INTERNET OF MEDICAL THINGS (IOMT) Providing an essential addition to the reference material available in the field of IoMT, this timely publication covers a range of applied research on healthcare, biomedical data mining, and the security and privacy of health records. With their ability to collect, analyze and transmit health data, IoMT tools are rapidly changing healthcare delivery. For patients and clinicians, these applications are playing a central part in tracking and preventing chronic illnesses — and they are poised to evolve the future of care. In this book, the authors explore the potential applications of a wave of sensor-based tools—including wearables and stand-alone devices for remote patient monitoring—and the marriage of internet-connected medical devices with patient information that ultimately sets the IoMT ecosystem apart. This book demonstrates the connectivity between medical devices and sensors is streamlining clinical workflow management and leading to an overall improvement in patient care, both inside care facilities and in remote locations.

A friendly, framework-agnostic tutorial that will help you grok how streaming systems work—and how to build your own! In Grokking Streaming Systems you will learn how to: Implement and troubleshoot streaming systems Design streaming systems for complex functionalities Assess parallelization requirements Spot networking bottlenecks and resolve back pressure Group data for high-performance systems Handle delayed events in real-time systems Grokking Streaming Systems is a simple guide to the complex concepts behind streaming systems. This friendly and framework-agnostic tutorial teaches you how to handle real-time events, and even design and build your own streaming job that’s a perfect fit for your needs. Each new idea is carefully explained with diagrams, clear examples, and fun dialogue between perplexed personalities! About the Technology Streaming systems minimize the time between receiving and processing event data, so they can deliver responses in real time. For applications in finance, security, and IoT where milliseconds matter, streaming systems are a requirement. And streaming is hot! Skills on platforms like Spark, Heron, and Kafka are in high demand. About the Book Grokking Streaming Systems introduces real-time event streaming applications in clear, reader-friendly language. This engaging book illuminates core concepts like data parallelization, event windows, and backpressure without getting bogged down in framework-specific details. As you go, you’ll build your own simple streaming tool from the ground up to make sure all the ideas and techniques stick. The helpful and entertaining illustrations make streaming systems come alive as you tackle relevant examples like real-time credit card fraud detection and monitoring IoT services. What's Inside Implement and troubleshoot streaming systems Design streaming systems for complex functionalities Spot networking bottlenecks and resolve backpressure Group data for high-performance systems About the Reader No prior experience with streaming systems is assumed. Examples in Java. About the Authors Josh Fischer and Ning Wang are Apache Committers, and part of the committee for the Apache Heron distributed stream processing engine. Quotes Very well-written and enjoyable. I recommend this book to all software engineers working on data processing. - Apoorv Gupta, Facebook Finally, a much-needed introduction to streaming systems—a must-read for anyone interested in this technology. - Anupam Sengupta, Red Hat Tackles complex topics in a very approachable manner. - Marc Roulleau, GIRO A superb resource for helping you grasp the fundamentals of open-source streaming systems. - Simon Verhoeven, Cronos Explains all the main streaming concepts in a friendly way. Start with this one! - Cicero Zandona, Calypso Technologies

Create stunning and complex visualizations using the amazing Charticulator custom visuals in Power BI. Charticulator offers users immense power to generate visuals and graphics. To a beginner, there are myriad settings and options that can be combined in what feels like an unlimited number of combinations, giving it the unfair label, “the DAX of the charting world”. This is not true. This book is your start-to-finish guide to using Charticulator, a custom visualization software that Microsoft integrated into Power BI Desktop so that Power BI users can create incredibly powerful, customized charts and graphs. You will learn the concepts that underpin the software, journeying through every building block of chart design, enabling you to combine these parts to create spectacular visuals that represent the story of your data. Unlike other custom Power BI visuals, Charticulator runs in a separate application window within Power BI with its own interface and requires a different set of interactions and associated knowledge. This book covers the ins and outs of all of them. What You Will Learn Generate inspirational and technically competent visuals with no programming or other specialist technical knowledge Create charts that are not restricted to conventional chart types such as bar, line, or pie Limit the use of diverse Power BI custom visuals to one Charticulator custom visual Alleviate frustrations with the limitations of default chart types in Power BI, such as being able to plot data on only one categorical axis Use a much richer set of options to compare different sets of data Re-use your favorite or most often used chart designs with Charticulator templates Who This Book Is For The average Power BI user. It assumes no prior knowledge on the part of the reader other than being able to open Power BI desktop, import data, and create a simple Power BI visual. User experiences may vary, from people attending a Power BI training course to those with varying skills and abilities, from SQL developers and advanced Excel users to people with limited data analysis experience and technical skills.

Safeguarded Copy function that is available with IBM® Spectrum Virtualize Version 8.4.2 supports the ability to create cyber-resilient point-in-time copies of volumes that cannot be changed or deleted through user errors, malicious actions, or ransomware attacks. The system integrates with IBM Copy Services Manager to provide automated backup copies and data recovery. This IBM Redpaper® publication introduces the features and functions of Safeguarded Copy function by using several examples. This document is aimed at pre-sales and post-sales technical support specialists and storage administrators.

Compared to other functions of an organization, data science is highly speculative. Data science teams are often tasked with last-minute must-have deliverables that are well beyond their ability to produce. Data might be missing or have no signal, or the data models themselves might be impractical. This hands-on reference guides team leaders through the types of challenges you might face and the tools you need to work through them. Author Jacqueline Nolis, head of data science at Saturn Cloud, helps team leaders think through the various issues you'll encounter when running a data science team. You'll learn ways to set up your team, manage data scientists to promote their success, and collaborate with external stakeholders. Once you finish this report, you'll be ready to work through the challenges your current team faces or start a new data science team in an organization that needs one. Determine the scope of work before choosing your team of data scientists and support positions Successfully manage your relationship with stakeholders by providing your team with clear, achievable goals Create an environment to help data scientists and other team members succeed Choose a technical infrastructure for your team, including programming languages, databases, and deployment models

Simplify Big Data Analytics with Amazon EMR is a thorough guide to harnessing Amazon's EMR service for big data processing and analytics. From distributed computation pipelines to real-time streaming analytics, this book provides hands-on knowledge and actionable steps for implementing data solutions efficiently. What this Book will help me do Understand the architecture and key components of Amazon EMR and how to deploy it effectively. Learn to configure and manage distributed data processing pipelines using Amazon EMR. Implement security and data governance best practices within the Amazon EMR ecosystem. Master batch ETL and real-time analytics techniques using technologies like Apache Spark. Apply optimization and cost-saving strategies to scalable data solutions. Author(s) Sakti Mishra is a seasoned data professional with extensive expertise in deploying scalable analytics solutions on cloud platforms like AWS. With a background in big data technologies and a passion for teaching, Sakti ensures practical insights accompany every concept. Readers will find his approach thorough, hands-on, and highly informative. Who is it for? This book is perfect for data engineers, data scientists, and other professionals looking to leverage Amazon EMR for scalable analytics. If you are familiar with Python, Scala, or Java and have some exposure to Hadoop or AWS ecosystems, this book will empower you to design and implement robust data pipelines efficiently.

This book investigates the role of data mining in computational statistics for machine learning. It offers applications that can be used in various domains and examines the role of transformation functions in optimizing problem statements.

Discover how to harness the power of the Elastic Stack 8.0 to manage, analyze, and secure complex data environments. You will learn to combine components such as Elasticsearch, Kibana, Logstash, and more to build scalable and effective solutions for your organization. By focusing on hands-on implementations, this book ensures you can apply your knowledge to real-world use cases. What this Book will help me do Set up and manage Elasticsearch clusters tailored to various architecture scenarios. Utilize Logstash and Elastic Agent to ingest and process diverse data sources efficiently. Create interactive dashboards and data models in Kibana, enabling business intelligence insights. Implement secure and effective search infrastructures for enterprise applications. Deploy Elastic SIEM to fortify your organization's security against modern cybersecurity threats. Author(s) Asjad Athick is a seasoned technologist and author with expertise in developing scalable data solutions. With years of experience working with the Elastic Stack, Asjad brings a pragmatic approach to teaching complex architectures. His dedication to explaining technical concepts in an accessible manner makes this book a valuable resource for learners. Who is it for? This book is ideal for developers seeking practical knowledge in search, observability, and security solutions using Elastic Stack. Solutions architects who aim to design scalable data platforms will also benefit greatly. Even tech leads or managers keen to understand the Elastic Stack's impact on their operations will find the insights valuable. No prior experience with Elastic Stack is needed.

Leverage Apache Spark within a modern data engineering ecosystem. This hands-on guide will teach you how to write fully functional applications, follow industry best practices, and learn the rationale behind these decisions. With Apache Spark as the foundation, you will follow a step-by-step journey beginning with the basics of data ingestion, processing, and transformation, and ending up with an entire local data platform running Apache Spark, Apache Zeppelin, Apache Kafka, Redis, MySQL, Minio (S3), and Apache Airflow. Apache Spark applications solve a wide range of data problems from traditional data loading and processing to rich SQL-based analysis as well as complex machine learning workloads and even near real-time processing of streaming data. Spark fits well as a central foundation for any data engineering workload. This book will teach you to write interactive Spark applications using Apache Zeppelin notebooks, write and compilereusable applications and modules, and fully test both batch and streaming. You will also learn to containerize your applications using Docker and run and deploy your Spark applications using a variety of tools such as Apache Airflow, Docker and Kubernetes. ​Reading this book will empower you to take advantage of Apache Spark to optimize your data pipelines and teach you to craft modular and testable Spark applications. You will create and deploy mission-critical streaming spark applications in a low-stress environment that paves the way for your own path to production. ​ What You Will Learn Simplify data transformation with Spark Pipelines and Spark SQL Bridge data engineering with machine learning Architect modular data pipeline applications Build reusable application components and libraries Containerize your Spark applications for consistency and reliability Use Docker and Kubernetes to deploy your Spark applications Speed up application experimentation using Apache Zeppelin and Docker Understand serializable structured data and data contracts Harness effective strategies for optimizing data in your data lakes Build end-to-end Spark structured streaming applications using Redis and Apache Kafka Embrace testing for your batch and streaming applications Deploy and monitor your Spark applications Who This Book Is For Professional software engineers who want to take their current skills and apply them to new and exciting opportunities within the data ecosystem, practicing data engineers who are looking for a guiding light while traversing the many challenges of moving from batch to streaming modes, data architects who wish to provide clear and concise direction for how best to harness anduse Apache Spark within their organization, and those interested in the ins and outs of becoming a modern data engineer in today's fast-paced and data-hungry world

The IBM® TS4500 (TS4500) tape library is a next-generation tape solution that offers higher storage density and better integrated management than previous solutions. This IBM Redbooks® publication gives you a close-up view of the new IBM TS4500 tape library. In the TS4500, IBM delivers the density that today's and tomorrow's data growth requires. It has the cost-effectiveness and the manageability to grow with business data needs, while you preserve investments in IBM tape library products. Now, you can achieve a low per-terabyte cost and high density, with up to 13 PB of data (up to 39 PB compressed) in a single 10 square-foot library by using LTO Ultrium 9 cartridges or 11 PB with 3592 cartridges. The TS4500 offers the following benefits: Support of the IBM Linear Tape-Open (LTO) Ultrium 9 tape drive: Store up to 1.04 EB 2.5:1 compressed per library with IBM LTO 9 cartridges. High availability: Dual active accessors with integrated service bays reduce inactive service space by 40%. The Elastic Capacity option can be used to eliminate inactive service space. Flexibility to grow: The TS4500 library can grow from the right side and the left side of the first L frame because models can be placed in any active position. Increased capacity: The TS4500 can grow from a single L frame up to another 17 expansion frames with a capacity of over 23,000 cartridges. High-density (HD) generation 1 frames from the TS3500 library can be redeployed in a TS4500. Capacity on demand (CoD): CoD is supported through entry-level, intermediate, and base-capacity configurations. Advanced Library Management System (ALMS): ALMS supports dynamic storage management, which enables users to create and change logical libraries and configure any drive for any logical library. Support for IBM TS1160 while also supporting TS1155, TS1150, and TS1140 tape drive. The TS1160 gives organizations an easy way to deliver fast access to data, improve security, and provide long-term retention, all at a lower cost than disk solutions. The TS1160 offers high-performance, flexible data storage with support for data encryption. Also, this enhanced fifth-generation drive can help protect investments in tape automation by offering compatibility with existing automation. Store up to 1.05 EB 3:1 compressed per library with IBM 3592 cartridges Integrated TS7700 back-end Fibre Channel (FC) switches are available. Up to four library-managed encryption (LME) key paths per logical library are available. This book describes the TS4500 components, feature codes, specifications, supported tape drives, encryption, new integrated management console (IMC), command-line interface (CLI), and REST over SCSI (RoS) to obtain status information about library components. You learn how to accomplish the following tasks: Improve storage density with increased expansion frame capacity up to 2.4 times, and support 33% more tape drives per frame

Dive into the world of reproducible data science with Pachyderm, a specialized platform designed for version-controlled data pipelines. By following this book, 'Reproducible Data Science with Pachyderm,' you'll gain the skills to implement robust, scalable machine learning workflows with Pachyderm 2.0, covering setup, integration, and advanced use cases. What this Book will help me do Build scalable, version-controlled data pipelines with Pachyderm's unique features. Understand the principles behind reproducible data science and implement them effectively. Deploy Pachyderm on AWS, Google Cloud, and Azure while integrating with popular tools. Create and manage end-to-end machine learning workflows, including hyperparameter tuning. Leverage advanced integrations, such as Pachyderm Notebooks and language clients like Python and Go. Author(s) Svetlana Karslioglu is a seasoned data scientist with extensive experience in constructing scalable machine learning and data processing systems. With years in both practical implementation and educational endeavors, she has a talent for breaking down complex concepts into accessible learning paths. Her approach is hands-on and results-oriented, aimed at empowering professionals to excel in the field of data science. Who is it for? This book is intended for data scientists, machine learning engineers, and data engineers who are keen to ensure reproducibility in their workflows. Ideal readers may have familiarity with data science basics and some exposure to Kubernetes and programming languages like Python. By studying the book, learners will establish confidence in implementing Pachyderm for scalable and reliable data pipelines.

"Data Lakehouse in Action" provides a comprehensive exploration of the Data Lakehouse architecture, a modern solution for scalable and effective large-scale analytics. This book guides you through understanding the principles and components of the architecture, and its implementation using cloud platforms like Azure. Learn the practical techniques for designing robust systems tailored to organizational needs and maturity. What this Book will help me do Understand the evolution and need for modern data architecture patterns like Data Lakehouse. Learn how to design systems for data ingestion, storage, processing, and serving in a Data Lakehouse. Develop best practices for data governance and security in the Data Lakehouse architecture. Discover various analytics workflows enabled by the Data Lakehouse, including real-time and batch approaches. Implement practical Data Lakehouse patterns on a cloud platform, and integrate them with macro-patterns such as Data Mesh. Author(s) Pradeep Menon is a seasoned data architect and engineer with extensive experience implementing data analytics solutions for leading companies. With a penchant for simplifying complex architectures, Pradeep has authored several technical publications and frequently shares his expertise at industry conferences. His hands-on approach and passion for teaching shine through in his practical guides. Who is it for? This book is ideal for data professionals including architects, engineers, and data strategists eager to enhance their knowledge in modern analytics platforms. If you have a basic understanding of data architecture and are curious about implementing systems governed by the Data Lakehouse paradigm, this book is for you. It bridges foundational concepts with advanced practices, making it suitable for learners aiming to contribute effectively to their organization's analytics efforts.

IBM Spectrum® Virtualize based storage systems are secure storage platforms that implement various security-related features, in terms of system-level access controls and data-level security features. This document outlines the available security features and options of IBM Spectrum Virtualize based storage systems. It is not intended as a "how to" or best practice document. Instead, it is a checklist of features that can be reviewed by a user security team to aid in the definition of a policy to be followed when implementing IBM FlashSystem®, IBM SAN Volume Controller, and IBM Spectrum Virtualize for Public Cloud. The topics that are discussed in this paper can be broadly split into two categories: System security This type of security encompasses the first three lines of defense that prevent unauthorized access to the system, protect the logical configuration of the storage system, and restrict what actions users can perform. It also ensures visibility and reporting of system level events that can be used by a Security Information and Event Management (SIEM) solution, such as IBM QRadar®. Data security This type of security encompasses the fourth line of defense. It protects the data that is stored on the system against theft, loss, or attack. These data security features include, but are not limited to, encryption of data at rest (EDAR) or IBM Safeguarded Copy (SGC). This document is correct as of IBM Spectrum Virtualize version 8.5.0.

Think big about your data! PySpark brings the powerful Spark big data processing engine to the Python ecosystem, letting you seamlessly scale up your data tasks and create lightning-fast pipelines. In Data Analysis with Python and PySpark you will learn how to: Manage your data as it scales across multiple machines Scale up your data programs with full confidence Read and write data to and from a variety of sources and formats Deal with messy data with PySpark’s data manipulation functionality Discover new data sets and perform exploratory data analysis Build automated data pipelines that transform, summarize, and get insights from data Troubleshoot common PySpark errors Creating reliable long-running jobs Data Analysis with Python and PySpark is your guide to delivering successful Python-driven data projects. Packed with relevant examples and essential techniques, this practical book teaches you to build pipelines for reporting, machine learning, and other data-centric tasks. Quick exercises in every chapter help you practice what you’ve learned, and rapidly start implementing PySpark into your data systems. No previous knowledge of Spark is required. About the Technology The Spark data processing engine is an amazing analytics factory: raw data comes in, insight comes out. PySpark wraps Spark’s core engine with a Python-based API. It helps simplify Spark’s steep learning curve and makes this powerful tool available to anyone working in the Python data ecosystem. About the Book Data Analysis with Python and PySpark helps you solve the daily challenges of data science with PySpark. You’ll learn how to scale your processing capabilities across multiple machines while ingesting data from any source—whether that’s Hadoop clusters, cloud data storage, or local data files. Once you’ve covered the fundamentals, you’ll explore the full versatility of PySpark by building machine learning pipelines, and blending Python, pandas, and PySpark code. What's Inside Organizing your PySpark code Managing your data, no matter the size Scale up your data programs with full confidence Troubleshooting common data pipeline problems Creating reliable long-running jobs About the Reader Written for data scientists and data engineers comfortable with Python. About the Author As a ML director for a data-driven software company, Jonathan Rioux uses PySpark daily. He teaches the software to data scientists, engineers, and data-savvy business analysts. Quotes A clear and in-depth introduction for truly tackling big data with Python. - Gustavo Patino, Oakland University William Beaumont School of Medicine The perfect way to learn how to analyze and master huge datasets. - Gary Bake, Brambles Covers both basic and more advanced topics of PySpark, with a good balance between theory and hands-on. - Philippe Van Bergenl, P² Consulting For beginner to pro, a well-written book to help understand PySpark. - Raushan Kumar Jha, Microsoft

Today, more than 80% of the data transmitted over networks and archived on our computers, tablets, cell phones or clouds is multimedia data - images, videos, audio, 3D data. The applications of this data range from video games to healthcare, and include computer-aided design, video surveillance and biometrics. It is becoming increasingly urgent to secure this data, not only during transmission and archiving, but also during its retrieval and use. Indeed, in today’s "all-digital" world, it is becoming ever-easier to copy data, view it unrightfully, steal it or falsify it. Multimedia Security 1 analyzes the issues of the authentication of multimedia data, code and the embedding of hidden data, both from the point of view of defense and attack. Regarding the embedding of hidden data, it also covers invisibility, color, tracing and 3D data, as well as the detection of hidden messages in an image by steganalysis.

This handy reference book detailing the intricacies of R covers version 4.x features, including numerous and significant changes to syntax, strings, reference counting, grid units, and more. Starting with the basic structure of R, the book takes you on a journey through the terminology used in R and the syntax required to make R work. You will find looking up the correct form for an expression quick and easy. Some of the new material includes information on RStudio, S4 syntax, working with character strings, and an example using the Twitter API. With a copy of the R 4 Quick Syntax Reference in hand, you will find that you are able to use the multitude of functions available in R and are even able to write your own functions to explore and analyze data. What You Will Learn Discover the modes and classes of R objects and how to use them Use both packaged and user-created functions in R Import/export data and create new data objects in R Create descriptive functions and manipulate objects in R Take advantage of flow control and conditional statements Work with packages such as base, stats, and graphics Who This Book Is For Those with programming experience, either new to R, or those with at least some exposure to R but who are new to the latest version.

"Getting Started with CockroachDB" provides an in-depth introduction to CockroachDB, a modern, distributed SQL database designed for cloud-native applications. Through this guide, you'll learn how to deploy, manage, and optimize CockroachDB to build highly reliable, scalable database solutions tailored for demanding and distributed workloads. What this Book will help me do Understand the architecture and design principles of CockroachDB and its fault-tolerant model. Learn how to set up and manage CockroachDB clusters for high availability and automatic scaling. Discover the concepts of data distribution and geo-partitioning to achieve low-latency global interactions. Explore indexing mechanisms in CockroachDB to optimize query performance for fast data retrieval. Master operational strategies, security configuration, and troubleshooting techniques for database management. Author(s) Kishen Das Kondabagilu Rajanna is an experienced software developer and database expert with a deep interest in distributed architectures. With hands-on experience working with CockroachDB and other database technologies, Kishen is passionate about sharing actionable insights with readers. His approach focuses on equipping developers with practical skills to excel in building and managing scalable, efficient database services. Who is it for? This book is ideal for software developers, database administrators, and database engineers seeking to learn CockroachDB for building robust, scalable database systems. If you're new to CockroachDB but possess basic database knowledge, this guide will equip you with the practical skills to leverage CockroachDB's capabilities effectively.

This IBM® Redbooks® publication helps you with the planning, installation, and configuration of the new IBM Spectrum® Archive Enterprise Edition (EE) Version 1.3.2.2 for the IBM TS4500, IBM TS3500, IBM TS4300, and IBM TS3310 tape libraries. IBM Spectrum Archive Enterprise Edition enables the use of the LTFS for the policy management of tape as a storage tier in an IBM Spectrum Scale based environment. It also helps encourage the use of tape as a critical tier in the storage environment. This edition of this publication is the tenth edition of IBM Spectrum Archive Installation and Configuration Guide. IBM Spectrum Archive EE can run any application that is designed for disk files on a physical tape media. IBM Spectrum Archive EE supports the IBM Linear Tape-Open (LTO) Ultrium 9, 8, 7, 6, and 5 tape drives. and the IBM TS1160, TS1155, TS1150, and TS1140 tape drives. IBM Spectrum Archive EE can play a major role in reducing the cost of storage for data that does not need the access performance of primary disk. The use of IBM Spectrum Archive EE to replace disks with physical tape in tier 2 and tier 3 storage can improve data access over other storage solutions because it improves efficiency and streamlines management for files on tape. IBM Spectrum Archive EE simplifies the use of tape by making it transparent to the user and manageable by the administrator under a single infrastructure. This publication is intended for anyone who wants to understand more about IBM Spectrum Archive EE planning and implementation. This book is suitable for IBM customers, IBM Business Partners, IBM specialist sales representatives, and technical specialists.

Databases Illuminated, Fourth Edition is designed to help students integrate theoretical material with practical knowledge, using an approach that applies theory to practical database implementation.

We're at an inflection point in data, where our data management solutions no longer match the complexity of organizations, the proliferation of data sources, and the scope of our aspirations to get value from data with AI and analytics. In this practical book, author Zhamak Dehghani introduces data mesh, a decentralized sociotechnical paradigm drawn from modern distributed architecture that provides a new approach to sourcing, sharing, accessing, and managing analytical data at scale. Dehghani guides practitioners, architects, technical leaders, and decision makers on their journey from traditional big data architecture to a distributed and multidimensional approach to analytical data management. Data mesh treats data as a product, considers domains as a primary concern, applies platform thinking to create self-serve data infrastructure, and introduces a federated computational model of data governance. Get a complete introduction to data mesh principles and its constituents Design a data mesh architecture Guide a data mesh strategy and execution Navigate organizational design to a decentralized data ownership model Move beyond traditional data warehouses and lakes to a distributed data mesh