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Fundamentals Of Data Observability
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Book Synopsis Fundamentals of Data Observability by : Andy Petrella
Download or read book Fundamentals of Data Observability written by Andy Petrella and published by O'Reilly Media. This book was released on 2023-09-29 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quickly detect, troubleshoot, and prevent a wide range of data issues through data observability, a set of best practices that enable data teams to gain greater visibility of data and its usage. If you're a data engineer, data architect, or machine learning engineer, or if the quality of your work depends on the quality of your data, this book shows you how to focus on the practical aspects of introducing data observability in your everyday work. Author Andy Petrella helps you build the right habits to identify and solve data issues, such as data drifts and poor quality, so you can stop their propagation in data applications, pipelines, and analytics. You'll learn ways to introduce data observability, including setting up a framework for generating and collecting all the information you need. Learn the core principles and benefits of data observability Use data observability to detect, troubleshoot, and prevent data issues Follow the book�¢??s recipes to implement observability in your data projects Use data observability to create a trustable communication framework with data consumers Learn how to educate your peers about the benefits of data observability
Book Synopsis Fundamentals of Data Observability by : Andy Petrella
Download or read book Fundamentals of Data Observability written by Andy Petrella and published by "O'Reilly Media, Inc.". This book was released on 2023-08-14 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quickly detect, troubleshoot, and prevent a wide range of data issues through data observability, a set of best practices that enables data teams to gain greater visibility of data and its usage. If you're a data engineer, data architect, or machine learning engineer who depends on the quality of your data, this book shows you how to focus on the practical aspects of introducing data observability in your everyday work. Author Andy Petrella helps you build the right habits to identify and solve data issues, such as data drifts and poor quality, so you can stop their propagation in data applications, pipelines, and analytics. You'll learn ways to introduce data observability, including setting up a framework for generating and collecting all the information you need. Learn the core principles and benefits of data observability Use data observability to detect, troubleshoot, and prevent data issues Follow the book's recipes to implement observability in your data projects Use data observability to create a trustworthy communication framework with data consumers Learn how to educate your peers about the benefits of data observability
Book Synopsis Fundamentals of Data Observability by : Andy Petrella
Download or read book Fundamentals of Data Observability written by Andy Petrella and published by "O'Reilly Media, Inc.". This book was released on 2023-08-14 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quickly detect, troubleshoot, and prevent a wide range of data issues through data observability, a set of best practices that enables data teams to gain greater visibility of data and its usage. If you're a data engineer, data architect, or machine learning engineer who depends on the quality of your data, this book shows you how to focus on the practical aspects of introducing data observability in your everyday work. Author Andy Petrella helps you build the right habits to identify and solve data issues, such as data drifts and poor quality, so you can stop their propagation in data applications, pipelines, and analytics. You'll learn ways to introduce data observability, including setting up a framework for generating and collecting all the information you need. Learn the core principles and benefits of data observability Use data observability to detect, troubleshoot, and prevent data issues Follow the book's recipes to implement observability in your data projects Use data observability to create a trustworthy communication framework with data consumers Learn how to educate your peers about the benefits of data observability
Book Synopsis Fundamentals of Data Engineering by : Joe Reis
Download or read book Fundamentals of Data Engineering written by Joe Reis and published by "O'Reilly Media, Inc.". This book was released on 2022-06-22 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle. Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers. You'll understand how to apply the concepts of data generation, ingestion, orchestration, transformation, storage, and governance that are critical in any data environment regardless of the underlying technology. This book will help you: Get a concise overview of the entire data engineering landscape Assess data engineering problems using an end-to-end framework of best practices Cut through marketing hype when choosing data technologies, architecture, and processes Use the data engineering lifecycle to design and build a robust architecture Incorporate data governance and security across the data engineering lifecycle
Book Synopsis Data Observability for Data Engineering by : Michele Pinto
Download or read book Data Observability for Data Engineering written by Michele Pinto and published by Packt Publishing Ltd. This book was released on 2023-12-29 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover actionable steps to maintain healthy data pipelines to promote data observability within your teams with this essential guide to elevating data engineering practices Key Features Learn how to monitor your data pipelines in a scalable way Apply real-life use cases and projects to gain hands-on experience in implementing data observability Instil trust in your pipelines among data producers and consumers alike Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIn the age of information, strategic management of data is critical to organizational success. The constant challenge lies in maintaining data accuracy and preventing data pipelines from breaking. Data Observability for Data Engineering is your definitive guide to implementing data observability successfully in your organization. This book unveils the power of data observability, a fusion of techniques and methods that allow you to monitor and validate the health of your data. You’ll see how it builds on data quality monitoring and understand its significance from the data engineering perspective. Once you're familiar with the techniques and elements of data observability, you'll get hands-on with a practical Python project to reinforce what you've learned. Toward the end of the book, you’ll apply your expertise to explore diverse use cases and experiment with projects to seamlessly implement data observability in your organization. Equipped with the mastery of data observability intricacies, you’ll be able to make your organization future-ready and resilient and never worry about the quality of your data pipelines again.What you will learn Implement a data observability approach to enhance the quality of data pipelines Collect and analyze key metrics through coding examples Apply monkey patching in a Python module Manage the costs and risks associated with your data pipeline Understand the main techniques for collecting observability metrics Implement monitoring techniques for analytics pipelines in production Build and maintain a statistics engine continuously Who this book is for This book is for data engineers, data architects, data analysts, and data scientists who have encountered issues with broken data pipelines or dashboards. Organizations seeking to adopt data observability practices and managers responsible for data quality and processes will find this book especially useful to increase the confidence of data consumers and raise awareness among producers regarding their data pipelines.
Book Synopsis Data Quality Fundamentals by : Barr Moses
Download or read book Data Quality Fundamentals written by Barr Moses and published by "O'Reilly Media, Inc.". This book was released on 2022-09 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: Do your product dashboards look funky? Are your quarterly reports stale? Is the data set you're using broken or just plain wrong? These problems affect almost every team, yet they're usually addressed on an ad hoc basis and in a reactive manner. If you answered yes to these questions, this book is for you. Many data engineering teams today face the "good pipelines, bad data" problem. It doesn't matter how advanced your data infrastructure is if the data you're piping is bad. In this book, Barr Moses, Lior Gavish, and Molly Vorwerck, from the data observability company Monte Carlo, explain how to tackle data quality and trust at scale by leveraging best practices and technologies used by some of the world's most innovative companies. Build more trustworthy and reliable data pipelines Write scripts to make data checks and identify broken pipelines with data observability Learn how to set and maintain data SLAs, SLIs, and SLOs Develop and lead data quality initiatives at your company Learn how to treat data services and systems with the diligence of production software Automate data lineage graphs across your data ecosystem Build anomaly detectors for your critical data assets
Download or read book Data Curious written by Carl Allchin and published by "O'Reilly Media, Inc.". This book was released on 2023-07-14 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data has been a missing part of most academic curriculums for a long time, and we're all being affected. During challenging times, creating a data-informed culture can help you pivot quickly or prevent expensive missteps. Developing a data curious organization will take advantage of the burgeoning data resources available as a result of increasing digitalization. With this book, author Carl Allchin shows today's business professionals how to become data empowered. These tech-savvy business professionals will learn data literacy fundamentals—from understanding the possibilities to asking the right questions. You'll discover how to make the right technology choices and avoid pitfalls that could put your career and company at risk. Discover what an agile, empowered, data-driven organization should look like Examine how to use data in new ways to help your business come to life Learn key terms and concepts around data management and analytics Understand the differences between spreadsheet analysis and a data analytics pipeline Get advice for working with data scientists and explore ways to mitigate the IT department's concerns
Book Synopsis Fundamentals of Analytics Engineering by : Dumky De Wilde
Download or read book Fundamentals of Analytics Engineering written by Dumky De Wilde and published by Packt Publishing Ltd. This book was released on 2024-03-29 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain a holistic understanding of the analytics engineering lifecycle by integrating principles from both data analysis and engineering Key Features Discover how analytics engineering aligns with your organization's data strategy Access insights shared by a team of seven industry experts Tackle common analytics engineering problems faced by modern businesses Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionNavigate the world of data analytics with Fundamentals of Analytics Engineering—guiding you from foundational concepts to advanced techniques of data ingestion and warehousing, data lakehouse, and data modeling. Written by a team of 7 industry experts, this book helps you to transform raw data into structured insights. You’ll discover how to clean, filter, aggregate, and reformat data, and seamlessly serve it across diverse platforms. With practical guidance, you’ll also learn how to build a simple data platform using Airbyte for ingestion, Google BigQuery for warehousing, dbt for transformations, and Tableau for visualization. From data quality and observability to fostering collaboration on codebases, you’ll find effective strategies for ensuring data integrity and driving collaborative success. As you advance, you'll become well-versed with the CI/CD principles for automated code building, testing, and deployment—laying the foundation for consistent and reliable pipelines. With invaluable insights into gathering business requirements, documenting complex business logic, and the importance of data governance, you’ll develop a holistic understanding of the analytics lifecycle. By the end of this book, you’ll be armed with the essential techniques and best practices for developing scalable analytics solutions from end to end.What you will learn Design and implement data pipelines from ingestion to serving data Explore best practices for data modeling and schema design Gain insights into the use of cloud-based analytics platforms and tools for scalable data processing Understand the principles of data governance and collaborative coding Comprehend data quality management in analytics engineering Gain practical skills in using analytics engineering tools to conquer real-world data challenges Who this book is for This book is for data engineers and data analysts considering pivoting their careers into analytics engineering. Analytics engineers who want to upskill and search for gaps in their knowledge will also find this book helpful, as will other data professionals who want to understand the value of analytics engineering in their organization's journey toward data maturity. To get the most out of this book, you should have a basic understanding of data analysis and engineering concepts such as data cleaning, visualization, ETL and data warehousing.
Book Synopsis Scaling Machine Learning with Spark by : Adi Polak
Download or read book Scaling Machine Learning with Spark written by Adi Polak and published by "O'Reilly Media, Inc.". This book was released on 2023-03-07 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to build end-to-end scalable machine learning solutions with Apache Spark. With this practical guide, author Adi Polak introduces data and ML practitioners to creative solutions that supersede today's traditional methods. You'll learn a more holistic approach that takes you beyond specific requirements and organizational goals--allowing data and ML practitioners to collaborate and understand each other better. Scaling Machine Learning with Spark examines several technologies for building end-to-end distributed ML workflows based on the Apache Spark ecosystem with Spark MLlib, MLflow, TensorFlow, and PyTorch. If you're a data scientist who works with machine learning, this book shows you when and why to use each technology. You will: Explore machine learning, including distributed computing concepts and terminology Manage the ML lifecycle with MLflow Ingest data and perform basic preprocessing with Spark Explore feature engineering, and use Spark to extract features Train a model with MLlib and build a pipeline to reproduce it Build a data system to combine the power of Spark with deep learning Get a step-by-step example of working with distributed TensorFlow Use PyTorch to scale machine learning and its internal architecture
Book Synopsis Data Quality in the Age of AI by : Andrew Jones
Download or read book Data Quality in the Age of AI written by Andrew Jones and published by Packt Publishing Ltd. This book was released on 2024-05-24 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unlock the power of data with expert insights to enhance data quality, maximizing the potential of AI, and establishing a data-centric culture Key Features Gain a profound understanding of the interplay between data quality and AI Explore strategies to improve data quality with practical implementation and real-world results Acquire the skills to measure and evaluate data quality, empowering data-driven decisions Purchase of the Kindle book includes a free PDF eBook Book DescriptionAs organizations worldwide seek to revamp their data strategies to leverage AI advancements and benefit from newfound capabilities, data quality emerges as the cornerstone for success. Without high-quality data, even the most advanced AI models falter. Enter Data Quality in the Age of AI, a detailed report that illuminates the crucial role of data quality in shaping effective data strategies. Packed with actionable insights, this report highlights the critical role of data quality in your overall data strategy. It equips teams and organizations with the knowledge and tools to thrive in the evolving AI landscape, serving as a roadmap for harnessing the power of data quality, enabling them to unlock their data's full potential, leading to improved performance, reduced costs, increased revenue, and informed strategic decisions.What you will learn Discover actionable steps to establish data quality as the foundation of your data culture Enhance data quality directly at its source with effective strategies and best practices Elevate data quality standards and enhance data literacy within your organization Identify and measure data quality within the dataset Adopt a product mindset to address data quality challenges Explore emerging architectural patterns like data mesh and data contracts Assign roles, responsibilities, and incentives for data generators Gain insights from real-world case studies Who this book is for This report is for data leaders and decision-makers, including CTOs, CIOs, CISOs, CPOs, and CEOs responsible for shaping their organization's data strategy to maximize data value, especially those interested in harnessing recent AI advancements.
Book Synopsis Flow Architectures by : James Urquhart
Download or read book Flow Architectures written by James Urquhart and published by "O'Reilly Media, Inc.". This book was released on 2021-01-06 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Software development today is embracing events and streaming data, which optimizes not only how technology interacts but also how businesses integrate with one another to meet customer needs. This phenomenon, called flow, consists of patterns and standards that determine which activity and related data is communicated between parties over the internet. This book explores critical implications of that evolution: What happens when events and data streams help you discover new activity sources to enhance existing businesses or drive new markets? What technologies and architectural patterns can position your company for opportunities enabled by flow? James Urquhart, global field CTO at VMware, guides enterprise architects, software developers, and product managers through the process. Learn the benefits of flow dynamics when businesses, governments, and other institutions integrate via events and data streams Understand the value chain for flow integration through Wardley mapping visualization and promise theory modeling Walk through basic concepts behind today's event-driven systems marketplace Learn how today's integration patterns will influence the real-time events flow in the future Explore why companies should architect and build software today to take advantage of flow in coming years
Book Synopsis 97 Things Every Data Engineer Should Know by : Tobias Macey
Download or read book 97 Things Every Data Engineer Should Know written by Tobias Macey and published by "O'Reilly Media, Inc.". This book was released on 2021-06-11 with total page 243 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges. Edited by Tobias Macey, host of the popular Data Engineering Podcast, this book presents 97 concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data. Data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will greatly benefit from the wisdom and experience of their peers. Topics include: The Importance of Data Lineage - Julien Le Dem Data Security for Data Engineers - Katharine Jarmul The Two Types of Data Engineering and Data Engineers - Jesse Anderson Six Dimensions for Picking an Analytical Data Warehouse - Gleb Mezhanskiy The End of ETL as We Know It - Paul Singman Building a Career as a Data Engineer - Vijay Kiran Modern Metadata for the Modern Data Stack - Prukalpa Sankar Your Data Tests Failed! Now What? - Sam Bail
Book Synopsis Data Pipelines Pocket Reference by : James Densmore
Download or read book Data Pipelines Pocket Reference written by James Densmore and published by O'Reilly Media. This book was released on 2021-02-10 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today's modern data stack. You'll learn common considerations and key decision points when implementing pipelines, such as batch versus streaming data ingestion and build versus buy. This book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions. You'll learn: What a data pipeline is and how it works How data is moved and processed on modern data infrastructure, including cloud platforms Common tools and products used by data engineers to build pipelines How pipelines support analytics and reporting needs Considerations for pipeline maintenance, testing, and alerting
Download or read book Data Mesh written by Zhamak Dehghani and published by "O'Reilly Media, Inc.". This book was released on 2022-03-08 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many enterprises are investing in a next-generation data lake, hoping to democratize data at scale to provide business insights and ultimately make automated intelligent decisions. In this practical book, author Zhamak Dehghani reveals that, despite the time, money, and effort poured into them, data warehouses and data lakes fail when applied at the scale and speed of today's organizations. A distributed data mesh is a better choice. Dehghani guides architects, technical leaders, and decision makers on their journey from monolithic big data architecture to a sociotechnical paradigm that draws from modern distributed architecture. A data mesh considers domains as a first-class concern, applies platform thinking to create self-serve data infrastructure, treats data as a product, and introduces a federated and computational model of data governance. This book shows you why and how. Examine the current data landscape from the perspective of business and organizational needs, environmental challenges, and existing architectures Analyze the landscape's underlying characteristics and failure modes Get a complete introduction to data mesh principles and its constituents Learn how to design a data mesh architecture Move beyond a monolithic data lake to a distributed data mesh.
Download or read book Engineering MLOps written by Emmanuel Raj and published by Packt Publishing Ltd. This book was released on 2021-04-19 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get up and running with machine learning life cycle management and implement MLOps in your organization Key FeaturesBecome well-versed with MLOps techniques to monitor the quality of machine learning models in productionExplore a monitoring framework for ML models in production and learn about end-to-end traceability for deployed modelsPerform CI/CD to automate new implementations in ML pipelinesBook Description Engineering MLps presents comprehensive insights into MLOps coupled with real-world examples in Azure to help you to write programs, train robust and scalable ML models, and build ML pipelines to train and deploy models securely in production. The book begins by familiarizing you with the MLOps workflow so you can start writing programs to train ML models. Then you'll then move on to explore options for serializing and packaging ML models post-training to deploy them to facilitate machine learning inference, model interoperability, and end-to-end model traceability. You'll learn how to build ML pipelines, continuous integration and continuous delivery (CI/CD) pipelines, and monitor pipelines to systematically build, deploy, monitor, and govern ML solutions for businesses and industries. Finally, you'll apply the knowledge you've gained to build real-world projects. By the end of this ML book, you'll have a 360-degree view of MLOps and be ready to implement MLOps in your organization. What you will learnFormulate data governance strategies and pipelines for ML training and deploymentGet to grips with implementing ML pipelines, CI/CD pipelines, and ML monitoring pipelinesDesign a robust and scalable microservice and API for test and production environmentsCurate your custom CD processes for related use cases and organizationsMonitor ML models, including monitoring data drift, model drift, and application performanceBuild and maintain automated ML systemsWho this book is for This MLOps book is for data scientists, software engineers, DevOps engineers, machine learning engineers, and business and technology leaders who want to build, deploy, and maintain ML systems in production using MLOps principles and techniques. Basic knowledge of machine learning is necessary to get started with this book.
Book Synopsis Designing Data Visualizations by : Noah Iliinsky
Download or read book Designing Data Visualizations written by Noah Iliinsky and published by "O'Reilly Media, Inc.". This book was released on 2011-09-16 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data visualization is an efficient and effective medium for communicating large amounts of information, but the design process can often seem like an unexplainable creative endeavor. This concise book aims to demystify the design process by showing you how to use a linear decision-making process to encode your information visually. Delve into different kinds of visualization, including infographics and visual art, and explore the influences at work in each one. Then learn how to apply these concepts to your design process. Learn data visualization classifications, including explanatory, exploratory, and hybrid Discover how three fundamental influences—the designer, the reader, and the data—shape what you create Learn how to describe the specific goal of your visualization and identify the supporting data Decide the spatial position of your visual entities with axes Encode the various dimensions of your data with appropriate visual properties, such as shape and color See visualization best practices and suggestions for encoding various specific data types
Book Synopsis Site Reliability Engineering by : Niall Richard Murphy
Download or read book Site Reliability Engineering written by Niall Richard Murphy and published by "O'Reilly Media, Inc.". This book was released on 2016-03-23 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: The overwhelming majority of a software system’s lifespan is spent in use, not in design or implementation. So, why does conventional wisdom insist that software engineers focus primarily on the design and development of large-scale computing systems? In this collection of essays and articles, key members of Google’s Site Reliability Team explain how and why their commitment to the entire lifecycle has enabled the company to successfully build, deploy, monitor, and maintain some of the largest software systems in the world. You’ll learn the principles and practices that enable Google engineers to make systems more scalable, reliable, and efficient—lessons directly applicable to your organization. This book is divided into four sections: Introduction—Learn what site reliability engineering is and why it differs from conventional IT industry practices Principles—Examine the patterns, behaviors, and areas of concern that influence the work of a site reliability engineer (SRE) Practices—Understand the theory and practice of an SRE’s day-to-day work: building and operating large distributed computing systems Management—Explore Google's best practices for training, communication, and meetings that your organization can use
