then move on to build, train, test, and deploy more complex networks, such as autoencoders, recurrent neural networks (RNNs), long short-term memory (LSTM), and convolutional neural networks (CNNs). In each chapter, depending on the network and use case, you’ll learn how to prepare data, encode incoming data, and apply best practices.By the end of this book, you’ll have learned how to design a variety of different neural architectures and will be able to train, test, and deploy the final network.

Coding with ChatGPT and Other LLMs. Navigate LLMs for effective coding, debugging, and AI-driven development

Dr. Vincent Austin Hall

Keeping up with the AI revolution and its application in coding can be challenging, but with guidance from AI and ML expert Dr. Vincent Hall—who holds a PhD in machine learning and has extensive experience in licensed software development—this book helps both new and experienced coders to quickly adopt best practices and stay relevant in the field.You’ll learn how to use LLMs such as ChatGPT and Bard to produce efficient, explainable, and shareable code and discover techniques to maximize the potential of LLMs. The book focuses on integrated development environments (IDEs) and provides tips to avoid pitfalls, such as bias and unexplainable code, to accelerate your coding speed. You’ll master advanced coding applications with LLMs, including refactoring, debugging, and optimization, while examining ethical considerations, biases, and legal implications. You’ll also use cutting-edge tools for code generation, architecting, description, and testing to avoid legal hassles while advancing your career.By the end of this book, you’ll be well-prepared for future innovations in AI-driven software development, with the ability to anticipate emerging LLM technologies and generate ideas that shape the future of development.

Continuous Testing, Quality, Security, and Feedback. Essential strategies and secure practices for DevOps, DevSecOps, and SRE transformations

Marc Hornbeek, Dan Wakeman

Organizations struggle to integrate and execute continuous testing, quality, security, and feedback practices into their DevOps, DevSecOps, and SRE approaches to achieve successful digital transformations. This book addresses these challenges by embedding these critical practices into your software development lifecycle.Beginning with the foundational concepts, the book progresses to practical applications, helping you understand why these practices are crucial in today’s fast-paced software development landscape. You’ll discover continuous strategies to avoid the common pitfalls and streamline the quality, security, and feedback mechanisms within software development processes. You’ll explore planning, discovery, and benchmarking through systematic engineering approaches, tailored to organizational needs. You’ll learn how to select toolchains, integrating AI/ML for resilience, and implement real-world case studies to achieve operational excellence. You’ll learn how to create strategic roadmaps, aligned with digital transformation goals, and measure outcomes recognized by DORA. You’ll explore emerging trends that are reshaping continuous practices in software development.By the end of this book, you’ll have the knowledge and skills to drive continuous improvement across the software development lifecycle.

Crafting Test-Driven Software with Python. Write test suites that scale with your applications' needs and complexity using Python and PyTest

Alessandro Molina

Test-driven development (TDD) is a set of best practices that helps developers to build more scalable software and is used to increase the robustness of software by using automatic tests. This book shows you how to apply TDD practices effectively in Python projects.You’ll begin by learning about built-in unit tests and Mocks before covering rich frameworks like PyTest and web-based libraries such as WebTest and Robot Framework, discovering how Python allows you to embrace all modern testing practices with ease. Moving on, you’ll find out how to design tests and balance them with new feature development and learn how to create a complete test suite with PyTest. The book helps you adopt a hands-on approach to implementing TDD and associated methodologies that will have you up and running and make you more productive in no time. With the help of step-by-step explanations of essential concepts and practical examples, you’ll explore automatic tests and TDD best practices and get to grips with the methodologies and tools available in Python for creating effective and robust applications.By the end of this Python book, you will be able to write reliable test suites in Python to ensure the long-term resilience of your application using the range of libraries offered by Python for testing and development.

Creating Actionable Insights Using CRM Analytics. Learn how to build insightful and actionable data analytics dashboards

Mark Tossell

CRM Analytics, formerly known as Tableau CRM and Einstein Analytics, is a powerful and versatile data analytics platform that enables organizations to extract, combine, transform, and visualize their data to create valuable business insights.Creating Actionable Insights Using CRM Analytics provides a hands-on approach to CRM Analytics implementation and associated methodologies that will have you up and running and productive in no time. The book provides you with detailed explanations of essential concepts to help you to gain confidence and become competent in using the CRM Analytics platform for data extraction, combination, transformation, visualization, and action. As you make progress, you'll understand what CRM Analytics is and where it provides business value. You'll also learn how to bring your data together in CRM Analytics, build datasets and lenses for data analysis, create effective analytics dashboards for visualization and consumption by end users, and build dashboard actions that take the user from data to insight to action with ease.By the end of this book, you'll be able to solve business problems using CRM Analytics and design, build, test, and deploy analytics dashboards efficiently.

Cryptography Algorithms. A guide to algorithms in blockchain, quantum cryptography, zero-knowledge protocols, and homomorphic encryption

Massimo Bertaccini

Cryptography Algorithms is designed to help you get up and running with modern cryptography algorithms. You'll not only explore old and modern security practices but also discover practical examples of implementing them effectively.The book starts with an overview of cryptography, exploring key concepts including popular classical symmetric and asymmetric algorithms, protocol standards, and more. You'll also cover everything from building crypto codes to breaking them. In addition to this, the book will help you to understand the difference between various types of digital signatures. As you advance, you will become well-versed with the new-age cryptography algorithms and protocols such as public and private key cryptography, zero-knowledge protocols, elliptic curves, quantum cryptography, and homomorphic encryption. Finally, you'll be able to apply the knowledge you've gained with the help of practical examples and use cases.By the end of this cryptography book, you will be well-versed with modern cryptography and be able to effectively apply it to security applications.

Cyber Minds. Insights on cybersecurity across the cloud, data, artificial intelligence, blockchain, and IoT to keep you cyber safe

Shira Rubinoff

Shira Rubinoff's Cyber Minds brings together the top authorities in cybersecurity to discuss the emergent threats that face industries, societies, militaries, and governments today.With new technology threats, rising international tensions, and state-sponsored cyber attacks, cybersecurity is more important than ever. Cyber Minds serves as a strategic briefing on cybersecurity and data safety, collecting expert insights from sector security leaders, including:General Gregory Touhill, former Federal Chief Information Security Officer of the United StatesKevin L. Jackson, CEO and Founder, GovCloudMark Lynd, Digital Business Leader, NETSYNCJoseph Steinberg, Internet Security advisor and thought leaderJim Reavis, Co-Founder and CEO, Cloud Security AllianceDr. Tom Kellerman, Chief Cybersecurity Officer for Carbon Black Inc and Vice Chair of Strategic Cyber Ventures BoardMary Ann Davidson, Chief Security Officer, OracleDr. Sally Eaves, Emergent Technology CTO, Global Strategy Advisor – Blockchain AI FinTech, Social Impact award winner, keynote speaker and authorDr. Guenther Dobrauz, Partner with PwC in Zurich and Leader of PwC Legal SwitzerlandBarmak Meftah, President, AT&T CybersecurityCleve Adams, CEO, Site 1001 (AI and big data based smart building company)Ann Johnson, Corporate Vice President – Cybersecurity Solutions Group, MicrosoftBarbara Humpton, CEO, Siemens USABusinesses and states depend on effective cybersecurity. This book will help you to arm and inform yourself on what you need to know to keep your business – or your country – safe.

Cybersecurity - Attack and Defense Strategies. Counter modern threats and employ state-of-the-art tools and techniques to protect your organization against cybercriminals - Second Edition

Yuri Diogenes, Dr. Erdal Ozkaya

Cybersecurity – Attack and Defense Strategies, Second Edition is a completely revised new edition of the bestselling book, covering the very latest security threats and defense mechanisms including a detailed overview of Cloud Security Posture Management (CSPM) and an assessment of the current threat landscape, with additional focus on new IoT threats and cryptomining.Cybersecurity starts with the basics that organizations need to know to maintain a secure posture against outside threat and design a robust cybersecurity program. It takes you into the mindset of a Threat Actor to help you better understand the motivation and the steps of performing an actual attack – the Cybersecurity kill chain. You will gain hands-on experience in implementing cybersecurity using new techniques in reconnaissance and chasing a user’s identity that will enable you to discover how a system is compromised, and identify and then exploit the vulnerabilities in your own system.This book also focuses on defense strategies to enhance the security of a system. You will also discover in-depth tools, including Azure Sentinel, to ensure there are security controls in each network layer, and how to carry out the recovery process of a compromised system.

Czysty kod w Pythonie. Twórz wydajny i łatwy w utrzymaniu kod. Wydanie II

Mariano Anaya

Popularność Pythona, ulubionego języka programistów i naukowców, stale rośnie. Jest on bowiem łatwy do nauczenia się: nawet początkujący programista może napisać działający kod. W efekcie, mimo że Python pozwala na pisanie kodu przejrzystego i prostego w konserwacji, zdarzają się przypadki kodu źle zorganizowanego, nieczytelnego i praktycznie nietestowalnego. Jedną z przyczyn tego stanu rzeczy jest tendencja niektórych programistów do pisania kodu bez czytelnej struktury. Zidentyfikowanie takich problemów i ich rozwiązywanie nie jest łatwym zadaniem. Dzięki tej książce nauczysz się korzystać z kilku narzędzi służących do zarządzania projektami napisanymi w Pythonie. Dowiesz się, czym się charakteryzuje czysty kod i jakie techniki umożliwiają tworzenie czytelnego i wydajnego kodu. Przekonasz się, że do tego celu wystarczą standardowa biblioteka Pythona i zestaw najlepszych praktyk programistycznych. Opisano tu szczegóły programowania obiektowego w Pythonie wraz z zastosowaniem deskryptorów i generatorów. Zaprezentowano również zasady testowania oprogramowania i sposoby rozwiązywania problemów poprzez implementację wzorców projektowych w kodzie. Pokazano też, jak można podzielić monolityczną aplikację na mikrousługi, by otrzymać solidną architekturę aplikacji. W książce między innymi: konfiguracja wydajnego środowiska programistycznego tworzenie zaawansowanych projektów obiektowych techniki eliminacji zdublowanego kodu i tworzenie rozbudowanych abstrakcji zastosowanie dekoratorów i deskryptorów skuteczna refaktoryzacja kodu budowa solidnej architektury opartej na czystym kodzie Pythona Czysty kod w Pythonie. Tylko taki warto pisać!

Czyszczenie danych w Pythonie. Receptury. Nowoczesne techniki i narzędzia Pythona do wykrywania i eliminacji zanieczyszczeń oraz wydobywania kluczowych cech z danych

Michael Walker

Przetwarzanie dużych ilości danych daje wiedzę, która leży u podstaw istotnych decyzji podejmowanych przez organizację. Pozwala to na uzyskiwanie znakomitych efektów: techniki wydobywania wiedzy z danych stają się coraz bardziej wyrafinowane. Podstawowym warunkiem sukcesu jest uzyskanie odpowiedniej jakości danych. Wykorzystanie niespójnych i niepełnych informacji prowadzi do podejmowania błędnych decyzji. Konsekwencją mogą być straty finansowe, stwarzanie konkretnych zagrożeń czy uszczerbek na wizerunku. A zatem oczyszczanie jest wyjątkowo ważną częścią analizy danych. Ta książka jest praktycznym zbiorem gotowych do użycia receptur, podanych tak, aby maksymalnie ułatwić proces przygotowania danych do analizy. Omówiono tu takie kwestie dotyczące danych jak importowanie, ocena ich jakości, uzupełnianie braków, porządkowanie i agregacja, a także przekształcanie. Poza zwięzłym omówieniem tych zadań zaprezentowano najskuteczniejsze techniki ich wykonywania za pomocą różnych narzędzi: Pandas, NumPy, Matplotlib czy SciPy. W ramach każdej receptury wyjaśniono skutki podjętych działań. Cennym uzupełnieniem jest zestaw funkcji i klas zdefiniowanych przez użytkownika, które służą do automatyzacji oczyszczania danych. Umożliwiają one też dostrojenie procesu do konkretnych potrzeb. W książce znajdziesz receptury, dzięki którym: wczytasz i przeanalizujesz dane z różnych źródeł uporządkujesz dane, poprawisz ich błędy i uzupełnisz braki efektywnie skorzystasz z bibliotek Pythona zastosujesz wizualizacje do analizy danych napiszesz własne funkcje i klasy do automatyzacji procesu oczyszczania danych Prawdziwą wartość mają tylko oczyszczone i spójne dane!

Dancing with Python. Learn to code with Python and Quantum Computing

Robert S. Sutor

Dancing with Python helps you learn Python and quantum computing in a practical way. It will help you explore how to work with numbers, strings, collections, iterators, and files.The book goes beyond functions and classes and teaches you to use Python and Qiskit to create gates and circuits for classical and quantum computing. Learn how quantum extends traditional techniques using the Grover Search Algorithm and the code that implements it. Dive into some advanced and widely used applications of Python and revisit strings with more sophisticated tools, such as regular expressions and basic natural language processing (NLP). The final chapters introduce you to data analysis, visualizations, and supervised and unsupervised machine learning.By the end of the book, you will be proficient in programming the latest and most powerful quantum computers, the Pythonic way.

Daniel Arbuckle's Mastering Python. Build powerful Python applications

Daniel Arbuckle

Daniel Arbuckle's Mastering Python covers the basics of operating in a Python development environment, before moving on to more advanced topics. Daniel presents you with real-world solutions to Python 3.6 and advanced-level concepts, such as reactive programming, microservices, ctypes, and Cython tools.You don't need to be familiar with the Python language to use this book, as Daniel starts with a Python primer. Throughout, Daniel highlights the major aspects of managing your Python development environment, shows you how to handle parallel computation, and helps you to master asynchronous I/O with Python 3.6 to improve performance. Finally, Daniel will teach you the secrets of metaprogramming and unit testing in Python, helping you acquire the perfect skillset to be a Python expert. Daniel will get you up to speed on everything from basic programming practices to high-end tools and techniques, things that will help set you apart as a successful Python programmer.

Data Analysis Foundations with Python. Master Data Analysis with Python: From Basics to Advanced Techniques

Cuantum Technologies LLC

Embark on a comprehensive journey through data analysis with Python. Begin with an introduction to data analysis and Python, setting a strong foundation before delving into Python programming basics. Learn to set up your data analysis environment, ensuring you have the necessary tools and libraries at your fingertips. As you progress, gain proficiency in NumPy for numerical operations and Pandas for data manipulation, mastering the skills to handle and transform data efficiently.Proceed to data visualization with Matplotlib and Seaborn, where you'll create insightful visualizations to uncover patterns and trends. Understand the core principles of exploratory data analysis (EDA) and data preprocessing, preparing your data for robust analysis. Explore probability theory and hypothesis testing to make data-driven conclusions and get introduced to the fundamentals of machine learning. Delve into supervised and unsupervised learning techniques, laying the groundwork for predictive modeling.To solidify your knowledge, engage with two practical case studies: sales data analysis and social media sentiment analysis. These real-world applications will demonstrate best practices and provide valuable tips for your data analysis projects.

Data Analysis with Python. A Modern Approach

David Taieb

Data Analysis with Python offers a modern approach to data analysis so that you can work with the latest and most powerful Python tools, AI techniques, and open source libraries. Industry expert David Taieb shows you how to bridge data science with the power of programming and algorithms in Python. You'll be working with complex algorithms, and cutting-edge AI in your data analysis. Learn how to analyze data with hands-on examples using Python-based tools and Jupyter Notebook. You'll find the right balance of theory and practice, with extensive code files that you can integrate right into your own data projects.Explore the power of this approach to data analysis by then working with it across key industry case studies. Four fascinating and full projects connect you to the most critical data analysis challenges you’re likely to meet in today. The first of these is an image recognition application with TensorFlow – embracing the importance today of AI in your data analysis. The second industry project analyses social media trends, exploring big data issues and AI approaches to natural language processing. The third case study is a financial portfolio analysis application that engages you with time series analysis - pivotal to many data science applications today. The fourth industry use case dives you into graph algorithms and the power of programming in modern data science. You'll wrap up with a thoughtful look at the future of data science and how it will harness the power of algorithms and artificial intelligence.

Data Engineering with AWS Cookbook. A recipe-based approach to help you tackle data engineering problems with AWS services

Trâm Ngoc Pham, Gonzalo Herreros González, Viquar Khan, Huda Nofal

Performing data engineering with Amazon Web Services (AWS) combines AWS's scalable infrastructure with robust data processing tools, enabling efficient data pipelines and analytics workflows. This comprehensive guide to AWS data engineering will teach you all you need to know about data lake management, pipeline orchestration, and serving layer construction.Through clear explanations and hands-on exercises, you’ll master essential AWS services such as Glue, EMR, Redshift, QuickSight, and Athena. Additionally, you’ll explore various data platform topics such as data governance, data quality, DevOps, CI/CD, planning and performing data migration, and creating Infrastructure as Code. As you progress, you will gain insights into how to enrich your platform and use various AWS cloud services such as AWS EventBridge, AWS DataZone, and AWS SCT and DMS to solve data platform challenges.Each recipe in this book is tailored to a daily challenge that a data engineer team faces while building a cloud platform. By the end of this book, you will be well-versed in AWS data engineering and have gained proficiency in key AWS services and data processing techniques. You will develop the necessary skills to tackle large-scale data challenges with confidence.

Data Engineering with AWS. Learn how to design and build cloud-based data transformation pipelines using AWS

Gareth Eagar

Written by a Senior Data Architect with over twenty-five years of experience in the business, Data Engineering for AWS is a book whose sole aim is to make you proficient in using the AWS ecosystem. Using a thorough and hands-on approach to data, this book will give aspiring and new data engineers a solid theoretical and practical foundation to succeed with AWS.As you progress, you’ll be taken through the services and the skills you need to architect and implement data pipelines on AWS. You'll begin by reviewing important data engineering concepts and some of the core AWS services that form a part of the data engineer's toolkit. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how the transformed data is used by various data consumers. You’ll also learn about populating data marts and data warehouses along with how a data lakehouse fits into the picture. Later, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. In the final chapters, you'll understand how the power of machine learning and artificial intelligence can be used to draw new insights from data.By the end of this AWS book, you'll be able to carry out data engineering tasks and implement a data pipeline on AWS independently.

Data Engineering with Google Cloud Platform. A practical guide to operationalizing scalable data analytics systems on GCP

Adi Wijaya

With this book, you'll understand how the highly scalable Google Cloud Platform (GCP) enables data engineers to create end-to-end data pipelines right from storing and processing data and workflow orchestration to presenting data through visualization dashboards.Starting with a quick overview of the fundamental concepts of data engineering, you'll learn the various responsibilities of a data engineer and how GCP plays a vital role in fulfilling those responsibilities. As you progress through the chapters, you'll be able to leverage GCP products to build a sample data warehouse using Cloud Storage and BigQuery and a data lake using Dataproc. The book gradually takes you through operations such as data ingestion, data cleansing, transformation, and integrating data with other sources. You'll learn how to design IAM for data governance, deploy ML pipelines with the Vertex AI, leverage pre-built GCP models as a service, and visualize data with Google Data Studio to build compelling reports. Finally, you'll find tips on how to boost your career as a data engineer, take the Professional Data Engineer certification exam, and get ready to become an expert in data engineering with GCP.By the end of this data engineering book, you'll have developed the skills to perform core data engineering tasks and build efficient ETL data pipelines with GCP.