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Books > Computing & IT > Computer software packages > Other software packages > Enterprise software > General
Because it continually implements entrepreneurial creativity and
innovative business models, the economic landscape is ever-changing
in today's globalized world. As consumers become more willing to
accept new strategic trends, this has led to the emergence of
disruptive technologies. Since this equipment has an insufficient
amount of information and high risks, it is necessary to assess the
potential of disruptive technologies in the commercial environment.
Impact of Disruptive Technologies on the Sharing Economy provides
emerging research exploring the theoretical and practical aspects
of disruptive technologies and knowledge-based entrepreneurial
efforts and applications within management, business, and
economics. Featuring coverage on a broad range of topics such as
consumer ethics, corporate governance, and insurance issues, this
book is ideally designed for IT specialists, IT consultants,
software developers, computer engineers, managers, executives,
managing directors, students, professors, scientists,
professionals, industry practitioners, academicians, and
researchers seeking current research on the consequences of
disruptive technologies.
Explore Kinesis managed services such as Kinesis Data Streams,
Kinesis Data Analytics, Kinesis Data Firehose, and Kinesis Video
Streams with the help of practical use cases Key Features Get well
versed with the capabilities of Amazon Kinesis Explore the
monitoring, scaling, security, and deployment patterns of various
Amazon Kinesis services Learn how other Amazon Web Services and
third-party applications such as Splunk can be used as destinations
for Kinesis data Book DescriptionAmazon Kinesis is a collection of
secure, serverless, durable, and highly available purpose-built
data streaming services. This data streaming service provides APIs
and client SDKs that enable you to produce and consume data at
scale. Scalable Data Streaming with Amazon Kinesis begins with a
quick overview of the core concepts of data streams, along with the
essentials of the AWS Kinesis landscape. You'll then explore the
requirements of the use case shown through the book to help you get
started and cover the key pain points encountered in the data
stream life cycle. As you advance, you'll get to grips with the
architectural components of Kinesis, understand how they are
configured to build data pipelines, and delve into the applications
that connect to them for consumption and processing. You'll also
build a Kinesis data pipeline from scratch and learn how to
implement and apply practical solutions. Moving on, you'll learn
how to configure Kinesis on a cloud platform. Finally, you'll learn
how other AWS services can be integrated into Kinesis. These
services include Redshift, Dynamo Database, AWS S3, Elastic Search,
and third-party applications such as Splunk. By the end of this AWS
book, you'll be able to build and deploy your own Kinesis data
pipelines with Kinesis Data Streams (KDS), Kinesis Data Firehose
(KFH), Kinesis Video Streams (KVS), and Kinesis Data Analytics
(KDA). What you will learn Get to grips with data streams,
decoupled design, and real-time stream processing Understand the
properties of KFH that differentiate it from other Kinesis services
Monitor and scale KDS using CloudWatch metrics Secure KDA with
identity and access management (IAM) Deploy KVS as infrastructure
as code (IaC) Integrate services such as Redshift, Dynamo Database,
and Splunk into Kinesis Who this book is forThis book is for
solutions architects, developers, system administrators, data
engineers, and data scientists looking to evaluate and choose the
most performant, secure, scalable, and cost-effective data
streaming technology to overcome their data ingestion and
processing challenges on AWS. Prior knowledge of cloud
architectures on AWS, data streaming technologies, and
architectures is expected.
Harness the power of Salesforce to manage and grow your business.
This book shows you how to use the Salesforce CRM tool to
consolidate consumer data into a single place to gain better
insight into your business and more easily manage data. Data (such
as email, spreadsheets, databases) is generated through the front
office or face of your business, where your company interacts with
customers and revenue is generated. In a hotel, for instance, the
front office is the lobby where guests are greeted, their problems
are handled, and room payments are made. Another example is a
coffee shop, where the front office is an employee taking a
customer's order or serving a drink. Salespeople connect to
customers by selling your company's goods or services. Marketing
team members connect with them through advertising and promotional
activities. Service and support staff assist customers with
problems and provide help with products. This book introduces the
many ways Salesforce-based innovations are transforming the
technology landscape and the strategies that may be used for
designing and launching a digital front office. The book examines
how organizations can launch and grow digital solutions and
strategies for the governance of the platform and provides an
overview of digital transformation across industries. What You Will
Learn Understand basic Salesforce concepts, including the digital
front office process tower, lead to cash journey, core CRM
functions, best practices, and more Review data management
concepts, integrated sales, customer service, marketing operations,
and proposal and business development needs in a systematic way Use
frameworks to build a business architecture and multi-year
technology roadmap Get familiar with Salesforce business processes
and concepts such as account, contact, lead, and opportunity
management; marketing campaigns; master data management (MDM); and
lead scoring, grading, and activity management across the front
office Define and develop digital marketing challenges and strategy
(people, process, brand, messaging, and ROI), measure campaign
data, and create an end-to-end campaign in Salesforce Who This Book
Is For Business executives, C-suites, IT management, and Salesforce
managers and professionals working in IT, business development,
sales operations, program management, marketing operations, and
proposal development
Learn how to extend the capabilities of Power Apps by building code
components using Power Apps Component Framework Key Features
Understand how to extend Power Apps' capabilities Enhance your
skills with the help of practical code components used throughout
the book Overcome common challenges, avoid pitfalls, and improve
your code Book DescriptionPower Apps Component Framework is used by
professional developers to extend the capabilities of model-driven
and canvas apps. Extending Microsoft Power Apps with Power Apps
Component Framework will take you through the basic as well as
advanced topics using practical examples. The book starts by
helping you understand the fundamentals of the framework, its
lifecycle, and the tools that you'll use to build code components
using best practices and file management guidelines. You'll then
learn how to extend Power Apps step by step and apply the
principles and concepts covered in the book to build code
components for field type attributes. The book covers different
ways of debugging code components and guides you through the
process of building code components for datasets. You'll also
explore the functions and methods provided by the framework to
enhance your controls using powerful sets of libraries and
extensions. As you advance, you'll get to grips with creating and
managing authentication profiles, discover different ways of
deploying code components, and configure code components in
model-driven and canvas apps. Finally, you'll learn some of the
important features of the framework and learn modern web
development practices. By the end of this Power Apps book, you'll
be able to build, debug, enrich, and deploy code components
confidently. What you will learn Understand the fundamentals of
Power Apps Component Framework Explore the tools that make it easy
to build code components Build code components for both a field and
a dataset Debug using test harness and Fiddler Implement caching
techniques Find out how to work with the Dataverse Web API Build
code components using React and Fluent UI controls Discover
different deployment strategies Who this book is forThis book is
for developers who are looking to build advanced skills for
extending the capabilities of Power Apps. Basic knowledge of Power
Apps and web development is necessary to get started with this
book.
Prepare to achieve AWS Machine Learning Specialty certification
with this complete, up-to-date guide and take the exam with
confidence Key Features Get to grips with core machine learning
algorithms along with AWS implementation Build model training and
inference pipelines and deploy machine learning models to the
Amazon Web Services (AWS) cloud Learn all about the AWS services
available for machine learning in order to pass the MLS-C01 exam
Book DescriptionThe AWS Certified Machine Learning Specialty exam
tests your competency to perform machine learning (ML) on AWS
infrastructure. This book covers the entire exam syllabus using
practical examples to help you with your real-world machine
learning projects on AWS. Starting with an introduction to machine
learning on AWS, you'll learn the fundamentals of machine learning
and explore important AWS services for artificial intelligence
(AI). You'll then see how to prepare data for machine learning and
discover a wide variety of techniques for data manipulation and
transformation for different types of variables. The book also
shows you how to handle missing data and outliers and takes you
through various machine learning tasks such as classification,
regression, clustering, forecasting, anomaly detection, text
mining, and image processing, along with the specific ML algorithms
you need to know to pass the exam. Finally, you'll explore model
evaluation, optimization, and deployment and get to grips with
deploying models in a production environment and monitoring them.
By the end of this book, you'll have gained knowledge of the key
challenges in machine learning and the solutions that AWS has
released for each of them, along with the tools, methods, and
techniques commonly used in each domain of AWS ML. What you will
learn Understand all four domains covered in the exam, along with
types of questions, exam duration, and scoring Become well-versed
with machine learning terminologies, methodologies, frameworks, and
the different AWS services for machine learning Get to grips with
data preparation and using AWS services for batch and real-time
data processing Explore the built-in machine learning algorithms in
AWS and build and deploy your own models Evaluate machine learning
models and tune hyperparameters Deploy machine learning models with
the AWS infrastructure Who this book is forThis AWS book is for
professionals and students who want to prepare for and pass the AWS
Certified Machine Learning Specialty exam or gain deeper knowledge
of machine learning with a special focus on AWS. Beginner-level
knowledge of machine learning and AWS services is necessary before
getting started with this book.
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