Products related to Data:
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Managing Cloud Native Data on Kubernetes : Architecting Cloud Native Data Services Using Open Source Technology
Kubernetes has become the primary platform for deploying and managing cloud native applications.But because it was originally designed for stateless workloads, working with data on Kubernetes has been challenging.If you want to avoid the inefficiencies and duplicative costs of having separate infrastructure for applications and data, this practical guide can help. Using Kubernetes as your platform, you'll discover open source technologies that are designed and built for the cloud.Delve into case studies to avoid the pitfalls others have faced and explore new use cases.Get an insider's view of what's coming from the innovators who are creating next-generation architectures and infrastructure. And you'll learn how to: Manage different data use cases on Kubernetes Reduce costs and simplify application development Leverage data and infrastructure to create new use cases and business models Make data infrastructure choices that are cost-efficient, secure, scalable, and elastic And more
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Cloud Data Architectures Demystified : Gain the expertise to build Cloud data solutions as per the organization's needs
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The Enterprise Data Catalog : Improve Data Discovery, Ensure Data Governance, and Enable Innovation
Combing the web is simple, but how do you search for data at work?It's difficult and time-consuming, and can sometimes seem impossible.This book introduces a practical solution: the data catalog.Data analysts, data scientists, and data engineers will learn how to create true data discovery in their organizations, making the catalog a key enabler for data-driven innovation and data governance. Author Ole Olesen-Bagneux explains the benefits of implementing a data catalog.You'll learn how to organize data for your catalog, search for what you need, and manage data within the catalog.Written from a data management perspective and from a library and information science perspective, this book helps you:Learn what a data catalog is and how it can help your organizationOrganize data and its sources into domains and describe them with metadataSearch data using very simple-to-complex search techniques and learn to browse in domains, data lineage, and graphsManage the data in your company via a data catalogImplement a data catalog in a way that exactly matches the strategic priorities of your organizationUnderstand what the future has in store for data catalogs
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Discovering Computers: Digital Technology, Data, and Devices
DISCOVERING COMPUTERS: DIGITAL TECHNOLOGY, DATA, AND DEVICES, 17th edition, teaches you not only the basics of technology, but also how you will use it -- and the responsibilities that go along with being a digital citizen.Focusing on current technology, the content addresses convergence of devices and platforms.Each module integrates practical how-to tips, ethics issues and security topics, while Consider This boxes woven throughout help you sharpen your critical-thinking skills.A variety of end-of-module activities -- checkpoint questions, small group activities and problem-solving exercises -- enable you to put what you learn into practice.MindTap digital learning solution is also available.Using an inviting approach that ensures understanding, DISCOVERING COMPUTERS equips you with the information you need for success at home, school and work.
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What is the question about data protection and data security?
The question about data protection and data security revolves around how organizations can safeguard sensitive information from unauthorized access, breaches, and cyber threats. It also involves ensuring compliance with data protection regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Additionally, the question may address best practices for data encryption, access controls, regular security audits, and incident response plans to mitigate risks and protect data integrity.
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What is the difference between data protection and data security?
Data protection refers to the policies and procedures put in place to ensure that personal data is handled in compliance with privacy laws and regulations. This includes obtaining consent for data collection, limiting access to personal information, and ensuring data is accurate and up to date. On the other hand, data security focuses on protecting data from unauthorized access, use, or destruction. This includes measures such as encryption, firewalls, and access controls to prevent data breaches and cyber attacks. In summary, data protection is about the legal and ethical handling of personal data, while data security is about the technical measures used to protect that data from unauthorized access or misuse.
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"May cloud providers delete my data?"
Yes, cloud providers have the ability to delete your data. This can happen if you violate the terms of service, fail to pay for the service, or if the provider experiences a technical issue that results in data loss. It's important to carefully review the terms of service and understand the provider's data retention and deletion policies to ensure that your data is protected. Additionally, it's a good practice to regularly back up your data to mitigate the risk of data loss.
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Can cloud providers delete my data?
Yes, cloud providers have the ability to delete your data. When you store data with a cloud provider, you are essentially entrusting them with the responsibility of managing and maintaining that data. This means that they have the authority to delete your data if necessary, such as in the case of a violation of their terms of service or if you fail to pay for the service. It's important to carefully review the terms of service and understand the provider's data retention policies to ensure that your data is protected. Additionally, it's a good practice to regularly back up your data to prevent any potential loss.
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Foundations for Architecting Data Solutions : Managing Successful Data Projects
Big Data Solution Architecture provides everyone from CIOs and COOs to lead architects and lead developers with the fundamental concepts of big data development.Authors Ted Malaska and Jonathan Seidman guide you through all the major components necessary to start, architect, and develop successful big data projects.This practical book covers a variety of different big data architectures and applications, from massive data pipelines to web scale applications.Each chapter addresses a different part of the software development life cycle and identifies patterns that build on one another to maximize success throughout the life of your project.You’ll learn how to: Build a Big Data center of excellence in your company for the first time Identify and manage risk in your data project Retain and motivate teams to increase engagement and innovation Maximize Big Data ROI and align cost structure to help your company attain success
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Snowflake Cookbook : Techniques for building modern cloud data warehousing solutions
Develop modern solutions with Snowflake's unique architecture and integration capabilities; process bulk and real-time data into a data lake; and leverage time travel, cloning, and data-sharing features to optimize data operationsKey FeaturesBuild and scale modern data solutions using the all-in-one Snowflake platformPerform advanced cloud analytics for implementing big data and data science solutionsMake quicker and better-informed business decisions by uncovering key insights from your dataBook DescriptionSnowflake is a unique cloud-based data warehousing platform built from scratch to perform data management on the cloud.This book introduces you to Snowflake's unique architecture, which places it at the forefront of cloud data warehouses. You'll explore the compute model available with Snowflake, and find out how Snowflake allows extensive scaling through the virtual warehouses.You will then learn how to configure a virtual warehouse for optimizing cost and performance.Moving on, you'll get to grips with the data ecosystem and discover how Snowflake integrates with other technologies for staging and loading data. As you progress through the chapters, you will leverage Snowflake's capabilities to process a series of SQL statements using tasks to build data pipelines and find out how you can create modern data solutions and pipelines designed to provide high performance and scalability.You will also get to grips with creating role hierarchies, adding custom roles, and setting default roles for users before covering advanced topics such as data sharing, cloning, and performance optimization. By the end of this Snowflake book, you will be well-versed in Snowflake's architecture for building modern analytical solutions and understand best practices for solving commonly faced problems using practical recipes. What you will learnGet to grips with data warehousing techniques aligned with Snowflake's cloud architectureBroaden your skills as a data warehouse designer to cover the Snowflake ecosystemTransfer skills from on-premise data warehousing to the Snowflake cloud analytics platformOptimize performance and costs associated with a Snowflake solutionStage data on object stores and load it into SnowflakeSecure data and share it efficiently for accessManage transactions and extend Snowflake using stored proceduresExtend cloud data applications using Spark ConnectorWho this book is forThis book is for data warehouse developers, data analysts, database administrators, and anyone involved in designing, implementing, and optimizing a Snowflake data warehouse.Knowledge of data warehousing and database and cloud concepts will be useful.Basic familiarity with Snowflake is beneficial, but not necessary.
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Designing Cloud Data Platforms
Centralized data warehouses, the long-time defacto standard for housing data for analytics, are rapidly giving way to multi-faceted cloud data platforms.Companies that embrace modern cloud data platforms benefit from an integrated view of their business using all of their data and can take advantage of advanced analytic practices to drive predictions and as yet unimagined data services. Designing Cloud Data Platforms is an hands-on guide to envisioning and designing a modern scalable data platform that takes full advantage of the flexibility of the cloud.As you read, you’ll learn the core components of a cloud data platform design, along with the role of key technologies like Spark and Kafka Streams.You’ll also explore setting up processes to manage cloud-based data, keep it secure, and using advanced analytic and BI tools to analyse it. about the technologyAccess to affordable, dependable, serverless cloud services has revolutionized the way organizations can approach data management, and companies both big and small are raring to migrate to the cloud.But without a properly designed data platform, data in the cloud can remain just as siloed and inaccessible as it is today for most organizations. Designing Cloud Data Platforms lays out the principles of a well-designed platform that uses the scalable resources of the public cloud to manage all of an organization's data, and present it as useful business insights. about the bookIn Designing Cloud Data Platforms, you’ll learn how to integrate data from multiple sources into a single, cloud-based, modern data platform.Drawing on their real-world experiences designing cloud data platforms for dozens of organizations, cloud data experts Danil Zburivsky and Lynda Partner take you through a six-layer approach to creating cloud data platforms that maximizes flexibility and manageability and reduces costs.Starting with foundational principles, you’ll learn how to get data into your platform from different databases, files, and APIs, the essential practices for organizing and processing that raw data, and how to best take advantage of the services offered by major cloud vendors.As you progress past the basics you’ll take a deep dive into advanced topics to get the most out of your data platform, including real-time data management, machine learning analytics, schema management, and more. what's inside The tools of different public cloud for implementing data platformsBest practices for managing structured and unstructured data setsMachine learning tools that can be used on top of the cloudCost optimization techniques about the readerFor data professionals familiar with the basics of cloud computing and distributed data processing systems like Hadoop and Spark. about the authors Danil Zburivsky has over 10 years experience designing and supporting large-scale data infrastructure for enterprises across the globe. Lynda Partner is the VP of Analytics-as-a-Service at Pythian, and has been on the business side of data for over 20 years.
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The Cloud Data Lake : A Guide to Building Robust Cloud Data Architecture
More organizations than ever understand the importance of data lake architectures for deriving value from their data.Building a robust, scalable, and performant data lake remains a complex proposition, however, with a buffet of tools and options that need to work together to provide a seamless end-to-end pipeline from data to insights. This book provides a concise yet comprehensive overview on the setup, management, and governance of a cloud data lake.Author Rukmani Gopalan, a product management leader and data enthusiast, guides data architects and engineers through the major aspects of working with a cloud data lake, from design considerations and best practices to data format optimizations, performance optimization, cost management, and governance. Learn the benefits of a cloud-based big data strategy for your organizationGet guidance and best practices for designing performant and scalable data lakesExamine architecture and design choices, and data governance principles and strategiesBuild a data strategy that scales as your organizational and business needs increaseImplement a scalable data lake in the cloudUse cloud-based advanced analytics to gain more value from your data
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How to calculate the amount of data in digital video data?
To calculate the amount of data in digital video data, you need to consider the resolution, frame rate, and bit depth of the video. First, calculate the total number of pixels in each frame by multiplying the width by the height of the video resolution. Then, multiply this by the number of frames per second to get the total number of pixels per second. Finally, multiply this by the bit depth (usually 8 bits per color channel) to get the total amount of data per second in bits.
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How do you calculate the amount of data in digital video data?
The amount of data in digital video data can be calculated by multiplying the video resolution (width x height in pixels), the bit depth (number of bits used to represent each color channel), the frame rate (number of frames per second), and the duration of the video in seconds. This will give you the total amount of data in bits. To convert this to a more commonly used unit, such as megabytes or gigabytes, you can divide the total amount of data in bits by 8 to get the amount in bytes, and then divide by 1024 multiple times to convert to larger units.
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What are examples of data security?
Examples of data security measures include encryption, which involves converting data into a code that can only be accessed with the correct key; access controls, which limit who can view or modify data; firewalls, which protect networks from unauthorized access; and regular data backups to ensure that information can be restored in the event of a security breach or data loss. Additionally, strong password policies, multi-factor authentication, and security training for employees are also important components of data security.
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What is data protection and security?
Data protection and security refer to the measures and practices put in place to safeguard data from unauthorized access, use, or disclosure. This includes protecting data from cyber threats such as hacking, malware, and phishing attacks, as well as ensuring compliance with privacy regulations and laws. Data protection and security also involve implementing encryption, access controls, and regular data backups to prevent data loss and maintain the integrity of sensitive information. Overall, data protection and security are essential for maintaining the confidentiality, integrity, and availability of data.
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