Confidently orchestrate your data pipelines with Apache Airflow by applying industry best practices and scalable strategies Key Features Understand the steps for migrating from Airflow 1.x to 2.x and explore the new features and improvements in version 2.x Learn Apache Airflow workflow authoring through real-world use cases Uncover strategies to operationalize your Airflow instance and pipelines for resilient operations and high throughput Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionData professionals face the monumental task of managing complex data pipelines, orchestrating workflows across diverse systems, and ensuring scalable, reliable data processing. This definitive guide to mastering Apache Airflow, written by experts in engineering, data strategy, and problem-solving across tech, financial, and life sciences industries, is your key to overcoming these challenges. It covers everything from the basics of Airflow and its core components to advanced topics such as custom plugin development, multi-tenancy, and cloud deployment. Starting with an introduction to data orchestration and the significant updates in Apache Airflow 2.0, this book takes you through the essentials of DAG authoring, managing Airflow components, and connecting to external data sources. Through real-world use cases, you’ll gain practical insights into implementing ETL pipelines and machine learning workflows in your environment. You’ll also learn how to deploy Airflow in cloud environments, tackle operational considerations for scaling, and apply best practices for CI/CD and monitoring. By the end of this book, you’ll be proficient in operating and using Apache Airflow, authoring high-quality workflows in Python for your specific use cases, and making informed decisions crucial for production-ready implementation.What you will learn Explore the new features and improvements in Apache Airflow 2.0 Design and build data pipelines using DAGs Implement ETL pipelines, ML workflows, and other advanced use cases Develop and deploy custom plugins and UI extensions Deploy and manage Apache Airflow in cloud environments such as AWS, GCP, and Azure Describe a path for the scaling of your environment over time Apply best practices for monitoring and maintaining Airflow Who this book is for This book is for data engineers, developers, IT professionals, and data scientists who want to optimize workflow orchestration with Apache Airflow. It's perfect for those who recognize Airflow’s potential and want to avoid common implementation pitfalls. Whether you’re new to data, an experienced professional, or a manager seeking insights, this guide will support you. A functional understanding of Python, some business experience, and basic DevOps skills are helpful. While prior experience with Airflow is not required, it is beneficial.

Empower Your Data Workflow Orchestration and Automation Are you ready to embark on a journey into the world of data workflow orchestration and automation with Apache Airflow? "Mastering Apache Airflow" is your comprehensive guide to harnessing the full potential of this powerful platform for managing complex data pipelines. Whether you're a data engineer striving to optimize workflows or a business analyst aiming to streamline data processing, this book equips you with the knowledge and tools to master the art of Airflow-based workflow automation.

Become a successful machine learning professional by effortlessly deploying machine learning models to production and implementing cloud-based machine learning models for widespread organizational use Key FeaturesLearn best practices about bringing your models to productionExplore the tools available for serving ML models and the differences between themUnderstand state-of-the-art monitoring approaches for model serving implementationsBook Description Serving patterns enable data science and ML teams to bring their models to production. Most ML models are not deployed for consumers, so ML engineers need to know the critical steps for how to serve an ML model. This book will cover the whole process, from the basic concepts like stateful and stateless serving to the advantages and challenges of each. Batch, real-time, and continuous model serving techniques will also be covered in detail. Later chapters will give detailed examples of keyed prediction techniques and ensemble patterns. Valuable associated technologies like TensorFlow severing, BentoML, and RayServe will also be discussed, making sure that you have a good understanding of the most important methods and techniques in model serving. Later, you'll cover topics such as monitoring and performance optimization, as well as strategies for managing model drift and handling updates and versioning. The book will provide practical guidance and best practices for ensuring that your model serving pipeline is robust, scalable, and reliable. Additionally, this book will explore the use of cloud-based platforms and services for model serving using AWS SageMaker with the help of detailed examples. By the end of this book, you'll be able to save and serve your model using state-of-the-art techniques. What you will learnExplore specific patterns in model serving that are crucial for every data science professionalUnderstand how to serve machine learning models using different techniquesDiscover the various approaches to stateless servingImplement advanced techniques for batch and streaming model servingGet to grips with the fundamental concepts in continued model evaluationServe machine learning models using a fully managed AWS Sagemaker cloud solutionWho this book is for This book is for machine learning engineers and data scientists who want to bring their models into production. Those who are familiar with machine learning and have experience of using machine learning techniques but are looking for options and strategies to bring their models to production will find great value in this book. Working knowledge of Python programming is a must to get started.

Overcome advanced challenges in building end-to-end ML solutions by leveraging the capabilities of Amazon SageMaker for developing and integrating ML models into production Key FeaturesLearn best practices for all phases of building machine learning solutions - from data preparation to monitoring models in productionAutomate end-to-end machine learning workflows with Amazon SageMaker and related AWSDesign, architect, and operate machine learning workloads in the AWS CloudBook Description Amazon SageMaker is a fully managed AWS service that provides the ability to build, train, deploy, and monitor machine learning models. The book begins with a high-level overview of Amazon SageMaker capabilities that map to the various phases of the machine learning process to help set the right foundation. You'll learn efficient tactics to address data science challenges such as processing data at scale, data preparation, connecting to big data pipelines, identifying data bias, running A/B tests, and model explainability using Amazon SageMaker. As you advance, you'll understand how you can tackle the challenge of training at scale, including how to use large data sets while saving costs, monitoring training resources to identify bottlenecks, speeding up long training jobs, and tracking multiple models trained for a common goal. Moving ahead, you'll find out how you can integrate Amazon SageMaker with other AWS to build reliable, cost-optimized, and automated machine learning applications. In addition to this, you'll build ML pipelines integrated with MLOps principles and apply best practices to build secure and performant solutions. By the end of the book, you'll confidently be able to apply Amazon SageMaker's wide range of capabilities to the full spectrum of machine learning workflows. What you will learnPerform data bias detection with AWS Data Wrangler and SageMaker ClarifySpeed up data processing with SageMaker Feature StoreOvercome labeling bias with SageMaker Ground TruthImprove training time with the monitoring and profiling capabilities of SageMaker DebuggerAddress the challenge of model deployment automation with CI/CD using the SageMaker model registryExplore SageMaker Neo for model optimizationImplement data and model quality monitoring with Amazon Model MonitorImprove training time and reduce costs with SageMaker data and model parallelismWho this book is for This book is for expert data scientists responsible for building machine learning applications using Amazon SageMaker. Working knowledge of Amazon SageMaker, machine learning, deep learning, and experience using Jupyter Notebooks and Python is expected. Basic knowledge of AWS related to data, security, and monitoring will help you make the most of the book.

"An Airflow bible. Useful for all kinds of users, from novice to expert." - Rambabu Posa, Sai Aashika Consultancy Data Pipelines with Apache Airflow teaches you how to build and maintain effective data pipelines. A successful pipeline moves data efficiently, minimizing pauses and blockages between tasks, keeping every process along the way operational. Apache Airflow provides a single customizable environment for building and managing data pipelines, eliminating the need for a hodgepodge collection of tools, snowflake code, and homegrown processes. Using real-world scenarios and examples, Data Pipelines with Apache Airflow teaches you how to simplify and automate data pipelines, reduce operational overhead, and smoothly integrate all the technologies in your stack. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Data pipelines manage the flow of data from initial collection through consolidation, cleaning, analysis, visualization, and more. Apache Airflow provides a single platform you can use to design, implement, monitor, and maintain your pipelines. Its easy-to-use UI, plug-and-play options, and flexible Python scripting make Airflow perfect for any data management task. About the book Data Pipelines with Apache Airflow teaches you how to build and maintain effective data pipelines. You’ll explore the most common usage patterns, including aggregating multiple data sources, connecting to and from data lakes, and cloud deployment. Part reference and part tutorial, this practical guide covers every aspect of the directed acyclic graphs (DAGs) that power Airflow, and how to customize them for your pipeline’s needs. What's inside Build, test, and deploy Airflow pipelines as DAGs Automate moving and transforming data Analyze historical datasets using backfilling Develop custom components Set up Airflow in production environments About the reader For DevOps, data engineers, machine learning engineers, and sysadmins with intermediate Python skills. About the author Bas Harenslak and Julian de Ruiter are data engineers with extensive experience using Airflow to develop pipelines for major companies. Bas is also an Airflow committer. Table of Contents PART 1 - GETTING STARTED 1 Meet Apache Airflow 2 Anatomy of an Airflow DAG 3 Scheduling in Airflow 4 Templating tasks using the Airflow context 5 Defining dependencies between tasks PART 2 - BEYOND THE BASICS 6 Triggering workflows 7 Communicating with external systems 8 Building custom components 9 Testing 10 Running tasks in containers PART 3 - AIRFLOW IN PRACTICE 11 Best practices 12 Operating Airflow in production 13 Securing Airflow 14 Project: Finding the fastest way to get around NYC PART 4 - IN THE CLOUDS 15 Airflow in the clouds 16 Airflow on AWS 17 Airflow on Azure 18 Airflow in GCP

Explore modern data engineering techniques and best practices to build scalable, efficient, and future-proof data processing systems across cloud platforms Key Features Architect and engineer optimized data solutions in the cloud with best practices for performance and cost-effectiveness Explore design patterns and use cases to balance roles, technology choices, and processes for a future-proof design Learn from experts to avoid common pitfalls in data engineering projects Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionRevolutionize your approach to data processing in the fast-paced business landscape with this essential guide to data engineering. Discover the power of scalable, efficient, and secure data solutions through expert guidance on data engineering principles and techniques. Written by two industry experts with over 60 years of combined experience, it offers deep insights into best practices, architecture, agile processes, and cloud-based pipelines. You’ll start by defining the challenges data engineers face and understand how this agile and future-proof comprehensive data solution architecture addresses them. As you explore the extensive toolkit, mastering the capabilities of various instruments, you’ll gain the knowledge needed for independent research. Covering everything you need, right from data engineering fundamentals, the guide uses real-world examples to illustrate potential solutions. It elevates your skills to architect scalable data systems, implement agile development processes, and design cloud-based data pipelines. The book further equips you with the knowledge to harness serverless computing and microservices to build resilient data applications. By the end, you'll be armed with the expertise to design and deliver high-performance data engineering solutions that are not only robust, efficient, and secure but also future-ready.What you will learn Architect scalable data solutions within a well-architected framework Implement agile software development processes tailored to your organization's needs Design cloud-based data pipelines for analytics, machine learning, and AI-ready data products Optimize data engineering capabilities to ensure performance and long-term business value Apply best practices for data security, privacy, and compliance Harness serverless computing and microservices to build resilient, scalable, and trustworthy data pipelines Who this book is for If you are a data engineer, ETL developer, or big data engineer who wants to master the principles and techniques of data engineering, this book is for you. A basic understanding of data engineering concepts, ETL processes, and big data technologies is expected. This book is also for professionals who want to explore advanced data engineering practices, including scalable data solutions, agile software development, and cloud-based data processing pipelines.

Google Certification Guide - Google Professional Data Engineer Navigate the Data Landscape with Google Cloud Expertise Embark on a journey to become a Google Professional Data Engineer with this comprehensive guide. Tailored for data professionals seeking to leverage Google Cloud's powerful data solutions, this book provides a deep dive into the core concepts, practices, and tools necessary to excel in the field of data engineering. Inside, You'll Explore: Fundamentals to Advanced Data Concepts: Understand the full spectrum of Google Cloud data services, from BigQuery and Dataflow to AI and machine learning integrations. Practical Data Engineering Scenarios: Learn through hands-on examples and real-life case studies that demonstrate how to effectively implement data solutions on Google Cloud. Focused Exam Strategy: Prepare for the certification exam with detailed insights into the exam format, including key topics, study strategies, and practice questions. Current Trends and Best Practices: Stay abreast of the latest advancements in Google Cloud data technologies, ensuring your skills are up-to-date and industry-relevant. Authored by a Data Engineering Expert Written by an experienced data engineer, this guide bridges practical application with theoretical knowledge, offering a comprehensive and practical learning experience. Your Comprehensive Guide to Data Engineering Certification Whether you're an aspiring data engineer or an experienced professional looking to validate your Google Cloud skills, this book is an invaluable resource, guiding you through the nuances of data engineering on Google Cloud and preparing you for the Professional Data Engineer exam. Elevate Your Data Engineering Skills This guide is more than a certification prep book; it's a deep dive into the art of data engineering in the Google Cloud ecosystem, designed to equip you with advanced skills and knowledge for a successful career in data engineering. Begin Your Data Engineering Journey Step into the world of Google Cloud data engineering with confidence. This guide is your first step towards mastering the concepts and practices of data engineering and achieving certification as a Google Professional Data Engineer. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com