The most important factors that determine the correct operation of modern automation, electronics, industrial computers and other devices are the correct power supply and the appropriate climatic conditions. These are necessary to ensure ongoing operation of priority infrastructure equipment – server room, data communication, power engineering.
This paper discusses making realistic improvements to power, cooling, racks, physical security, monitoring, and lighting. The focus of this paper is on small server rooms and branch offices with up to 10kW of IT load.
Hello, greetings, and welcome to Conversational Unified
Communications. I’m Brien Posey. For those of you who don’t
know me, I am a long time Conversational Geek author, and
17-time Microsoft MVP. My professional background is in both
IT and in commercial astronautics. It’s an odd combination for
sure. I sometimes find myself setting up virtual machines on
one day, and being strapped into a space capsule the next day
(seriously). Thankfully, Peter, Nick, and the rest of the folks at
Conversational Geek have embraced my unorthodox (dare I say
eccentric) career choices and have allowed me to author books
on subjects ranging from the cloud computing to real life
rocket science. I would like to personally invite you to go to
ConversationalGeek.com and check out some of these other
In this book, I want to talk about unified communications.
Those of you who follow my work may recall that back in 2009 I
wrote another book about unified communications called Brien
Posey’s Guide to Pract
Regardless of whether your data resides on-premises, in the cloud, or a
combination of both, you are vulnerable to security threats, data breaches,
data loss, and more. Security is often cited as a concern for organizations
who are migrating to the public cloud, but the belief that the public cloud
is not secure is a myth. In fact, the leading public cloud service providers
have built rigorous security capabilities to ensure that your applications,
assets, and services are protected. Security in the public cloud is now
becoming a driver for many organizations, but in a rapidly evolving
multicloud environment, you must keep up with changes that might
impact your security posture.
This eBook outlines the three core recommendations for cloud security
across Amazon Web Services (AWS), Microsoft Azure, and Google
Both the speed of innovation and the uniqueness of cloud technology is
forcing security teams everywhere to rethink classic security concepts
and processes. In order to keep their cloud environment secure,
businesses are implementing new security strategies that address the
distributed nature of cloud infrastructure.
Security in the cloud involves policies, procedures, controls, and
technologies working together to protect your cloud resources, which
includes stored data, deployed applications, and more. But how do you
know which cloud service provider offers the best security services? And
what do you do if you’re working on improving security for a hybrid or
This ebook provides a security comparison across the three main public
cloud providers: Amazon Web Services (AWS), Microsoft Azure, and
Google Cloud Platform (GCP). With insight from leading cloud experts,
we also analyze the differences between security in the cloud and
on-premises infrastructure, debunk
Published By: Tricentis
Published Date: Aug 19, 2019
Think back just 5 years ago. In 2014…
• The seminal DevOps book—Gene Kim’s The Phoenix Project—was one year old
• Gartner predicted that 25% of Global 2000 enterprises would adopt DevOps to some extent by 20161
• "Continuous Testing” just started appearing in industry publications and conferences2
• Many of today’s popular test frameworks were brand new—or not yet released
• The term “microservices” was just entering our lexicon
• QC/UFT and ALM were still sold by HP (not even HPE yet)
• Only 30% of enterprise software testing was performed fully “in house”3
• There was no GDPR restricting the use of production data for software testing
• Packaged apps were typically updated on an annual or semi-annual basis and modern platforms like
SAP S/4HANA and Salesforce Lightning hadn’t even been announced
Times have changed—a lot. If the way that you’re testing hasn’t already transformed dramatically, it will soon.
And the pace and scope of disruption will continue to escalate throughout the fo
Published By: Tricentis
Published Date: Aug 19, 2019
The way that we develop and deliver software has changed dramatically in the
past 5 years—but the metrics we use to measure quality remain largely the
same. Despite seismic shifts in business expectations, development methodologies,
system architectures, and team structures, most organizations still
rely on quality metrics that were designed for a much different era.
Every other aspect of application delivery has been scrutinized and optimized
as we transform our processes for DevOps. Why not put quality metrics under
the microscope as well?
Are metrics like number of automated tests, test case coverage, and pass/fail
rate important in the context of DevOps, where the goal is immediate insight
into whether a given release candidate has an acceptable level of risk? What
other metrics can help us ensure that the steady stream of updates don’t undermine
the very user experience that we’re working so hard to enhance?
To provide the DevOps community an objective perspective
on what quality
Digital business initiatives have expanded in scope and complexity as companies have increased the rate of digital innovation to capture new market opportunities. As applications built using fine-grained microservices and functions become pervasive, many companies are seeing the need to go beyond traditional API management to execute new architectural patterns and use cases.
APIs are evolving both in the way they are structured and in how they are used, to not only securely expose data to partners, but to create ecosystems of internal and/or third-party developers.
In this datasheet, learn how you can use TIBCO Cloud™ Mashery® to:
Create an internal and external developer ecosystem
Secure your data and scale distribution
Optimize and manage microservices
Expand your partner network
Run analytics on your API performance
This paper explores the merits of agile integration architecture--a container-based, decentralized, and microservices-aligned approach for integration solutions that meets the demands of agility, scalability, and resilience required by digital transformation.
In Forrester’s evaluation of the emerging market for conversational computing platforms, we identified the seven most significant providers — Amazon, Google, IBM, Microsoft, Nuance Communications, Oracle, and Rulai — in the category and evaluated them. This report details our findings about how each vendor scored against nine criteria and where they stand in relation to each other. Application developers should use this review to select the right partners for their conversational computing platform needs.
Infinidat® enterprise storage solutions are based upon the unique and patented Infinidat storage architecture—A fully abstracted set of Software-Defined Storage (SDS) functions integrated with the best-of-breed off-the-shelf commodity hardware. Infinidat’s software-focused architecture, an evolution and revolution in data management design over 30 years in the making, solves the conflicting requirements of bigger, faster, and less expensive. This paper discusses the technology that enables Infinidat to be the only enterprise storage provider that achieves multi-petabyte capacity with faster than all-flash performance (over 1.3M IOPS at microsecond latency) and an unprecedented seven-nines availability, all at the lowest Total Cost of Ownership (TCO).
Over the last decade, the enterprise analytics landscape has dramatically
transformed. Vendors have come and gone, and platforms have
continually expanded their offerings to include new functionality and
keep pace with the demands of the businesses they serve. Originally
envisioned as an IT-centric tool for enterprise reporting, analytics today
has evolved into a business solution—empowering a range of users
across every line of business, including front-line employees, field
personnel, and executives.
The rise of self-service analytics over the past decade has played a key role in promoting a data-driven mindset
within every business function. However, this practice is limited to a skilled few. The vast majority of business
professionals lack the time, analytical skills, or inclination to conduct their own analyses, and fail to effectively use
analytics on a day-to-day basis. The result? Despite decades of investments, BI adoption at most organizations
remains at 30%.
The failure of e
With trends such as big data, artificial intelligence and IoT dominating
today’s business technology headlines, the buzz around enterprise
mobility (now considered known and familiar) may have lessened, but
its importance has not. Consider its key role in today’s top tech trends,
and then also consider this. According to an Oxford Economics survey
of 500 senior IT executives, CEOs, and other senior managers, 80%
say workers cannot do their jobs effectively without a mobile
device. The same survey shows:
82% say mobile devices are critical to employee productivity
82% say mobile devices are critical to agility and the speed of decision making
76% say mobile devices are critical to customer service and satisfaction
75% say mobile devices are critical to innovation and collaboration
70% say mobile devices are critical to employee satisfaction
and 70% say mobile devices are critical to revenue growth.
The known and familiar now demands a fresh look and focus from
enterprise organizations a
Today, despite massive investments in data, IT infrastructure,
and analytics software, the adoption of analytics continues to lag
behind. In fact, according to Gartner, most organizations fail to hit
the 30% mark. That means that more than 70% of people at most
organizations are going without access to the critical information
they need to perform to the best of their abilities.
What’s stopping organizations from breaking through the 30%
barrier and driving the pervasive adoption of intelligence? Simple.
The majority of existing tools only cater to users who are naturally
analytically inclined—the analysts, data scientists, and architects
of the world. The other 70%—the people making the operational
decisions daily within a business—simply lack the time, skill, or
desire to seek out data and intelligence on their own.
HyperIntelligence helps organizations operationalize their
existing investments and arm everyone across the organization
with intelligence. Whether
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