mercredi 4 mai 2016

Big Data a definition

Big Data a definition

Big data is an evolving term that describes any voluminous amount of structured, semi-structured and unstructured data that has the potential to be mined for information.
Big data has also been defined by the four Vs: https://www.oracle.com/big-data/index.html

Volume.

The amount of data. While volume indicates more data, it is the granular nature of the data that is unique. Big data requires processing high volumes of low-density, unstructured Hadoop data—that is, data of unknown value, such as Twitter data feeds, click streams on a web page and a mobile app, network traffic, sensor-enabled equipment capturing data at the speed of light, and many more. It is the task of big data to convert such Hadoop data into valuable information. For some organizations, this might be tens of terabytes, for others it may be hundreds of petabytes.

Velocity.

The fast rate at which data is received and perhaps acted upon. The highest velocity data normally streams directly into memory versus being written to disk. Some Internet of Things (IoT) applications have health and safety ramifications that require real-time evaluation and action. Other internet-enabled smart products operate in real time or near real time. For example, consumer eCommerce applications seek to combine mobile device location and personal preferences to make time-sensitive marketing offers. Operationally, mobile application experiences have large user populations, increased network traffic, and the expectation for immediate response.

Variety.

New unstructured data types. Unstructured and semi-structured data types, such as text, audio, and video require additional processing to both derive meaning and the supporting metadata. Once understood, unstructured data has many of the same requirements as structured data, such as summarization, lineage, auditability, and privacy. Further complexity arises when data from a known source changes without notice. Frequent or real-time schema changes are an enormous burden for both transaction and analytical environments.

Value.

Data has intrinsic value—but it must be discovered. There are a range of quantitative and investigative techniques to derive value from data—from discovering a consumer preference or sentiment, to making a relevant offer by location, or for identifying a piece of equipment that is about to fail. The technological breakthrough is that the cost of data storage and compute has exponentially decreased, thus providing an abundance of data from which statistical analysis on the entire data set versus previously only sample. The technological breakthrough makes much more accurate and precise decisions possible. However, finding value also requires new discovery processes involving clever and insightful analysts, business users, and executives. The real big data challenge is a human one, which is learning to ask the right questions, recognizing patterns, making informed assumptions, and predicting behavior.

dimanche 10 avril 2016

These Are the Cities Where Tech Workers Live Largest

USA Today (04/07/16) John Shinal

Annual data released by the U.S. Bureau of Labor Statistics demonstrates the value of an education in the science, technology, engineering, or math fields. Workers employed in computer and math occupations in the cities with the most technology employees earned yearly salaries about 50 percent to 75 percent higher than the overall workforce. Seattle tech workers, for example, had a mean salary of $108,350, or 78 percent more than the $61,000 earned by all workers there. That was the highest tech-worker premium in the 10 largest hubs, followed by Dallas-Fort Worth, Houston, and Austin. The same is true in the burgeoning tech hub of Oakland, CA, where workers in computer and math occupations were paid 70 percent more. Computer and math occupations in Los Angeles, Philadelphia, San Jose, and San Francisco all earn more than 60 percent more than their non-tech counterparts. Although Washington, D.C., is among the largest tech-employing regions, its tech workers had the smallest salary differential, at 54 percent, likely due to the large numbers of federal government workers. Among tech occupations, software developers and systems analysts were the highest in number in nearly all of the largest tech hubs, surpassing computer programmers, network and database administrators, computer research scientists, and computer-support specialists.
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mercredi 6 avril 2016

The Java™ Tutorials : Aggregate Operations

The Java Tutorials are practical guides for programmers who want to use the Java programming language to create applications. They include hundreds of complete, working examples, and dozens of lessons. Groups of related lessons are organized into "trails". (The full tutorials https://docs.oracle.com/javase/tutorial/)

Let us stress this week on

Aggregate Operations:

Prerequisite:  Lambda Expressions and Method References.

Then follow the Aggregate Operations tutorial