Advertisement
| data pipeline diagram tool: Enterprise Integration Patterns Gregor Hohpe, 2003 |
| data pipeline diagram tool: Mastering Cloud Data Cybellium Ltd, 2023-09-06 Cybellium Ltd is dedicated to empowering individuals and organizations with the knowledge and skills they need to navigate the ever-evolving computer science landscape securely and learn only the latest information available on any subject in the category of computer science including: - Information Technology (IT) - Cyber Security - Information Security - Big Data - Artificial Intelligence (AI) - Engineering - Robotics - Standards and compliance Our mission is to be at the forefront of computer science education, offering a wide and comprehensive range of resources, including books, courses, classes and training programs, tailored to meet the diverse needs of any subject in computer science. Visit https://www.cybellium.com for more books. |
| data pipeline diagram tool: Data Engineering with AWS Gareth Eagar, 2023-10-31 Looking to revolutionize your data transformation game with AWS? Look no further! From strong foundations to hands-on building of data engineering pipelines, our expert-led manual has got you covered. Key Features Delve into robust AWS tools for ingesting, transforming, and consuming data, and for orchestrating pipelines Stay up to date with a comprehensive revised chapter on Data Governance Build modern data platforms with a new section covering transactional data lakes and data mesh Book DescriptionThis book, authored by a seasoned Senior Data Architect with 25 years of experience, aims to help you achieve proficiency in using the AWS ecosystem for data engineering. This revised edition provides updates in every chapter to cover the latest AWS services and features, takes a refreshed look at data governance, and includes a brand-new section on building modern data platforms which covers; implementing a data mesh approach, open-table formats (such as Apache Iceberg), and using DataOps for automation and observability. You'll begin by reviewing the key concepts and essential AWS tools in a data engineer's toolkit and getting acquainted with modern data management approaches. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how that transformed data is used by various data consumers. You’ll learn how to ensure strong data governance, and about populating data marts and data warehouses along with how a data lakehouse fits into the picture. After that, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. Then, you'll explore how the power of machine learning and artificial intelligence can be used to draw new insights from data. In the final chapters, you'll discover transactional data lakes, data meshes, and how to build a cutting-edge data platform on AWS. By the end of this AWS book, you'll be able to execute data engineering tasks and implement a data pipeline on AWS like a pro!What you will learn Seamlessly ingest streaming data with Amazon Kinesis Data Firehose Optimize, denormalize, and join datasets with AWS Glue Studio Use Amazon S3 events to trigger a Lambda process to transform a file Load data into a Redshift data warehouse and run queries with ease Visualize and explore data using Amazon QuickSight Extract sentiment data from a dataset using Amazon Comprehend Build transactional data lakes using Apache Iceberg with Amazon Athena Learn how a data mesh approach can be implemented on AWS Who this book is forThis book is for data engineers, data analysts, and data architects who are new to AWS and looking to extend their skills to the AWS cloud. Anyone new to data engineering who wants to learn about the foundational concepts, while gaining practical experience with common data engineering services on AWS, will also find this book useful. A basic understanding of big data-related topics and Python coding will help you get the most out of this book, but it’s not a prerequisite. Familiarity with the AWS console and core services will also help you follow along. |
| data pipeline diagram tool: Building ETL Pipelines with Python Brij Kishore Pandey, Emily Ro Schoof, 2023-09-29 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. |
| data pipeline diagram tool: Solutions Architect's Handbook Saurabh Shrivastava, Neelanjali Srivastav, 2020-03-21 From fundamentals and design patterns to the different strategies for creating secure and reliable architectures in AWS cloud, learn everything you need to become a successful solutions architect Key Features Create solutions and transform business requirements into technical architecture with this practical guide Understand various challenges that you might come across while refactoring or modernizing legacy applications Delve into security automation, DevOps, and validation of solution architecture Book DescriptionBecoming a solutions architect gives you the flexibility to work with cutting-edge technologies and define product strategies. This handbook takes you through the essential concepts, design principles and patterns, architectural considerations, and all the latest technology that you need to know to become a successful solutions architect. This book starts with a quick introduction to the fundamentals of solution architecture design principles and attributes that will assist you in understanding how solution architecture benefits software projects across enterprises. You'll learn what a cloud migration and application modernization framework looks like, and will use microservices, event-driven, cache-based, and serverless patterns to design robust architectures. You'll then explore the main pillars of architecture design, including performance, scalability, cost optimization, security, operational excellence, and DevOps. Additionally, you'll also learn advanced concepts relating to big data, machine learning, and the Internet of Things (IoT). Finally, you'll get to grips with the documentation of architecture design and the soft skills that are necessary to become a better solutions architect. By the end of this book, you'll have learned techniques to create an efficient architecture design that meets your business requirements.What you will learn Explore the various roles of a solutions architect and their involvement in the enterprise landscape Approach big data processing, machine learning, and IoT from an architect s perspective and understand how they fit into modern architecture Discover different solution architecture patterns such as event-driven and microservice patterns Find ways to keep yourself updated with new technologies and enhance your skills Modernize legacy applications with the help of cloud integration Get to grips with choosing an appropriate strategy to reduce cost Who this book is for This book is for software developers, system engineers, DevOps engineers, architects, and team leaders working in the information technology industry who aspire to become solutions architect professionals. A good understanding of the software development process and general programming experience with any language will be useful. |
| data pipeline diagram tool: InfoWorld , 1999-12-06 InfoWorld is targeted to Senior IT professionals. Content is segmented into Channels and Topic Centers. InfoWorld also celebrates people, companies, and projects. |
| data pipeline diagram tool: Progress in Artificial Intelligence Eugénio Oliveira, João Gama, Zita Vale, Henrique Lopes Cardoso, 2017-08-24 This book constitutes the refereed proceedings of the 18th EPIA Conference on Artificial Intelligence, EPIA 2017, held in Porto, Portugal, in September 2017. The 69 revised full papers and 2 short papers presented were carefully reviewed and selected from a total of 177 submissions. The papers are organized in 16 tracks devoted to the following topics: agent-based modelling for criminological research (ABM4Crime), artificial intelligence in cyber-physical and distributed embedded systems (AICPDES), artificial intelligence in games (AIG), artificial intelligence in medicine (AIM), artificial intelligence in power and energy systems (AIPES), artificial intelligence in transportation systems (AITS), artificial life and evolutionary algorithms (ALEA), ambient intelligence and affective environments (AmIA), business applications of artificial intelligence (BAAI), intelligent robotics (IROBOT), knowledge discovery and business intelligence (KDBI), knowledge representation and reasoning (KRR), multi-agent systems: theory and applications (MASTA), software engineering for autonomous and intelligent systems (SE4AIS), social simulation and modelling (SSM), and text mining and applications (TeMA). |
| data pipeline diagram tool: Data Engineering with Apache Spark, Delta Lake, and Lakehouse Manoj Kukreja, Danil Zburivsky, 2021-10-22 Understand the complexities of modern-day data engineering platforms and explore strategies to deal with them with the help of use case scenarios led by an industry expert in big data Key FeaturesBecome well-versed with the core concepts of Apache Spark and Delta Lake for building data platformsLearn how to ingest, process, and analyze data that can be later used for training machine learning modelsUnderstand how to operationalize data models in production using curated dataBook Description In the world of ever-changing data and schemas, it is important to build data pipelines that can auto-adjust to changes. This book will help you build scalable data platforms that managers, data scientists, and data analysts can rely on. Starting with an introduction to data engineering, along with its key concepts and architectures, this book will show you how to use Microsoft Azure Cloud services effectively for data engineering. You'll cover data lake design patterns and the different stages through which the data needs to flow in a typical data lake. Once you've explored the main features of Delta Lake to build data lakes with fast performance and governance in mind, you'll advance to implementing the lambda architecture using Delta Lake. Packed with practical examples and code snippets, this book takes you through real-world examples based on production scenarios faced by the author in his 10 years of experience working with big data. Finally, you'll cover data lake deployment strategies that play an important role in provisioning the cloud resources and deploying the data pipelines in a repeatable and continuous way. By the end of this data engineering book, you'll know how to effectively deal with ever-changing data and create scalable data pipelines to streamline data science, ML, and artificial intelligence (AI) tasks. What you will learnDiscover the challenges you may face in the data engineering worldAdd ACID transactions to Apache Spark using Delta LakeUnderstand effective design strategies to build enterprise-grade data lakesExplore architectural and design patterns for building efficient data ingestion pipelinesOrchestrate a data pipeline for preprocessing data using Apache Spark and Delta Lake APIsAutomate deployment and monitoring of data pipelines in productionGet to grips with securing, monitoring, and managing data pipelines models efficientlyWho this book is for This book is for aspiring data engineers and data analysts who are new to the world of data engineering and are looking for a practical guide to building scalable data platforms. If you already work with PySpark and want to use Delta Lake for data engineering, you'll find this book useful. Basic knowledge of Python, Spark, and SQL is expected. |
| data pipeline diagram tool: International Tables for Crystallography, Definition and Exchange of Crystallographic Data Sydney Hall, Brian McMahon, 2005-10-07 International Tables for Crystallography is the definitive resource and reference work for crystallography and structural science. Each of the volumes in the series contains articles and tables of data relevant to crystallographic research and to applications of crystallographic methods in all sciences concerned with the structure and properties of materials. Emphasis is given to symmetry, diffraction methods and techniques of crystal-structure determination, and the physical and chemical properties of crystals. The data are accompanied by discussions of theory, practical explanations and examples, all of which are useful for teaching. Volume G deals with methods and tools for organizing, archiving and retrieving crystallographic data. The volume describes the Crystallographic Information File (CIF), the standard data exchange and archival file format used throughout crystallography. The volume is divided into five parts: Part 1 – An introduction to the development of CIF. Part 2 – Details concepts and specifications of the files and languages. Part 3 – Discusses general considerations when defining a CIF data item and the classification and use of data. Part 4 - Defines all the data names for the core and other dictionaries. Part 5 - Describes CIF applications, including general advice and considerations for programmers. The accompanying software includes the CIF dictionaries in machine-readable form and a collection of libraries and utility programs. Volume G is an essential guide for programmers and data managers handling crystal-structure information, and provides in-depth information vital for recording or using single-crystal or powder diffraction data in small-molecule, inorganic and biological macromolecular structure science. More information on the series can be found at: http://it.iucr.org |
| data pipeline diagram tool: Software Architecture Matthias Galster, |
| data pipeline diagram tool: Enterprise AI in the Cloud Rabi Jay, 2023-12-20 Embrace emerging AI trends and integrate your operations with cutting-edge solutions Enterprise AI in the Cloud: A Practical Guide to Deploying End-to-End Machine Learning and ChatGPT Solutions is an indispensable resource for professionals and companies who want to bring new AI technologies like generative AI, ChatGPT, and machine learning (ML) into their suite of cloud-based solutions. If you want to set up AI platforms in the cloud quickly and confidently and drive your business forward with the power of AI, this book is the ultimate go-to guide. The author shows you how to start an enterprise-wide AI transformation effort, taking you all the way through to implementation, with clearly defined processes, numerous examples, and hands-on exercises. You’ll also discover best practices on optimizing cloud infrastructure for scalability and automation. Enterprise AI in the Cloud helps you gain a solid understanding of: AI-First Strategy: Adopt a comprehensive approach to implementing corporate AI systems in the cloud and at scale, using an AI-First strategy to drive innovation State-of-the-Art Use Cases: Learn from emerging AI/ML use cases, such as ChatGPT, VR/AR, blockchain, metaverse, hyper-automation, generative AI, transformer models, Keras, TensorFlow in the cloud, and quantum machine learning Platform Scalability and MLOps (ML Operations): Select the ideal cloud platform and adopt best practices on optimizing cloud infrastructure for scalability and automation AWS, Azure, Google ML: Understand the machine learning lifecycle, from framing problems to deploying models and beyond, leveraging the full power of Azure, AWS, and Google Cloud platforms AI-Driven Innovation Excellence: Get practical advice on identifying potential use cases, developing a winning AI strategy and portfolio, and driving an innovation culture Ethical and Trustworthy AI Mastery: Implement Responsible AI by avoiding common risks while maintaining transparency and ethics Scaling AI Enterprise-Wide: Scale your AI implementation using Strategic Change Management, AI Maturity Models, AI Center of Excellence, and AI Operating Model Whether you're a beginner or an experienced AI or MLOps engineer, business or technology leader, or an AI student or enthusiast, this comprehensive resource empowers you to confidently build and use AI models in production, bridging the gap between proof-of-concept projects and real-world AI deployments. With over 300 review questions, 50 hands-on exercises, templates, and hundreds of best practice tips to guide you through every step of the way, this book is a must-read for anyone seeking to accelerate AI transformation across their enterprise. |
| data pipeline diagram tool: Game Development Tools Marwan Ansari, 2016-04-19 This book brings the insights of game professionals, DCC creators, hardware vendors, and current researchers together into a collection that focuses on the most underrepresented and critical part of game production: tools development. The first gems-type book dedicated to game tools, this volume focuses on practical, implementable tools for game de |
| data pipeline diagram tool: International Tables for Crystallography, Definition and Exchange of Crystallographic Data Sydney R. Hall, Brian McMahon, 2005-08-19 International Tables for Crystallography Volume G, Definition and exchange of crystallographic data, describes the standard data exchange and archival file format (the Crystallographic Information File, or CIF) used throughout crystallography. It provides in-depth information vital for small-molecule, inorganic and macromolecular crystallographers, mineralogists, chemists, materials scientists, solid-state physicists and others who wish to record or use the results of a single-crystal or powder diffraction experiment. The volume also provides the detailed data ontology necessary for programmers and database managers to design interoperable computer applications. The accompanying CD-ROM contains the CIF dictionaries in machine-readable form and a collection of libraries and utility programs. This volume is an essential guide and reference for programmers of crystallographic software, data managers handling crystal-structure information and practising crystallographers who need to use CIF. |
| data pipeline diagram tool: AWS Certified Machine Learning Study Guide Shreyas Subramanian, Stefan Natu, 2021-11-19 Succeed on the AWS Machine Learning exam or in your next job as a machine learning specialist on the AWS Cloud platform with this hands-on guide As the most popular cloud service in the world today, Amazon Web Services offers a wide range of opportunities for those interested in the development and deployment of artificial intelligence and machine learning business solutions. The AWS Certified Machine Learning Study Guide: Specialty (MLS-CO1) Exam delivers hyper-focused, authoritative instruction for anyone considering the pursuit of the prestigious Amazon Web Services Machine Learning certification or a new career as a machine learning specialist working within the AWS architecture. From exam to interview to your first day on the job, this study guide provides the domain-by-domain specific knowledge you need to build, train, tune, and deploy machine learning models with the AWS Cloud. And with the practice exams and assessments, electronic flashcards, and supplementary online resources that accompany this Study Guide, you’ll be prepared for success in every subject area covered by the exam. You’ll also find: An intuitive and organized layout perfect for anyone taking the exam for the first time or seasoned professionals seeking a refresher on machine learning on the AWS Cloud Authoritative instruction on a widely recognized certification that unlocks countless career opportunities in machine learning and data science Access to the Sybex online learning resources and test bank, with chapter review questions, a full-length practice exam, hundreds of electronic flashcards, and a glossary of key terms AWS Certified Machine Learning Study Guide: Specialty (MLS-CO1) Exam is an indispensable guide for anyone seeking to prepare themselves for success on the AWS Certified Machine Learning Specialty exam or for a job interview in the field of machine learning, or who wishes to improve their skills in the field as they pursue a career in AWS machine learning. |
| data pipeline diagram tool: Fundamentals of Analytics Engineering Dumky De Wilde, Fanny Kassapian, Jovan Gligorevic, Juan Manuel Perafan, Lasse Benninga, Ricardo Angel Granados Lopez, Taís Laurindo Pereira, 2024-03-29 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 DescriptionWritten by a team of 7 industry experts, Fundamentals of Analytics Engineering will introduce you to everything from foundational concepts to advanced skills to get started as an analytics engineer. After conquering data ingestion and techniques for data quality and scalability, you’ll learn about techniques such as data cleaning transformation, data modeling, SQL query optimization and reuse, and serving data across different platforms. Armed with this knowledge, you will implement a simple data platform from ingestion to visualization, using tools like Airbyte Cloud, Google BigQuery, dbt, and Tableau. You’ll also get to grips with strategies for data integrity with a focus on data quality and observability, along with collaborative coding practices like version control with Git. You’ll learn about advanced principles like CI/CD, automating workflows, gathering, scoping, and documenting business requirements, as well as data governance. 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 Scale data processing with cloud based analytics platforms and tools Understand the principles of data quality management and data governance Streamline code base with best practices like collaborative coding, version control, reviews and standards Automate and orchestrate data pipelines Drive business adoption with effective scoping and prioritization of analytics use cases 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. |
| data pipeline diagram tool: Big Data Analytics and Knowledge Discovery Robert Wrembel, Johann Gamper, Gabriele Kotsis, A Min Tjoa, Ismail Khalil, 2023-08-09 This book constitutes the proceedings of the 25th International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2023, which took place in Penang, Malaysia, during August 29-30, 2023. The 18 full papers presented together with 19 short papers were carefully reviewed and selected from a total of 83 submissions. They were organized in topical sections as follows: Data quality; advanced analytics and pattern discovery; machine learning; deep learning; and data management. |
| data pipeline diagram tool: Mastering Elastic Stack Yuvraj Gupta, Ravi Kumar Gupta, 2017-02-28 Get the most out of the Elastic Stack for various complex analytics using this comprehensive and practical guide About This Book Your one-stop solution to perform advanced analytics with Elasticsearch, Logstash, and Kibana Learn how to make better sense of your data by searching, analyzing, and logging data in a systematic way This highly practical guide takes you through an advanced implementation on the ELK stack in your enterprise environment Who This Book Is For This book cater to developers using the Elastic stack in their day-to-day work who are familiar with the basics of Elasticsearch, Logstash, and Kibana, and now want to become an expert at using the Elastic stack for data analytics. What You Will Learn Build a pipeline with help of Logstash and Beats to visualize Elasticsearch data in Kibana Use Beats to ship any type of data to the Elastic stack Understand Elasticsearch APIs, modules, and other advanced concepts Explore Logstash and it's plugins Discover how to utilize the new Kibana UI for advanced analytics See how to work with the Elastic Stack using other advanced configurations Customize the Elastic Stack and plugin development for each of the component Work with the Elastic Stack in a production environment Explore the various components of X-Pack in detail. In Detail Even structured data is useless if it can't help you to take strategic decisions and improve existing system. If you love to play with data, or your job requires you to process custom log formats, design a scalable analysis system, and manage logs to do real-time data analysis, this book is your one-stop solution. By combining the massively popular Elasticsearch, Logstash, Beats, and Kibana, elastic.co has advanced the end-to-end stack that delivers actionable insights in real time from almost any type of structured or unstructured data source. If your job requires you to process custom log formats, design a scalable analysis system, explore a variety of data, and manage logs, this book is your one-stop solution. You will learn how to create real-time dashboards and how to manage the life cycle of logs in detail through real-life scenarios. This book brushes up your basic knowledge on implementing the Elastic Stack and then dives deeper into complex and advanced implementations of the Elastic Stack. We'll help you to solve data analytics challenges using the Elastic Stack and provide practical steps on centralized logging and real-time analytics with the Elastic Stack in production. You will get to grip with advanced techniques for log analysis and visualization. Newly announced features such as Beats and X-Pack are also covered in detail with examples. Toward the end, you will see how to use the Elastic stack for real-world case studies and we'll show you some best practices and troubleshooting techniques for the Elastic Stack. Style and approach This practical guide shows you how to perform advanced analytics with the Elastic stack through real-world use cases. It includes common and some not so common scenarios to use the Elastic stack for data analysis. |
| data pipeline diagram tool: Pro Hadoop Data Analytics Kerry Koitzsch, 2016-12-29 Learn advanced analytical techniques and leverage existing tool kits to make your analytic applications more powerful, precise, and efficient. This book provides the right combination of architecture, design, and implementation information to create analytical systems that go beyond the basics of classification, clustering, and recommendation. Pro Hadoop Data Analytics emphasizes best practices to ensure coherent, efficient development. A complete example system will be developed using standard third-party components that consist of the tool kits, libraries, visualization and reporting code, as well as support glue to provide a working and extensible end-to-end system. The book also highlights the importance of end-to-end, flexible, configurable, high-performance data pipeline systems with analytical components as well as appropriate visualization results. You'll discover the importance of mix-and-match or hybrid systems, using different analytical components in one application. This hybrid approach will be prominent in the examples. What You'll Learn Build big data analytic systems with the Hadoop ecosystem Use libraries, tool kits, and algorithms to make development easier and more effective Apply metrics to measure performance and efficiency of components and systems Connect to standard relational databases, noSQL data sources, and more Follow case studies with example components to create your own systems Who This Book Is For Software engineers, architects, and data scientists with an interest in the design and implementation of big data analytical systems using Hadoop, the Hadoop ecosystem, and other associated technologies. |
| data pipeline diagram tool: Building Machine Learning Pipelines Hannes Hapke, Catherine Nelson, 2020-07-13 Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. Understand the steps to build a machine learning pipeline Build your pipeline using components from TensorFlow Extended Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines Work with data using TensorFlow Data Validation and TensorFlow Transform Analyze a model in detail using TensorFlow Model Analysis Examine fairness and bias in your model performance Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices Learn privacy-preserving machine learning techniques |
| data pipeline diagram tool: Splunk 7.x Quick Start Guide James H. Baxter, 2018-11-29 Learn how to architect, implement, and administer a complex Splunk Enterprise environment and extract valuable insights from business data. Key FeaturesUnderstand the various components of Splunk and how they work together to provide a powerful Big Data analytics solution. Collect and index data from a wide variety of common machine data sourcesDesign searches, reports, and dashboard visualizations to provide business data insightsBook Description Splunk is a leading platform and solution for collecting, searching, and extracting value from ever increasing amounts of big data - and big data is eating the world! This book covers all the crucial Splunk topics and gives you the information and examples to get the immediate job done. You will find enough insights to support further research and use Splunk to suit any business environment or situation. Splunk 7.x Quick Start Guide gives you a thorough understanding of how Splunk works. You will learn about all the critical tasks for architecting, implementing, administering, and utilizing Splunk Enterprise to collect, store, retrieve, format, analyze, and visualize machine data. You will find step-by-step examples based on real-world experience and practical use cases that are applicable to all Splunk environments. There is a careful balance between adequate coverage of all the critical topics with short but relevant deep-dives into the configuration options and steps to carry out the day-to-day tasks that matter. By the end of the book, you will be a confident and proficient Splunk architect and administrator. What you will learnDesign and implement a complex Splunk Enterprise solutionConfigure your Splunk environment to get machine data in and indexedBuild searches to get and format data for analysis and visualizationBuild reports, dashboards, and alerts to deliver critical insightsCreate knowledge objects to enhance the value of your dataInstall Splunk apps to provide focused views into key technologiesMonitor, troubleshoot, and manage your Splunk environmentWho this book is for This book is intended for experienced IT personnel who are just getting started working with Splunk and want to quickly become proficient with its usage. Data analysts who need to leverage Splunk to extract critical business insights from application logs and other machine data sources will also benefit from this book. |
| data pipeline diagram tool: InfoWorld , 1994-02-21 InfoWorld is targeted to Senior IT professionals. Content is segmented into Channels and Topic Centers. InfoWorld also celebrates people, companies, and projects. |
| data pipeline diagram tool: Proceedings of International Conference on Fourth Industrial Revolution and Beyond 2021 Sazzad Hossain, Md. Shahadat Hossain, M. Shamim Kaiser, Satya Prasad Majumder, Kanad Ray, 2022-10-03 This book includes papers in the research area of artificial intelligence, robotics and automation, IoT smart agriculture, data analysis and cloud computing, communication and technology, and signal and natural language processing. The book is a collection of research papers presented at the First International Conference on Fourth Industrial Revolution and Beyond (IC4IR 2021) organized by University Grants Commission of Bangladesh in association with IEEE Computer Society Bangladesh Chapter and Bangladesh Computer Society during December 10–11, 2021. |
| data pipeline diagram tool: The Definitive Guide to Modernizing Applications on Google Cloud Steve (Satish) Sangapu, Dheeraj Panyam, Jason Marston, 2022-01-06 Get to grips with the tools, services, and functions needed for application migration to help you move from legacy applications to cloud-native on Google Cloud Key FeaturesDiscover how a sample legacy application can be transformed into a cloud-native application on Google CloudLearn where to start and how to apply application modernization techniques and toolingWork with real-world use cases and instructions to modernize an application on Google CloudBook Description Legacy applications, which comprise 75–80% of all enterprise applications, often end up being stuck in data centers. Modernizing these applications to make them cloud-native enables them to scale in a cloud environment without taking months or years to start seeing the benefits. This book will help software developers and solutions architects to modernize their applications on Google Cloud and transform them into cloud-native applications. This book helps you to build on your existing knowledge of enterprise application development and takes you on a journey through the six Rs: rehosting, replatforming, rearchitecting, repurchasing, retiring, and retaining. You'll learn how to modernize a legacy enterprise application on Google Cloud and build on existing assets and skills effectively. Taking an iterative and incremental approach to modernization, the book introduces the main services in Google Cloud in an easy-to-understand way that can be applied immediately to an application. By the end of this Google Cloud book, you'll have learned how to modernize a legacy enterprise application by exploring various interim architectures and tooling to develop a cloud-native microservices-based application. What you will learnDiscover the principles and best practices for building cloud-native applicationsStudy the six Rs of migration strategy and learn when to choose which strategyRehost a legacy enterprise application on Google Compute EngineReplatform an application to use Google Load Balancer and Google Cloud SQLRefactor into a single-page application (SPA) supported by REST servicesReplatform an application to use Google Identity Platform and Firebase AuthenticationRefactor to microservices using the strangler patternAutomate the deployment process using a CI/CD pipeline with Google Cloud BuildWho this book is for This book is for software developers and solutions architects looking to gain experience in modernizing their enterprise applications to run on Google Cloud and transform them into cloud-native applications. Basic knowledge of Java and Spring Boot is necessary. Prior knowledge of Google Cloud is useful but not mandatory. |
| data pipeline diagram tool: Practical Cloud Security Chris Dotson, 2019-03-04 With their rapidly changing architecture and API-driven automation, cloud platforms come with unique security challenges and opportunities. This hands-on book guides you through security best practices for multivendor cloud environments, whether your company plans to move legacy on-premises projects to the cloud or build a new infrastructure from the ground up. Developers, IT architects, and security professionals will learn cloud-specific techniques for securing popular cloud platforms such as Amazon Web Services, Microsoft Azure, and IBM Cloud. Chris Dotson—an IBM senior technical staff member—shows you how to establish data asset management, identity and access management, vulnerability management, network security, and incident response in your cloud environment. |
| data pipeline diagram tool: Data Engineering Best Practices Richard J. Schiller, David Larochelle, 2024-10-11 Explore modern data engineering techniques and best practices to build scalable, efficient, and future-proof data processing systems across cloud platforms Key Features Architect and engineer optimized data solutions in the cloud with best practices for performance and cost-effectiveness Explore design patterns and use cases to balance roles, technology choices, and processes for a future-proof design Learn from experts to avoid common pitfalls in data engineering projects Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionRevolutionize your approach to data processing in the fast-paced business landscape with this essential guide to data engineering. Discover the power of scalable, efficient, and secure data solutions through expert guidance on data engineering principles and techniques. Written by two industry experts with over 60 years of combined experience, it offers deep insights into best practices, architecture, agile processes, and cloud-based pipelines. You’ll start by defining the challenges data engineers face and understand how this agile and future-proof comprehensive data solution architecture addresses them. As you explore the extensive toolkit, mastering the capabilities of various instruments, you’ll gain the knowledge needed for independent research. Covering everything you need, right from data engineering fundamentals, the guide uses real-world examples to illustrate potential solutions. It elevates your skills to architect scalable data systems, implement agile development processes, and design cloud-based data pipelines. The book further equips you with the knowledge to harness serverless computing and microservices to build resilient data applications. By the end, you'll be armed with the expertise to design and deliver high-performance data engineering solutions that are not only robust, efficient, and secure but also future-ready.What you will learn Architect scalable data solutions within a well-architected framework Implement agile software development processes tailored to your organization's needs Design cloud-based data pipelines for analytics, machine learning, and AI-ready data products Optimize data engineering capabilities to ensure performance and long-term business value Apply best practices for data security, privacy, and compliance Harness serverless computing and microservices to build resilient, scalable, and trustworthy data pipelines Who this book is for If you are a data engineer, ETL developer, or big data engineer who wants to master the principles and techniques of data engineering, this book is for you. A basic understanding of data engineering concepts, ETL processes, and big data technologies is expected. This book is also for professionals who want to explore advanced data engineering practices, including scalable data solutions, agile software development, and cloud-based data processing pipelines. |
| data pipeline diagram tool: Practical Real-time Data Processing and Analytics Shilpi Saxena, Saurabh Gupta, 2017-09-28 A practical guide to help you tackle different real-time data processing and analytics problems using the best tools for each scenario About This Book Learn about the various challenges in real-time data processing and use the right tools to overcome them This book covers popular tools and frameworks such as Spark, Flink, and Apache Storm to solve all your distributed processing problems A practical guide filled with examples, tips, and tricks to help you perform efficient Big Data processing in real-time Who This Book Is For If you are a Java developer who would like to be equipped with all the tools required to devise an end-to-end practical solution on real-time data streaming, then this book is for you. Basic knowledge of real-time processing would be helpful, and knowing the fundamentals of Maven, Shell, and Eclipse would be great. What You Will Learn Get an introduction to the established real-time stack Understand the key integration of all the components Get a thorough understanding of the basic building blocks for real-time solution designing Garnish the search and visualization aspects for your real-time solution Get conceptually and practically acquainted with real-time analytics Be well equipped to apply the knowledge and create your own solutions In Detail With the rise of Big Data, there is an increasing need to process large amounts of data continuously, with a shorter turnaround time. Real-time data processing involves continuous input, processing and output of data, with the condition that the time required for processing is as short as possible. This book covers the majority of the existing and evolving open source technology stack for real-time processing and analytics. You will get to know about all the real-time solution aspects, from the source to the presentation to persistence. Through this practical book, you'll be equipped with a clear understanding of how to solve challenges on your own. We'll cover topics such as how to set up components, basic executions, integrations, advanced use cases, alerts, and monitoring. You'll be exposed to the popular tools used in real-time processing today such as Apache Spark, Apache Flink, and Storm. Finally, you will put your knowledge to practical use by implementing all of the techniques in the form of a practical, real-world use case. By the end of this book, you will have a solid understanding of all the aspects of real-time data processing and analytics, and will know how to deploy the solutions in production environments in the best possible manner. Style and Approach In this practical guide to real-time analytics, each chapter begins with a basic high-level concept of the topic, followed by a practical, hands-on implementation of each concept, where you can see the working and execution of it. The book is written in a DIY style, with plenty of practical use cases, well-explained code examples, and relevant screenshots and diagrams. |
| data pipeline diagram tool: Software Architecture Bedir Tekinerdogan, Catia Trubiani, Chouki Tibermacine, Patrizia Scandurra, Carlos E. Cuesta, 2023-09-07 This book constitutes the refereed proceedings of the 17th International Conference on Software Architecture, ECSA 2023, held in Istanbul, Turkey, in September 2023. The 16 full papers and the 9 short papers included in this volume were carefully reviewed and selected from 71 submissions. They address the most recent, innovative, and significant findings and experiences in the field of software architecture research and practice. |
| data pipeline diagram tool: Data Engineering with Alteryx Paul Houghton, 2022-06-30 Build and deploy data pipelines with Alteryx by applying practical DataOps principles Key Features • Learn DataOps principles to build data pipelines with Alteryx • Build robust data pipelines with Alteryx Designer • Use Alteryx Server and Alteryx Connect to share and deploy your data pipelines Book Description Alteryx is a GUI-based development platform for data analytic applications. Data Engineering with Alteryx will help you leverage Alteryx's code-free aspects which increase development speed while still enabling you to make the most of the code-based skills you have. This book will teach you the principles of DataOps and how they can be used with the Alteryx software stack. You'll build data pipelines with Alteryx Designer and incorporate the error handling and data validation needed for reliable datasets. Next, you'll take the data pipeline from raw data, transform it into a robust dataset, and publish it to Alteryx Server following a continuous integration process. By the end of this Alteryx book, you'll be able to build systems for validating datasets, monitoring workflow performance, managing access, and promoting the use of your data sources. What you will learn • Build a working pipeline to integrate an external data source • Develop monitoring processes for the pipeline example • Understand and apply DataOps principles to an Alteryx data pipeline • Gain skills for data engineering with the Alteryx software stack • Work with spatial analytics and machine learning techniques in an Alteryx workflow Explore Alteryx workflow deployment strategies using metadata validation and continuous integration • Organize content on Alteryx Server and secure user access Who this book is for If you're a data engineer, data scientist, or data analyst who wants to set up a reliable process for developing data pipelines using Alteryx, this book is for you. You'll also find this book useful if you are trying to make the development and deployment of datasets more robust by following the DataOps principles. Familiarity with Alteryx products will be helpful but is not necessary. |
| data pipeline diagram tool: Biomedical Visualisation Paul M. Rea, 2019-07-16 This edited book explores the use of technology to enable us to visualise the life sciences in a more meaningful and engaging way. It will enable those interested in visualisation techniques to gain a better understanding of the applications that can be used in visualisation, imaging and analysis, education, engagement and training. The reader will be able to explore the utilisation of technologies from a number of fields to enable an engaging and meaningful visual representation of the biomedical sciences. This use of technology-enhanced learning will be of benefit for the learner, trainer and faculty, in patient care and the wider field of education and engagement. This second volume on Biomedical Visualisation will explore the use of a variety of visualisation techniques to enhance our understanding of how to visualise the body, its processes and apply it to a real world context. It is divided into three broad categories – Education; Craniofacial Anatomy and Applications and finally Visual Perception and Data Visualization. In the first four chapters, it provides a detailed account of the history of the development of 3D resources for visualisation. Following on from this will be three major case studies which examine a variety of educational perspectives in the creation of resources. One centres around neuropsychiatric education, one is based on gaming technology and its application in a university biology curriculum, and the last of these chapters examines how ultrasound can be used in the modern day anatomical curriculum. The next three chapters focus on a complex area of anatomy, and helps to create an engaging resource of materials focussed on craniofacial anatomy and applications. The first of these chapters examines how skulls can be digitised in the creation of an educational and training package, with excellent hints and tips. The second of these chapters has a real-world application related to forensic anatomy which examines skulls and soft tissue landmarks in the creation of a database for Cretan skulls, comparing it to international populations. The last three chapters present technical perspetives on visual perception and visualisation. By detailing visual perception, visual analytics and examination of multi-modal, multi-parametric data, these chapters help to understand the true scientific meaning of visualisation. The work presented here can be accessed by a wide range of users from faculty and students involved in the design and development of these processes, to those developing tools and techniques to enable visualisation in the sciences. |
| data pipeline diagram tool: Advanced Information Networking and Applications Leonard Barolli, Isaac Woungang, Tomoya Enokido, 2021-04-30 This book covers the theory, design and applications of computer networks, distributed computing and information systems. Networks of today are going through a rapid evolution, and there are many emerging areas of information networking and their applications. Heterogeneous networking supported by recent technological advances in low-power wireless communications along with silicon integration of various functionalities such as sensing, communications, intelligence and actuations is emerging as a critically important disruptive computer class based on a new platform, networking structure and interface that enable novel, low-cost and high-volume applications. Several of such applications have been difficult to realize because of many interconnections problems. To fulfill their large range of applications, different kinds of networks need to collaborate, and wired and next-generation wireless systems should be integrated in order to develop high-performance computing solutions to problems arising from the complexities of these networks. The aim of the book “Advanced Information Networking and Applications” is to provide latest research findings, innovative research results, methods and development techniques from both theoretical and practical perspectives related to the emerging areas of information networking and applications. |
| data pipeline diagram tool: Product-Focused Software Process Improvement Luca Ardito, Andreas Jedlitschka, Maurizio Morisio, Marco Torchiano, 2021-11-23 This book constitutes the refereed proceedings of the 22nd International Conference on Product-Focused Software Process Improvement, PROFES 2021, held in Turin, Italy, in November 2021. Due to COVID-19 pandemic the conference was held as a hybrid event. The 20 revised papers, including 14 full papers, 3 short papers and 3 industry papers, presented were carefully reviewed and selected from 48 submissions. The papers cover a broad range of topics related to professional software development and process improvement driven by product and service quality needs. They are organized in the following topical sections: agile and migration, requirements, human factors, and software quality. |
| data pipeline diagram tool: Languages, Compilers, and Tools for Embedded Systems Frank Mueller, Azer Bestavros, 1998 This book constitutes the strictly refereed post-workshop proceedings of the ACM SIGPLAN Workshop on Languages, Compilers, and Tools for Embedded Systems, LCTES '98, held in Montreal, Canada, in June 1998. The 19 revised papers presented were carefully reviewed and selected from a total of 54 submissions for inclusion in the book; also included are one full paper and an abstract of an invited contribution. The papers address all current aspects of research and development in the rapidly growing area of embedded systems and real-time computing. |
| data pipeline diagram tool: Implementing Enterprise Cybersecurity with Opensource Software and Standard Architecture Anand Handa, Rohit Negi, Sandeep Kumar Shukla, 2022-09-01 Many small and medium scale businesses cannot afford to procure expensive cybersecurity tools. In many cases, even after procurement, lack of a workforce with knowledge of the standard architecture of enterprise security, tools are often used ineffectively. The Editors have developed multiple projects which can help in developing cybersecurity solution architectures and the use of the right tools from the opensource software domain. This book has 8 chapters describing these projects in detail with recipes on how to use opensource tooling to obtain standard cyber defense and the ability to do self-penetration testing and vulnerability assessment. This book also demonstrates work related to malware analysis using machine learning and implementation of honeypots, network Intrusion Detection Systems in a security operation center environment. It is essential reading for cybersecurity professionals and advanced students. |
| data pipeline diagram tool: Mobile Commerce: Concepts, Methodologies, Tools, and Applications Management Association, Information Resources, 2017-06-19 In the era of digital technology, business transactions and partnerships across borders have become easier than ever. As part of this shift in the corporate sphere, managers, executives, and strategists across industries must acclimate themselves with the challenges and opportunities for conducting business. Mobile Commerce: Concepts, Methodologies, Tools, and Applications provides a comprehensive source of advanced academic examinations on the latest innovations and technologies for businesses. Including innovative studies on marketing, mobile commerce security, and wireless handheld devices, this multi-volume book is an ideal source for researchers, scholars, business executives, professionals, and graduate-level students. |
| data pipeline diagram tool: Emerging Bioinformatic Tools in Toxicogenomics Danyel Jennen, Paul Jennings, 2020-02-27 Toxicogenomics was established as a merger of toxicology with genomics approaches and methodologies more than 15 years ago, and considered of major value for studying toxic mechanisms-of-action in greater depth and for classification of toxic agents for predicting adverse human health risks. While the original focus was on technological validation of in particular microarray-based whole genome expression analysis (transcriptomics), mainly through cross-comparing different platforms for data generation (MAQC-I), it was soon appreciated that actually the wide variety of data analysis approaches represents the major source of inter-study variation. This led to early attempts towards harmonizing data analysis protocols focusing on microarray-based models for predicting toxicological and clinical end-points and on different methods for GWAS data (MAQC-II). Simultaneously, further technological developments, geared by increasing insights into the complexity of cellular regulation, enabled analyzing molecular perturbations across multiple genomics scales (epigenomics and microRNAs, metabolomics). While these were initially still based on microarray technology, this is currently being phased out and replaced by a variety of next generation sequencing-based methods enabling exploration of genomic responses to toxicants at even greater depth (SEQC-I). This raises the demand for reliable and robust data analysis approaches, ranging from harmonized bioinformatics concepts for preprocessing raw data to non-supervised and supervised methods for capturing and integrating the dynamic perturbations of cell function across dose and time, and thus retrieving mechanistic insights across multiple regulation scales. Traditional toxicology focused on dose-dependently determining apical endpoints of toxicity. With the advent of toxicogenomics, efforts towards better understanding underlying molecular mechanisms has led to the development of the concept of Adverse Outcome Pathways, which are basically presented as a structural network of linearly related gene-gene interactions regulating key events for inducing apical toxic endpoints of interest. Impulse challenges from exposure of biological systems to toxic agents will however induce a cascade-type of events, presenting both adverse and adaptive processes, thus requiring bioinformatics approaches and methods for complex dynamic data, generated not only across dose, but clearly also across time. Currently, time-resolved toxicogenomics data sets are increasingly being assembled in the course of large-scaled research projects, for instance devoted towards developing toxicogenomics-based predictive assays for evaluating chemical safety which are no longer animal-based. |
| data pipeline diagram tool: Accelerating DevSecOps on AWS Nikit Swaraj, 2022-04-28 Build high-performance CI/CD pipelines that are powered by AWS and the most cutting-edge tools and techniques Key FeaturesMaster the full AWS developer toolchain for building high-performance, resilient, and powerful CI/CD pipelinesGet to grips with Chaos engineering, DevSecOps, and AIOps as applied to CI/CDEmploy the latest tools and techniques to build a CI/CD pipeline for application and infrastructureBook Description Continuous integration and continuous delivery (CI/CD) has never been simple, but these days the landscape is more bewildering than ever; its terrain riddled with blind alleys and pitfalls that seem almost designed to trap the less-experienced developer. If you're determined enough to keep your balance on the cutting edge, this book will help you navigate the landscape with ease. This book will guide you through the most modern ways of building CI/CD pipelines with AWS, taking you step-by-step from the basics right through to the most advanced topics in this domain. The book starts by covering the basics of CI/CD with AWS. Once you're well-versed with tools such as AWS Codestar, Proton, CodeGuru, App Mesh, SecurityHub, and CloudFormation, you'll focus on chaos engineering, the latest trend in testing the fault tolerance of your system. Next, you'll explore the advanced concepts of AIOps and DevSecOps, two highly sought-after skill sets for securing and optimizing your CI/CD systems. All along, you'll cover the full range of AWS CI/CD features, gaining real-world expertise. By the end of this AWS book, you'll have the confidence you need to create resilient, secure, and performant CI/CD pipelines using the best techniques and technologies that AWS has to offer. What you will learnUse AWS Codestar to design and implement a full branching strategyEnforce Policy as Code using CloudFormation Guard and HashiCorp SentinelMaster app and infrastructure deployment at scale using AWS Proton and review app code using CodeGuruDeploy and manage production-grade clusters using AWS EKS, App Mesh, and X-RayHarness AWS Fault Injection Simulator to test the resiliency of your appWield the full arsenal of AWS Security Hub and Systems Manager for infrastructure security automationEnhance CI/CD pipelines with the AI-powered DevOps Guru serviceWho this book is for This book is for DevOps engineers, engineering managers, cloud developers, and cloud architects. Basic experience with the software development life cycle, DevOps, and AWS is all you need to get started. |
| data pipeline diagram tool: Data Mesh Zhamak Dehghani, 2022-03-08 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. |
| data pipeline diagram tool: Network World , 2000-04-10 For more than 20 years, Network World has been the premier provider of information, intelligence and insight for network and IT executives responsible for the digital nervous systems of large organizations. Readers are responsible for designing, implementing and managing the voice, data and video systems their companies use to support everything from business critical applications to employee collaboration and electronic commerce. |
| data pipeline diagram tool: Using the Structured Techniques Audrey M. Weaver, 1987 |
| data pipeline diagram tool: Dictionary of Computer Science, Engineering and Technology Philip A. Laplante, 2017-12-19 A complete lexicon of technical information, the Dictionary of Computer Science, Engineering, and Technology provides workable definitions, practical information, and enhances general computer science and engineering literacy. It spans various disciplines and industry sectors such as: telecommunications, information theory, and software and hardware systems. If you work with, or write about computers, this dictionary is the single most important resource you can put on your shelf. The dictionary addresses all aspects of computing and computer technology from multiple perspectives, including the academic, applied, and professional vantage points. Including more than 8,000 terms, it covers all major topics from artificial intelligence to programming languages, from software engineering to operating systems, and from database management to privacy issues. The definitions provided are detailed rather than concise. Written by an international team of over 80 contributors, this is the most comprehensive and easy-to-read reference of its kind. If you need to know the definition of anything related to computers you will find it in the Dictionary of Computer Science, Engineering, and Technology. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
CYBER SECURITY EVALUATION TOOL
3. Create the Diagram. CSET contains a graphical user interface that allows users to . diagram network topology and identify the “criticality” of the . network components. Users can create a …
Design of Single Cycle and Pipeline Processor - Computer …
Nov 19, 2014 · Still, the more important part of testing pipeline processor is cases with data dependences and/or control flow since Pipeline processor may have data and control hazards. …
Amazon Web Services (AWS) Data Pipeline Whitepaper
Amazon Web Services (AWS) provides AWS Data Pipeline, a data integration web service that is robust and highly available at nearly 1/10th the cost of other data integration tools. AWS Data …
UKOPA Good Practice Guide
The ILI tool collects various forms of data using sensors and electronics about the condition of the pipeline from the inside. ILI is used extensively in the pipeline industry, although not all …
AWS Data Pipeline - Developer Guide
AWS Data Pipeline service is in maintenance mode and no new features or region expansions are planned. To learn more and to find out how to migrate your existing workloads, see Migrating …
Observations on the Application of Smart ... - Pipeline Safety …
data, and power sources to drive all the electronics. Together this equipment mounted on a pig sled can 1 An anomaly is an imperfection in the pipe wall or weld. All pipelines have anomalies …
Guide to M&A Deal Flow and Pipeline Management - Midaxo
8 | Guide to M&A Deal Flow and Pipeline Management 2022 Midaxo Although you can run your pipeline and projects without a dedicated platform, it is much easier with a tool purpose-built …
Lab 5: RISC-V Introduction { Multi-Cycle and Two-Stage …
Lab 5: RISC-V Introduction { Multi-Cycle and Two-Stage Pipeline Due: 11:59:59pm, Monday October 26, 2015 1 Introduction This lab introduces the RISC-V processor and the tool ow …
DevSecOps Fundamentals - U.S. Department of Defense
• Inputs: Types of data collected by the tool category • Outputs: Types of artifacts that result from using the tool category • Baseline: Either a status of REQUIRED or PREFERRED, where …
DevSecOps Fundmentals Guidebook - U.S. Department of …
Oct 19, 2021 · • Inputs: Types of data collected by the tool category • Outputs: Types of artifacts that result from using the tool category • Baseline: Either a status of REQUIRED or …
AN INTRODUCTION TO PIPELINE PIGGING By Robert …
Sealing Disc Diameter 102% to 105% Pipeline ID Support Disc Diameter 99% Pipeline ID Pig Assembly Length 1.5 × Pipeline ID TYPES OF PIGS Batching Pig: Also known as a swabbing …
CS/ECE 752: Advanced Computer Architecture I - University …
Five Stage Pipeline Performance • Pipelining: cut datapath into N stages (here 5) • One insn in each stage in each cycle + Clock period = MAX(T insn-mem, T regfile, T ALU, T data-mem) + …
SAP Signavio Process Intelligence User Guide - SAP Online …
Managing Insights.....469 Who Can Work With Insights.....472
Chapter 06 Compression slides 091806 - Elsevier
Introduce the basic concepts of test data compression ... Force ATPG tool to generate patterns for broadcast scan. EE141 28 VLSI Test Principles and Architectures Ch. 6 - Test Compression – …
Data Warehousing on AWS
data pipeline. Such a pipeline extracts the data from the source system, converts it into a schema suitable for data warehousing, and then loads it into the data warehouse. In the next section, …
Development of a Risk Rating Matrix for Assessing Onshore …
diagram is used to display the results of the risk analysis for each pipeline/geographical region. The paper also includes a review of current failure databases to provide data on the frequency …
Image Sensor Processing (ISP) Pipeline - Xilinx
ISP pipeline being the building block of digital camera has found its application in different camera systems, where the application can be easily run in a few minutes in the Nimbix cloud or on …
Introduction to Spatial Transcriptomic (ST) Data Analysis
Challenges in ST Data Analysis •Wide range of protocols and data processing pipelines •A larger variety of file formats and data structures due to heterogeneity of methodologies •No …
ECDA Indirect Inspection Tools – ACVG, Current Attenuation …
What can CA data tell us? •It is a macro tool that highlights the bigger problems in a coated pipeline system including: –Shorts to other structures –Grounding to electric neutral –Bad …
Guidance for Modern Insurance Data Lakes on AWS
Guidance for Modern Insurance Data Lakes on AWS This architecture diagram shows how to collect, cleanse, and consume insurance data with ETL processes and data storage. Business …
Practical Guide to Interpreting RNA-seq Data - Cancer
III. Processing Pipeline Conceptual Diagram 18 Differential Expression Summarizing differences between two groups or conditions (KO vs. WT) Quantification Counting the number of reads …
Utility Network Data Migration: Best Practices - Esri
It is a key tool to migrate existing data into an asset package that will be loaded to a staged utility network. The sample utility network migration workspaces (available on GeoNet) are …
Oxford Nanopore bioinformatics pipeline: from basecalling to …
Date: 28 October 2021 Version: 1.1 Authors: Dr Linzy Elton, Professor Neil Stoker, Dr Sylvia Rofael 5 Coverage: this is the percentage of the whole genome that has been sequenced. For …
Automatic Digitization of Engineering Diagrams Using Deep …
tion step. An example of a snippet from an input diagram and a manually-labeled symbol is shown in Figure 2. In this section, we describe each step of the pipeline and demonstrate how each …
The SWIFT XRT Data Reduction Guide - NASA
The pipeline outputs different levels of science data, which are subsequently archived, corresponding to stages of the processing pipeline. It also produces a filter file (mkf file), ...
This Unit: Pipelining Advanced Computer Architecture I
Abstract Pipeline • This is an integer pipeline Execution stages are X,M,W • Usually also one or more floating-point (FP) pipelines Separate FP register file One “pipeline” per functional unit: …
Modernize Applications with Microservices Using Amazon EKS
Contributors to this reference architecture diagram include: • Sheng Chen, Senior Migration Solutions Architect, VMware Cloud on AWS • Jyothi Goudar, Manager Partner Solutions …
AGAAT: Automated computational tool integrating different
A computational tool that automates everything from raw data conversion to association analysis will be helpful for large scale genotyping array analysis. Another problem that can ... Figure 1: …
Architectural Patterns to Build End-to-End Data Driven …
data collection, manage billions of devices and purpose-built databases to save costs, modernize databases for the cloud, and innovate faster. Use analytics to get fastest insights on all your …
Educational Simulator for Analysing Pipelined LEGv8 …
code, simulate a partially or fully five -stage pipeline, and view register and memory values , input/output data from pipeline elements and both the data path and control path on the …
A guide to implementing DevSecOps - Opensource.com
remediation tool because it permits scanning automation throughout each pipeline phase. OSS is also foundational for adopting and security software containers and Kubernetes. Final …
tutorials - Dassault Systèmes
DATA PARTITIONING 38 STEP #1 Export a new Module 38 STEP #2 Open module as a new project 39 STEP #3 Reload the module 40 ... STEP #3 Create package element in diagram 1. …
5.2. Hardware Stalling - csl.cornell.edu
5. Pipeline Hazards: RAW Data Hazards 5.2. Hardware Stalling 5.2. Hardware Stalling Hardware includes control logic that freezes later instructions (in front of pipeline) until earlier instruction …
DYNAMIC SPEED CONTROL IN HIGH VELOCITY PIPELINES
pressure is required to kick the SCP into the pipeline. In this pipeline section the SCP was configured to try and control its speed to between 6-8 miles per hour. 3.4.2 SCP Speed and …
Pig Signallers - iNPIPE PRODUCTS™
Supplier of pipeline maintenance and testing equipment iNPIPE PRODUCTSTM standard valve options Important note: Valve specifications for pig signallers installed on pipelines, launchers …
Based on AutoCAD software and using AutoLisp …
Abstract—A pipeline diagram is an engineering ... device is called a piping diagram. It is an important tool in system design, helping engineers plan and ... programs to process and …
Direct Current Voltage Gradient Survey - Allied Corrosion
miss. All DCVG indications are electronically recorded with sub-meter accurate GPS location data using the Trimble GEO-HR GPS / Data Logger. DCVG applications: • One of the indirect …
Oracle Data Integrator Best Practices for a Data Warehouse
This document is intended for Data Integration Professional Services, System Integrators and IT teams that plan to use Oracle Data Integrator (ODI) as the Extract, Load and Transform tool in …
giotto-tda: A Topological Data Analysis Toolkit for Machine …
as an unweighted graph (or, more generally, as a simplicial complex). It is primarily used as a data visualization tool to explore substructures of interest in data. In giotto-tda, this algorithm is …
Cloud Data Governance and Catalog - Informatica
governance, data catalog and data quality capabilities into a singular tool for automating data intelligence insights. This IDMC service is built for organizations ... for example, if a data …
This document was downloaded from the Penspen Integrity …
years. Consequently, pigs are now an integral part of most pipeline operator’s integrity management programs, and their use and importance will increase. USING SMART PIG …
PODS Data Management - Pipeline and Hazardous …
Pipeline Spatial Data Model Family Tree 14 Event Feature Class Geometry Join Event Based Standards are like toothbrushes. Everybody wants one but nobody wants to use anybody …
Smart Data Integration (SDI) - SAP Online Help
where data from Smart Data Integration (SDI) is placed dur-ing export. Data is validated in the staging area before trans-ferring into the Incentive Management tables and executing the …
Data Sheet: PipelineStudio - Emerson
liquid and gas pipeline networks. Interruptions to online production create delays and increase operating costs. PipelineStudio is the industry-leading pipeline design and engineering …
Guide to Implementing DevSecOps for a Systems of …
Table 2: Roles, Responsibilities, and Pipeline Interactions 27 Table 3: Readiness and Fit Analysis Assumptions for DevSecOps 30 Table 4: Typical Transmission Mechanisms by Adoption …
Blocked Ingestion Pipeline Queues with How to Troubleshoot …
Identifying the queue responsible for blocked ingestion pipeline Remember the order of queues in the pipeline. Parsing/Aggregation queues are blocked due to Typing queue.
25. MIPS Pipeline - Pacific University
MIPS Pipelining A set of registers (IF/ID, ID/EX, EX/MEM, MEM/WB) is placed between each pipe stage 1. used to save instruction state as it propagates through the pipe 2. instructions are …
Pipeline: Introduction - Indian Institute of Information …
Speedup from pipeline = Average instruction time unpiplined/Average instruction time pipelined Consider a case for k-segment pipeline with a clock cycle time tp to execute n tasks. The first …
1H - HDD Construction Process - Notes - v5
Feb 1, 2018 · tool is attached. The reaming tool is then rotated and pulled back toward the drill rig while drilling fluid is pumped downhole through the drill pipe string. Sections (joints) of drill pipe …
