IT Infrastructure
Definition:
IT infrastructure refers to the composite hardware, software, network resources and services required for the existence, operation and management of an enterprise IT environment. It allows an organization to deliver IT solutions and services to its employees, partners and/or customers and is usually internal to an organization and deployed within owned facilities.
In a nutshell:
IT infrastructure consists of all components that somehow play a role in overall IT and IT-enabled operations. It can be used for internal business operations or developing customer IT or business solutions.
Typically, a standard IT infrastructure consists of the following components:
- Hardware: Servers, computers, data centers, switches, hubs and routers, etc.
- Software: Enterprise resource planning (ERP), customer relationship management (CRM), productivity applications and more.
- Network: Network enablement, Internet connectivity, firewall and security.
- Meatware: Human users, such as network administrators (NA), developers, designers and generic end users with access to any IT appliance or service are also part of an IT infrastructure, specifically with the advent of user-centric IT service development.
DevOps
What is DevOps?
DevOps is a term for a group of concepts that, while not all new, have catalyzed into a movement and are rapidly spreading throughout the technical community. Like any new and popular term, people have somewhat confused and sometimes contradictory impressions of what it is. Here’s my take on how DevOps can be usefully defined; I propose this definition as a standard framework to more clearly discuss the various issues DevOps covers. Like “Quality” or “Agile,” DevOps is a large enough concept that it requires some nuance to fully understand.
Definition of DevOps
DevOps is a new term emerging from the collision of two major related trends. The first was also called “agile system administration” or “agile operations”; it sprang from applying newer Agile and Lean approaches to operations work. The second is a much expanded understanding of the value of collaboration between development and operations staff throughout all stages of the development lifecycle when creating and operating a service, and how important operations has become in our increasingly service-oriented world (cf. Operations: The New Secret Sauce).
One definition Jez Humble explained to me is that DevOps is “a cross-disciplinary community of practice dedicated to the study of building, evolving and operating rapidly-changing resilient systems at scale.”
That’s good and meaty, but it may be a little too esoteric and specific to Internet startup types. I believe that you can define DevOps more practically as
DevOps is the practice of operations and development engineers participating together in the entire service lifecycle, from design through the development process to production support.
A primary corollary to this is that part of the major change in practice from previous methods is
DevOps is also characterized by operations staff making use many of the same techniques as developers for their systems work.
Those techniques can range from using source control to testing to participating in an Agile development process.
For this purpose, “DevOps” doesn’t differentiate between different sysadmin sub-disciplines – “Ops” is a blanket term for systems engineers, system administrators, operations staff, release engineers, DBAs, network engineers, security professionals, and various other subdisciplines and job titles. “Dev” is used as shorthand for developers in particular, but really in practice it is even wider and means “all the people involved in developing the product,” which can include Product, QA, and other kinds of disciplines.
DevOps has strong affinities with Agile and Lean approaches. The old view of operations tended towards the “Dev” side being the “makers” and the “Ops” side being the “people that deal with the creation after its birth” – the realization of the harm that has been done in the industry of those two being treated as siloed concerns is the core driver behind DevOps. In this way, DevOps can be interpreted as an outgrowth of Agile – agile software development prescribes close collaboration of customers, product management, developers, and (sometimes) QA to fill in the gaps and rapidly iterate towards a better product – DevOps says “yes, but service delivery and how the app and systems interact are a fundamental part of the value proposition to the client as well, and so the product team needs to include those concerns as a top level item.” From this perspective, DevOps is simply extending Agile principles beyond the boundaries of “the code” to the entire delivered service.
source: TheAgileAdmin
Business Intelligence
Definition
Knowing more about your business—and knowing it faster than others—is the best way to power innovation and gain an edge on your competition.
Business intelligence (BI) is a set of theories, methodologies, architectures, and technologies that transform raw data into meaningful and useful information for business purposes. BI can handle enormous amounts of unstructured data to help identify, develop and otherwise create new opportunities. BI, in simple words, makes interpreting voluminous data friendly. Making use of new opportunities and implementing an effective strategy can provide a competitive market advantage and long-term stability.
Generally, Business Intelligence is made up of an increasing number of components. These include the following:
Multidimensional aggregation and allocation
Denormalization, tagging and standardization
Realtime reporting with analytical alert
Interface with unstructured data source
Group consolidation, budgeting and rolling forecast
Statistical inference and probabilistic simulation
Key performance indicators optimization
Version control and process management
Open item management
BI technologies provide historical, current and predictive views of business operations. Common functions of business intelligence technologies are reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics and prescriptive analytics.
Though the term business intelligence is sometimes a synonym for competitive intelligence (because they both support decision making), BI uses technologies, processes, and applications to analyze mostly internal, structured data and business processes while competitive intelligence gathers, analyzes and disseminates information with a topical focus on company competitors. If understood broadly, business intelligence can include the subset of competitive intelligence.