Airflow For Data Workflow Automation

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Airflow For Data Workflow Automation

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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-05-22

Airflow For Data Workflow Automation written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-22 with Computers categories.

"Airflow for Data Workflow Automation" "Airflow for Data Workflow Automation" is a comprehensive guide designed for data engineers, architects, and platform specialists seeking to master the orchestration of robust, maintainable, and scalable data pipelines using Apache Airflow. Starting with the foundational principles of modern data workflow automation, the book meticulously explores key architecture concepts, the compelling rationale for orchestration tools, and the core terminology and patterns that underpin Airflow-powered systems. Readers will gain clarity on Airflow’s internal mechanics and understand how to leverage its capabilities to efficiently automate common as well as advanced data workflow tasks. Delving deeper, the book provides actionable insights into authoring, maintaining, and scaling Directed Acyclic Graphs (DAGs) within Airflow environments. It covers best practices in DAG design, dynamic workflow generation, advanced scheduling techniques, and robust testing methodologies. The coverage extends to a thorough exploration of operators, sensors, and Airflow’s extensibility for custom integrations and interoperability with external systems—ensuring reliability, idempotency, and efficiency across diverse data operations. Beyond core orchestration, the book addresses essential enterprise concerns, including security, governance, and observability, with practical guidance on authentication, secrets management, compliance, monitoring, and incident response. It offers proven strategies for cloud, hybrid, and containerized deployments, in addition to advanced topics such as plugin development, UI extension, and workflow versioning. Concluding with forward-looking use cases—ranging from MLOps and streaming pipelines to meta-workflows and community-driven innovation—this book equips professionals with the expertise to harness Airflow as a cornerstone of next-generation data infrastructure.

Data Pipelines With Apache Airflow

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Author : Bas P. Harenslak
language : en
Publisher: Simon and Schuster
Release Date : 2021-04-27

Data Pipelines With Apache Airflow written by Bas P. Harenslak and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-27 with Computers categories.

For DevOps, data engineers, machine learning engineers, and sysadmins with intermediate Python skills"--Back cover.

Alteryx Workflow Automation And Data Transformation

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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-03

Alteryx Workflow Automation And Data Transformation written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-03 with Computers categories.

"Alteryx Workflow Automation and Data Transformation" "Alteryx Workflow Automation and Data Transformation" is an authoritative guide designed for data professionals seeking to architect, optimize, and govern powerful Alteryx solutions at scale. Spanning the entire Alteryx ecosystem—from core platform architecture, engine internals, and API integrations to advanced data preparation, workflow automation, and analytics integration—this comprehensive volume delivers a unified approach to building resilient and high-performance data environments. Readers are guided through configuring robust lifecycle management, managing enterprise-grade security and governance, and deploying dynamic transformation pipelines enabled by custom scripting and workflow macros. The book presents sophisticated techniques for ingesting, processing, and outputting data across varied sources, including structured, unstructured, and cloud-native environments. Detailed coverage of advanced data cleansing, parameterized workflows, error handling, and pipeline optimization positions practitioners to tackle complex transformation use cases with precision. Furthermore, the text explores intelligent automation and orchestration strategies—ranging from dependency management and distributed execution to CI/CD integration—ensuring end-to-end automation that is both scalable and reliable. Recognizing the demands of modern analytics, "Alteryx Workflow Automation and Data Transformation" delves into predictive modeling, real-time analytics, and seamless integration with leading data science toolkits. It also equips readers to build custom tools using Alteryx SDKs, develop reusable connectors, and contribute to the thriving Alteryx community. Through an emphasis on auditability, compliance, environment scalability, and emerging trends like AI-driven automation, this book serves as an essential resource for organizations pursuing agile, future-ready data transformation at enterprise scale.

Ultimate Apache Superset For Data Visualization And Analytics Leverage Apache Superset To Create Interactive Dashboards And Master Modern Business Intelligence

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Author : Bragadeesh Sundararajan
language : en
Publisher: Orange Education Pvt Limited
Release Date : 2025-04-07

Ultimate Apache Superset For Data Visualization And Analytics Leverage Apache Superset To Create Interactive Dashboards And Master Modern Business Intelligence written by Bragadeesh Sundararajan and has been published by Orange Education Pvt Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-07 with Computers categories.

Apache Superset to Master Data Visualization and Build High-Impact BI Solutions Key Features● Learn to install, configure, and use Superset to create visualizations and build interactive dashboards.● Apply your learning to real-world data scenarios and business use cases, ensuring you can immediately apply these skills in your role.● Customize Superset with custom visualizations, integrate it with modern data pipelines, and learn how to deploy it in production environments. Book DescriptionApache Superset is a powerful open-source data visualization and business intelligence platform that enables professionals to create interactive dashboards effortlessly. With its user-friendly interface and broad compatibility with various data sources, Superset helps users uncover insights and make informed, data-driven decisions in real time. Ultimate Apache Superset for Data Visualization and Analytics offers a structured, hands-on approach to mastering Apache Superset. It begins with installation and configuration, guiding you through building your first visualization and dashboard. As you progress, you’ll explore advanced features such as SQL Lab, custom visualizations, and security management. The book also covers optimizing dashboards, integrating Superset with data pipelines, and deploying it in production environments. Each chapter includes practical examples, best practices, and real-world use cases to reinforce learning. By the end, you’ll have the expertise to build high-impact, interactive dashboards and confidently deploy Apache Superset in production. Whether you're a data analyst, engineer, or business professional, this book equips you with the skills to scale and customize Superset for your organization’s needs. Don't get left behind—unlock the full potential of Apache Superset and take your data visualization to the next level! What you will learn● Set up and configure Apache Superset for data visualization and BI● Design interactive dashboards and compelling data visualizations effortlessly● Use SQL Lab to query and explore datasets with precision● Develop custom visualizations and extend Superset with plugins● Implement role-based access control (RBAC) for secure data governance● Deploy, scale, and optimize Superset for enterprise-ready BI solutions

Data Engineering For Data Driven Marketing

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Author : Balamurugan Baluswamy
language : en
Publisher: Emerald Group Publishing
Release Date : 2025-03-10

Data Engineering For Data Driven Marketing written by Balamurugan Baluswamy and has been published by Emerald Group Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-10 with Business & Economics categories.

Offering a thorough exploration of the symbiotic relationship between data engineering and modern marketing strategies, Data Engineering for Data-Driven Marketing uses a strategic lens to delve into methodologies of collecting, transforming, and storing diverse data sources.

The Data Science Toolset

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Author : Barrett Williams
language : en
Publisher: Barrett Williams
Release Date : 2025-03-01

The Data Science Toolset written by Barrett Williams and has been published by Barrett Williams this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-01 with Computers categories.

Unlock the ultimate guide to mastering the expansive world of data science with "The Data Science Toolset." Whether you're a curious beginner or a seasoned analyst, this eBook is your gateway to an arsenal of powerful tools and techniques designed to elevate your data analysis skills and transform the way you work with data. Dive into the essential aspects of data tool selection, from understanding your data requirements to conducting thorough cost-benefit analyses. Unleash the potential of Python with in-depth guidance on libraries like Pandas and NumPy, ensuring you can manipulate data with ease. Elevate your visualization game with advanced techniques using Matplotlib, Seaborn, and interactive Plotly plots. Learn to clean, wrangle, and transform data efficiently and explore R's robust ecosystem, from data manipulation and visualization with ggplot2 to sophisticated statistical modeling. Discover how SQL can be your ally in writing efficient queries and handling complex data operations. Automation awaits you as you delve into workflow tools and pipeline building with Apache Airflow and Luigi. Excel doesn't get left behind; unlock its potential with advanced functions, pivot tables, and powerful data transformation using Power Query. Venture into the world of machine learning, understanding algorithms and model deployment with practical tools like Flask and Docker. Time series analysis and NLP techniques open doors to predictive and text data analysis, while big data frameworks like Hadoop and Spark redefine what you can achieve with vast datasets. With a focus on ethics and privacy, this eBook ensures you maintain integrity and compliance throughout your data journey. Finally, sustain your growth by exploring ways to stay current in the field and expand your professional network. "The Data Science Toolset" is more than a book—it's your companion for navigating the ever-evolving landscape of data science, empowering you with the knowledge to succeed in this dynamic domain. Get ready to transform your data insights into impactful decisions.

Automated Machine Learning On Aws

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Author : Trenton Potgieter
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-04-15

Automated Machine Learning On Aws written by Trenton Potgieter and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-15 with Computers categories.

Automate the process of building, training, and deploying machine learning applications to production with AWS solutions such as SageMaker Autopilot, AutoGluon, Step Functions, Amazon Managed Workflows for Apache Airflow, and more Key FeaturesExplore the various AWS services that make automated machine learning easierRecognize the role of DevOps and MLOps methodologies in pipeline automationGet acquainted with additional AWS services such as Step Functions, MWAA, and more to overcome automation challengesBook Description AWS provides a wide range of solutions to help automate a machine learning workflow with just a few lines of code. With this practical book, you'll learn how to automate a machine learning pipeline using the various AWS services. Automated Machine Learning on AWS begins with a quick overview of what the machine learning pipeline/process looks like and highlights the typical challenges that you may face when building a pipeline. Throughout the book, you'll become well versed with various AWS solutions such as Amazon SageMaker Autopilot, AutoGluon, and AWS Step Functions to automate an end-to-end ML process with the help of hands-on examples. The book will show you how to build, monitor, and execute a CI/CD pipeline for the ML process and how the various CI/CD services within AWS can be applied to a use case with the Cloud Development Kit (CDK). You'll understand what a data-centric ML process is by working with the Amazon Managed Services for Apache Airflow and then build a managed Airflow environment. You'll also cover the key success criteria for an MLSDLC implementation and the process of creating a self-mutating CI/CD pipeline using AWS CDK from the perspective of the platform engineering team. By the end of this AWS book, you'll be able to effectively automate a complete machine learning pipeline and deploy it to production. What you will learnEmploy SageMaker Autopilot and Amazon SageMaker SDK to automate the machine learning processUnderstand how to use AutoGluon to automate complicated model building tasksUse the AWS CDK to codify the machine learning processCreate, deploy, and rebuild a CI/CD pipeline on AWSBuild an ML workflow using AWS Step Functions and the Data Science SDKLeverage the Amazon SageMaker Feature Store to automate the machine learning software development life cycle (MLSDLC)Discover how to use Amazon MWAA for a data-centric ML processWho this book is for This book is for the novice as well as experienced machine learning practitioners looking to automate the process of building, training, and deploying machine learning-based solutions into production, using both purpose-built and other AWS services. A basic understanding of the end-to-end machine learning process and concepts, Python programming, and AWS is necessary to make the most out of this book.

Data Science And Analytics With Python

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Author : Jesus Rogel-Salazar
language : en
Publisher: CRC Press
Release Date : 2025-06-03

Data Science And Analytics With Python written by Jesus Rogel-Salazar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-03 with Computers categories.

Since the first edition of “Data Science and Analytics with Python” we have witnessed an unprecedented explosion in the interest and development within the fields of Artificial Intelligence and Machine Learning. This surge has led to the widespread adoption of the book, not just among business practitioners, but also by universities as a key textbook. In response to this growth, this new edition builds upon the success of its predecessor, expanding several sections, updating the code to reflect the latest advancements in Python libraries and modules, and addressing the ever-evolving landscape of generative AI (GenAI). This updated edition ensures that the examples and exercises remain relevant by incorporating the latest features of popular libraries such as Scikit-learn, pandas, and Numpy. Additionally, new sections delve into cutting-edge topics like generative AI, reflecting the advancements and the expanding role these technologies play. This edition also addresses crucial issues of explainability, transparency, and fairness in AI. These topics have rightly gained significant attention in recent years. As AI integrates more deeply into various aspects of our lives, understanding and mitigating biases, ensuring fairness, and maintaining transparency become paramount. This book provides comprehensive coverage of these topics, offering practical insights and guidance for data scientists and analysts. Designed as a practical companion for data analysts and budding data scientists, this book assumes a working knowledge of programming and statistical modelling but aims to guide readers deeper into the wonders of data analytics and machine learning. Maintaining the book's structure, each chapter stands alone as much as possible, allowing readers to use it as a reference as well as a textbook. Whether revisiting fundamental concepts or diving into new, advanced topics, this book offers something valuable for every reader.

Efficient Data Processing With Apache Pig

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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-17

Efficient Data Processing With Apache Pig written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-17 with Computers categories.

"Efficient Data Processing with Apache Pig" Efficient Data Processing with Apache Pig is the definitive guide to mastering high-performance data transformation and pipeline design in today’s complex big data landscape. The book opens with a thorough examination of Apache Pig’s evolution, architectural foundations, and its crucial role within distributed data ecosystems. Readers gain a strategic perspective on where Pig excels compared to frameworks like MapReduce, Hive, and Spark, alongside practical guidance for deploying robust, enterprise-grade environments that prioritize scalability, multi-tenancy, and production resilience. Spanning fundamental data modeling practices, advanced Pig Latin techniques, and deep dives into resource optimization, this book is tailored for engineers, architects, and data professionals seeking practical strategies for building efficient, reliable pipelines. Each chapter balances conceptual clarity with technical depth—exploring schema evolution, advanced joins, aggregation patterns, modular scripting, and the intricacies of performance tuning. Readers also benefit from comprehensive coverage of extending Pig with custom UDFs, integrating with external data sources, and the nuances of workflow orchestration across Oozie, Airflow, and cloud-native platforms. The book moves beyond code and configuration, addressing critical considerations in security, compliance, and data governance—from authentication and encryption to auditing and lifecycle management. It concludes with actionable frameworks for migration, modernization, and hybrid architectures, coupled with future-focused discussions on AI integration, the evolving open-source ecosystem, and innovative real-world use cases at scale. Efficient Data Processing with Apache Pig is both a practical reference and an indispensable roadmap for leveraging Pig to its full potential in modern data environments.

Advanced Data Engineering With Aws Building Scalable And Reliable Data Pipelines 2025

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Author : AUTHOR :1- GAYATRI TAVVA, AUTHOR :2 - DR PRIYANKA KAUSHIK
language : en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date :

Advanced Data Engineering With Aws Building Scalable And Reliable Data Pipelines 2025 written by AUTHOR :1- GAYATRI TAVVA, AUTHOR :2 - DR PRIYANKA KAUSHIK and has been published by YASHITA PRAKASHAN PRIVATE LIMITED this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

PREFACE The exponential growth of data has redefined the way organizations operate, compete, and innovate. In today’s digital era, businesses are no longer just consumers of data but active participants in building complex, scalable ecosystems that collect, process, store, and derive value from massive data streams. Amazon Web Services (AWS), as the world’s leading cloud platform, offers a robust suite of tools and services that empower enterprises to transform raw data into actionable insights with unprecedented speed and reliability. This book, Advanced Data Engineering on AWS: Building Scalable, Secure, and Intelligent Pipelines, is designed to guide readers through the essential foundations and evolving innovations in data engineering using AWS. It systematically covers the principles and practices needed to architect high-performance data pipelines that can handle modern business demands. The journey begins with establishing the Foundations of Data Engineering in the AWS Ecosystem, helping readers understand how AWS services interplay to create a seamless environment for data management. We then explore Designing Data Pipelines for Scalability and Reliability, focusing on the architectural patterns that ensure resilience and flexibility in an unpredictable data landscape. As data sources become increasingly diverse and dynamic, mastering Data Ingestion Techniques on AWS is critical. We delve into both batch and real-time ingestion strategies, enabling efficient collection of high-velocity data. Coupled with this is Data Storage Optimization using services like S3, Redshift, and Beyond, ensuring that storage solutions align with both performance and cost-efficiency goals. Understanding ETL and ELT on AWS is pivotal for preparing data for downstream analytics and machine learning tasks. Subsequently, Real-Time Data Processing on AWS highlights how to transform and analyze data streams to deliver timely, business-critical insights. Automation becomes key as we address Data Orchestration and Workflow Automation, enabling complex pipelines to run with minimal human intervention. Ensuring trust in data requires rigorous focus on Data Quality and Governance, laying a strong foundation for secure, compliant, and high-fidelity analytics. We further extend this security narrative in Security and Compliance in AWS Data Pipelines, offering a deep dive into encryption, access controls, and regulatory alignment. No modern pipeline is complete without observability; hence, Monitoring, Logging, and Performance Tuning explores techniques to gain actionable insights into pipeline behavior, prevent failures, and optimize operations proactively. In an increasingly globalized world, Advanced Architectures: Multi-Region and Hybrid Pipelines prepares readers for designing architectures that span geographic—es and cloud environments, ensuring data availability and fault tolerance. Finally, we look ahead to Future Trends: AI/ML-Driven Data Engineering on AWS, where artificial intelligence automates data engineering tasks, adaptive pipelines become reality, and next-generation solutions redefine how businesses leverage data at scale. This book aims to serve data engineers, architects, cloud practitioners, and technical leaders who seek to not only build scalable AWS-based systems but also future-proof their architectures in an evolving technology landscape. Through a blend of foundational principles, hands-on techniques, best practices, and forward-looking insights, this book is your comprehensive guide to mastering advanced data engineering on AWS. We invite you to embark on this journey to build the data systems that will power the intelligent enterprises of tomorrow. Authors Gayatri Tavva Dr Priyanka Kaushik