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Author: Jeremy Stanley Publisher: "O'Reilly Media, Inc." ISBN: 1098145895 Category : Languages : en Pages : 226
Book Description
The world's businesses ingest a combined 2.5 quintillion bytes of data every day. But how much of this vast amount of data--used to build products, power AI systems, and drive business decisions--is poor quality or just plain bad? This practical book shows you how to ensure that the data your organization relies on contains only high-quality records. Most data engineers, data analysts, and data scientists genuinely care about data quality, but they often don't have the time, resources, or understanding to create a data quality monitoring solution that succeeds at scale. In this book, Jeremy Stanley and Paige Schwartz from Anomalo explain how you can use automated data quality monitoring to cover all your tables efficiently, proactively alert on every category of issue, and resolve problems immediately. This book will help you: Learn why data quality is a business imperative Understand and assess unsupervised learning models for detecting data issues Implement notifications that reduce alert fatigue and let you triage and resolve issues quickly Integrate automated data quality monitoring with data catalogs, orchestration layers, and BI and ML systems Understand the limits of automated data quality monitoring and how to overcome them Learn how to deploy and manage your monitoring solution at scale Maintain automated data quality monitoring for the long term
Author: Jeremy Stanley Publisher: "O'Reilly Media, Inc." ISBN: 1098145909 Category : Computers Languages : en Pages : 220
Book Description
The world's businesses ingest a combined 2.5 quintillion bytes of data every day. But how much of this vast amount of data--used to build products, power AI systems, and drive business decisions--is poor quality or just plain bad? This practical book shows you how to ensure that the data your organization relies on contains only high-quality records. Most data engineers, data analysts, and data scientists genuinely care about data quality, but they often don't have the time, resources, or understanding to create a data quality monitoring solution that succeeds at scale. In this book, Jeremy Stanley and Paige Schwartz from Anomalo explain how you can use automated data quality monitoring to cover all your tables efficiently, proactively alert on every category of issue, and resolve problems immediately. This book will help you: Learn why data quality is a business imperative Understand and assess unsupervised learning models for detecting data issues Implement notifications that reduce alert fatigue and let you triage and resolve issues quickly Integrate automated data quality monitoring with data catalogs, orchestration layers, and BI and ML systems Understand the limits of automated data quality monitoring and how to overcome them Learn how to deploy and manage your monitoring solution at scale Maintain automated data quality monitoring for the long term
Author: Jeremy Stanley Publisher: ISBN: 9781098145934 Category : Languages : en Pages : 0
Book Description
The world's businesses ingest a combined 2.5 quintillion bytes of data every day. But how much of this vast amount of data--used to build products, power AI systems, and drive business decisions--is poor quality or just plain bad? This practical book shows you how to ensure that the data your organization relies on contains only high-quality records. Most data engineers, data analysts, and data scientists genuinely care about data quality, but they often don't have the time, resources, or understanding to create a data quality monitoring solution that succeeds at scale. In this book, Jeremy Stanley and Paige Schwartz from Anomalo explain how you can use automated data quality monitoring to cover all your tables efficiently, proactively alert on every category of issue, and resolve problems immediately. This book will help you: Learn why data quality is a business imperative Understand and assess unsupervised learning models for detecting data issues Implement notifications that reduce alert fatigue and let you triage and resolve issues quickly Integrate automated data quality monitoring with data catalogs, orchestration layers, and BI and ML systems Understand the limits of automated data quality monitoring and how to overcome them Learn how to deploy and manage your monitoring solution at scale Maintain automated data quality monitoring for the long term
Author: Alfredo Cuzzocrea Publisher: Springer Nature ISBN: 3031378903 Category : Computers Languages : en Pages : 256
Book Description
This book constitutes the refereed post-proceedings of the 10th International Conference and 11th International Conference on Data Management Technologies and Applications, DATA 2021 and DATA 2022, was held virtually due to the COVID-19 crisis on July 6–8, 2021 and in Lisbon, Portugal on July 11-13, 2022. The 11 full papers included in this book were carefully reviewed and selected from 148 submissions. They were organized in topical sections as follows: engineers and practitioners interested on databases, big data, data mining, data management, data security and other aspects of information systems and technology involving advanced applications of data.
Author: Sven Hartmann Publisher: Springer Nature ISBN: 3030590038 Category : Computers Languages : en Pages : 469
Book Description
The double volumes LNCS 12391-12392 constitutes the papers of the 31st International Conference on Database and Expert Systems Applications, DEXA 2020, which will be held online in September 2020. The 38 full papers presented together with 20 short papers plus 1 keynote papers in these volumes were carefully reviewed and selected from a total of 190 submissions.
Author: Brij Kishore Pandey Publisher: Packt Publishing Ltd ISBN: 1804615536 Category : Computers Languages : en Pages : 246
Book Description
Develop production-ready ETL pipelines by leveraging Python libraries and deploying them for suitable use cases Key Features Understand how to set up a Python virtual environment with PyCharm Learn functional and object-oriented approaches to create ETL pipelines Create robust CI/CD processes for ETL pipelines Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionModern extract, transform, and load (ETL) pipelines for data engineering have favored the Python language for its broad range of uses and a large assortment of tools, applications, and open source components. With its simplicity and extensive library support, Python has emerged as the undisputed choice for data processing. In this book, you’ll walk through the end-to-end process of ETL data pipeline development, starting with an introduction to the fundamentals of data pipelines and establishing a Python development environment to create pipelines. Once you've explored the ETL pipeline design principles and ET development process, you'll be equipped to design custom ETL pipelines. Next, you'll get to grips with the steps in the ETL process, which involves extracting valuable data; performing transformations, through cleaning, manipulation, and ensuring data integrity; and ultimately loading the processed data into storage systems. You’ll also review several ETL modules in Python, comparing their pros and cons when building data pipelines and leveraging cloud tools, such as AWS, to create scalable data pipelines. Lastly, you’ll learn about the concept of test-driven development for ETL pipelines to ensure safe deployments. By the end of this book, you’ll have worked on several hands-on examples to create high-performance ETL pipelines to develop robust, scalable, and resilient environments using Python.What you will learn Explore the available libraries and tools to create ETL pipelines using Python Write clean and resilient ETL code in Python that can be extended and easily scaled Understand the best practices and design principles for creating ETL pipelines Orchestrate the ETL process and scale the ETL pipeline effectively Discover tools and services available in AWS for ETL pipelines Understand different testing strategies and implement them with the ETL process Who this book is for If you are a data engineer or software professional looking to create enterprise-level ETL pipelines using Python, this book is for you. Fundamental knowledge of Python is a prerequisite.
Author: Laure Berti-Equille Publisher: John Wiley & Sons ISBN: 9781848215702 Category : Computers Languages : en Pages : 0
Book Description
This is the first book to be published on the topic of data quality exploration, analytics and quantitative data cleaning. The author provides a sound technical grounding in the subject and shows readers, through examples and practical case studies, how to apply statistics and data mining techniques to their own data quality issues. An overview of data quality analytics and techniques for data quality improvement is provided, and the author also present an iterative framework for the detection, explanation and quantitative cleaning of data quality problems and anomalies. The book then goes on to describe the methods for data quality measuring, monitoring and improvement and explains how readers can identify the best strategies for cleaning their data and for automating the process of data quality exploration and remediation.
Author: Gabriele Kotsis Publisher: Springer Nature ISBN: 3031143434 Category : Computers Languages : en Pages : 441
Book Description
This volume constitutes the refereed proceedings of the workshops held at the 33rd International Conference on Database and Expert Systems Applications, DEXA 2022, held in Vienna, Austria, in August 2022: The 6th International Workshop on Cyber-Security and Functional Safety in Cyber-Physical Systems (IWCFS 2022); 4th International Workshop on Machine Learning and Knowledge Graphs (MLKgraphs 2022); 2nd International Workshop on Time Ordered Data (ProTime2022); 2nd International Workshop on AI System Engineering: Math, Modelling and Software (AISys2022); 1st International Workshop on Distributed Ledgers and Related Technologies (DLRT2022); 1st International Workshop on Applied Research, Technology Transfer and Knowledge Exchange in Software and Data Science (ARTE2022). The 40 papers were thoroughly reviewed and selected from 62 submissions, and discuss a range of topics including: knowledge discovery, biological data, cyber security, cyber-physical system, machine learning, knowledge graphs, information retriever, data base, and artificial intelligence.
Author: David Loshin Publisher: Elsevier ISBN: 0080920349 Category : Computers Languages : en Pages : 423
Book Description
The Practitioner's Guide to Data Quality Improvement offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. It shares the fundamentals for understanding the impacts of poor data quality, and guides practitioners and managers alike in socializing, gaining sponsorship for, planning, and establishing a data quality program. It demonstrates how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. It includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning. This book is recommended for data management practitioners, including database analysts, information analysts, data administrators, data architects, enterprise architects, data warehouse engineers, and systems analysts, and their managers. Offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. Shows how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. Includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning.
Author: J.D. Tannock Publisher: Springer Science & Business Media ISBN: 9401123667 Category : Business & Economics Languages : en Pages : 243
Book Description
Quality is a topical issue in manufacturing. Competitive quality performance still eludes many manufacturers in the traditional industrialized countries. A lack of quality competitiveness is one of the root causes of the relative industrial decline and consequent trade imbalances which plague some Western economies. Many explanations are advanced for poor quality performance. Inadequate levels of investment in advanced technology, together with insufficient education and training of the workforce, are perhaps the most prominent. Some believe these problems are caused by a lack of awareness and commitment from top management, while others point to differences between industrial cultures. The established remedy is known as Total Quality Management (TQM). TQM requires a corporate culture change, driven from the top, and involving every employee in a process of never-ending quality improvement aimed at internal as well as external customers. The techniques deployed to achieve TQM include measures to improve motivation, training in problem-solving and statistical process control (SPC). Quality is, however, only one of the competitive pressures placed It is also upon the manufacturer by the modem global economy. imperative to remain economical and efficient, while increasing the flexibility and responsiveness of the design and manufacturing functions. Here the reduction or elimination of stock is of great importance, particularly as financial interest rates in the less successful manufacturing nations are frequently high. Product life cycles must become ever more compressed in response to the phenomenal design to-manufacture performance of some Pacific rim economies.
Author: Jeremy Stanley
Author: Jeremy Stanley
Author: Jeremy Stanley
Author: Alfredo Cuzzocrea
Author: Sven Hartmann
Author: Brij Kishore Pandey
Author: Matthias Galster
Author: Laure Berti-Equille
Author: Gabriele Kotsis
Author: David Loshin
Author: J.D. Tannock