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"Data warehousing."
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‘Fit-for-purpose?’ – challenges and opportunities for applications of blockchain technology in the future of healthcare
by
Clauson, Kevin A.
,
Kuo, Tsung-Ting
,
Church, George
in
Beyond Big Data to new Biomedical and Health Data Science moving to next century precision health
,
Biomedical Technology - methods
,
Biomedical Technology - organization & administration
2019
Blockchain is a shared distributed digital ledger technology that can better facilitate data management, provenance and security, and has the potential to transform healthcare. Importantly, blockchain represents a data architecture, whose application goes far beyond Bitcoin – the cryptocurrency that relies on blockchain and has popularized the technology. In the health sector, blockchain is being aggressively explored by various stakeholders to optimize business processes, lower costs, improve patient outcomes, enhance compliance, and enable better use of healthcare-related data. However, critical in assessing whether blockchain can fulfill the hype of a technology characterized as ‘revolutionary’ and ‘disruptive’, is the need to ensure that blockchain design elements consider actual healthcare needs from the diverse perspectives of consumers, patients, providers, and regulators. In addition, answering the real needs of healthcare stakeholders, blockchain approaches must also be responsive to the unique challenges faced in healthcare compared to other sectors of the economy. In this sense, ensuring that a health blockchain is ‘fit-for-purpose’ is pivotal. This concept forms the basis for this article, where we share views from a multidisciplinary group of practitioners at the forefront of blockchain conceptualization, development, and deployment.
Journal Article
European Union Regulations on Algorithmic Decision Making and a “Right to Explanation”
2017
We summarize the potential impact that the European Union's new General Data Protection Regulation will have on the routine use of machine‐learning algorithms. Slated to take effect as law across the European Union in 2018, it will place restrictions on automated individual decision making (that is, algorithms that make decisions based on user‐level predictors) that “significantly affect” users. When put into practice, the law may also effectively create a right to explanation, whereby a user can ask for an explanation of an algorithmic decision that significantly affects them. We argue that while this law may pose large challenges for industry, it highlights opportunities for computer scientists to take the lead in designing algorithms and evaluation frameworks that avoid discrimination and enable explanation.
Journal Article
Ultimate Azure Synapse Analytics
2025,2024
Unlock the full potential of Azure Synapse Analytics with Ultimate Azure Synapse Analytics, your definitive roadmap to mastering the art of data analytics in the cloud era. From the foundational concepts to advanced techniques, each chapter offers practical insights and hands-on tutorials to streamline your data workflows and drive actionable insights. Discover how Azure Synapse Analytics revolutionizes data processing and integration, empowering you to harness the vast capabilities of the Azure ecosystem. Seamlessly transition from traditional data warehousing to cutting-edge big data analytics, leveraging serverless and dedicated resources for optimal performance. Dive deep into Synapse SQL, explore advanced data engineering with Apache Spark, and delve into machine learning and DevOps practices to stay ahead in today's data-driven landscape. Whether you're seeking to optimize performance, ensure compliance, or facilitate seamless migration, this book provides the expertise needed to excel in your role. Gain valuable insights into industry best practices, enhance your data engineering skills, and drive innovation within your organization.
Hands-On Data Warehousing with Azure Data Factory
by
Cote, Christian
,
Ciaburro, Giuseppe
,
Gutzait, Michelle
in
Cloud computing
,
COMPUTERS
,
COMPUTERS / Computer Science
2018,2024
Leverage the power of Microsoft Azure Data Factory v2 to build hybrid data solutions Key Features * Combine the power of Azure Data Factory v2 and SQL Server Integration Services * Design and enhance performance and scalability of a modern ETL hybrid solution * Interact with the loaded data in data warehouse and data lake using Power BI Book Description ETL is one of the essential techniques in data processing. Given data is everywhere, ETL will always be the vital process to handle data from different sources. Hands-On Data Warehousing with Azure Data Factory starts with the basic concepts of data warehousing and ETL process. You will learn how Azure Data Factory and SSIS can be used to understand the key components of an ETL solution. You will go through different services offered by Azure that can be used by ADF and SSIS, such as Azure Data Lake Analytics, Machine Learning and Databrick's Spark with the help of practical examples. You will explore how to design and implement ETL hybrid solutions using different integration services with a step-by-step approach. Once you get to grips with all this, you will use Power BI to interact with data coming from different sources in order to reveal valuable insights. By the end of this book, you will not only learn how to build your own ETL solutions but also address the key challenges that are faced while building them. What you will learn * Understand the key components of an ETL solution using Azure Data Factory and Integration Services * Design the architecture of a modern ETL hybrid solution * Implement ETL solutions for both on-premises and Azure data * Improve the performance and scalability of your ETL solution * Gain thorough knowledge of new capabilities and features added to Azure Data Factory and Integration Services Who this book is for This book is for you if you are a software professional who develops and implements ETL solutions using Microsoft SQL Server or Azure cloud. It will be an added advantage if you are a software engineer, DW/ETL architect, or ETL developer, and know how to create a new ETL implementation or enhance an existing one with ADF or SSIS.
Data warehousing in the age of big data
2013
Data Warehousing in the Age of the Big Data will help you and your organization make the most of unstructured data with your existing data warehouse.As Big Data continues to revolutionize how we use data, it doesn't have to create more confusion.
Redis 4.x cookbook
by
Huang, Pengcheng
,
Wang, Zuofei
in
Big Data and Business Intelligence
,
COMPUTERS / Computer Science
2018,2024
Redis is a popular key-value store database used commonly across many enterprises. Based on the latest version of Redis 4.x, this book provides useful recipes to help you overcome any obstacle when it comes to the different tasks associated with Redis - from working with data types to administering and troubleshooting your Redis solution.