In November 2014, an especially chilling cyberattack shook the corporate world—something that went far beyond garden-variety theft of credit card numbers from a big-box store. Hackers, having explored the internal servers of Sony Pictures Entertainment, captured internal financial reports, top executives’ embarrassing e-mails, private employee health data, and even unreleased movies and scripts and dumped them on the open Web.
It seems logical that any business, whether a commercial enterprise or a not-for-profit business, would understand that building a secure organization is important to longterm success. When a business implements and maintains a strong security posture, it can take advantage of numerous benefits. An organization that can demonstrate an infrastructure protected by robust security mechanisms can potentially see a reduction in insurance premiums. A secure organization can use its security program as a marketing tool, demonstrating to clients that it values their business so much that it takes a very aggressive stance on protecting their information. But most importantly, a secure organization will not have to spend time and money identifying security breaches and responding to the results of those breaches.
Business Intelligence (Data Science for Business)
Business intelligence (BI) is an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance. BI is the set of techniques and tools for the transformation of raw data into meaningful and useful information for business analysis purposes. BI technologies are capable of handling large amounts of unstructured data to help identify, develop and otherwise create new strategic business opportunities.
BI systems and data became networked – and so, BI solutions that viewed sub-apps and data as a virtualized whole, existing in the Internet cloud or clouds, began to make sense – the so-called cloud BI which has become its own topic area: Big Data. One of the main areas contributing to the analysis of Big Data (BD) is data science (DS). Although this term has emerged only recently, it has a long history, as it is based on techniques and theories from fields, such as mathematics, statistics, machine learning, data engineering, etc. The integration of these fields into the Big Data paradigm has resulted in a new study area called Big Data Science (BDS).
A distributed systems is one in which components located at networked computers communicate and coordinate their actions only by passing messages. This definition leads to the following characteristics of distributed systems:
- Concurrency of components,
- Lack of global clock and,
- Independent failures of components.
The challenges arising from the construction of distributed systems are the heterogeneity of their components, openness (which allows components to be added or replaced), security, scalability – the ability to work well when the load or the number of users increases – failure handling, concurrency of components, transparency and providing quality of service (Coulouris).
Coud Computing & Big Data
Cloud Computing (CC) is a paradigm for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable cloud resources accessed through services which can be rapidly provisioned and released with minimal management effort or service provider interaction.
One of the most important challenges in CC is how to process large amounts of data (also known as Big Data – BD) in an efficient and reliable way. In December 2012, the International Data Corporation (IDC) stated that, by the end of 2012, the total data generated was 2.8 Zettabytes (ZB) (2.8 trillion Gigabytes) . Further-more, the IDC predicts that the total data generated by 2020 will be 40 ZB. This is roughly equivalent to 5.2 terabytes (TB) of data generated by every human being alive in that year. In addition, according to the report, only 0.5% of the data have been analyzed up to the present time, and one-quarter of all the currently available data may contain valuable information. This means that Big Data (BD) processing will be a highly relevant topic in the coming years.
Development of Cloud Computing Applications (DCCA)
Cloud Computing (CC) is defined by ISO as the paradigm for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable cloud resources accessed through services which can be rapidly provisioned and released with minimal management effort or service provider interaction.
Cloud services are categorized into three service models: 1) Infrastructure as a Service (IaaS), 2: Platform as a Service (PaaS), and 3: Software as a Service (SaaS). Cloud-based development platforms in PaaS and IaaS public clouds — such as Google, Amazon Web Services, Microsoft, and Salesforce.com — are really in their awkward teenage years, but they show cost savings and better efficiencies.
The main goal of this course is to learn to desing, develop and implant Cloud Computing Applicatios. The above by means of new development frameworks and programming languages.
Distributed Data Base Systems (DDBS)
DDBS technology is the union of what appear to be two diametrically opposed approaches to data processing: database system and computer network technologies. A distributed database is a collection of multiple, logically interrelated databases distributed over a computer network. A DDBS is then defined as the software system that permits the management of the distributed databases and makes the distribution transparent to the users.