Memory management is always a priority in pretty much any programming language you would want to use. In the lower level languages such as C, there are a number of functions which help you manage the memory your application uses, but they are not the easiest to use. Some of the more modern programming languages such as Python, Ruby, Perl, and of course the subject of this article, Javascript all have a built in feature called garbage collection.

Garbage collection essentially means that the languages compiler will automatically free the memory being occupied by unused variables and objects, but there is no telling when this could occur. It is purely down to the compiler to decide when the garbage collection process should be initiated.

Over time, companies are migrating from COBOL to the latest standard of C# solutions due to reasons such as cumbersome deployment processes, scarcity of trained developers, platform dependencies, increasing maintenance fees. Whether a company wants to migrate to reporting applications, operational infrastructure, or management support systems, shifting from COBOL to C# solutions can be time-consuming and highly risky, expensive, and complicated. However, the following four techniques can help companies reduce the complexity and risk around their modernization efforts. 

All COBOL to C# Solutions are Equal 

It can be daunting for a company to sift through a set of sophisticated services and tools on the market to boost their modernization efforts. Manual modernization solutions often turn into an endless nightmare while the automated ones are saturated with solutions that generate codes that are impossible to maintain and extend once the migration is over. However, your IT department can still work with tools and services and create code that is easier to manage if it wants to capitalize on technologies such as DevOps. 

Narrow the Focus 

Most legacy systems are incompatible with newer systems. For years now, companies have passed legacy systems to one another without considering functional relationships and proper documentation features. However, a detailed analysis of databases and legacy systems can be useful in decision-making and risk mitigation in any modernization effort. It is fairly common for companies to uncover a lot of unused and dead code when they analyze their legacy inventory carefully. Those discoveries, however can help reduce the cost involved in project implementation and the scope of COBOL to C# modernization. Research has revealed that legacy inventory analysis can result in a 40% reduction of modernization risk. Besides making the modernization effort less complex, trimming unused and dead codes and cost reduction, companies can gain a lot more from analyzing these systems. 

Understand Thyself 

For most companies, the legacy system entails an entanglement of intertwined code developed by former employees who long ago left the organization. The developers could apply any standards and left behind little documentation, and this made it extremely risky for a company to migrate from a COBOL to C# solution. In 2013, CIOs teamed up with other IT stakeholders in the insurance industry in the U.S to conduct a study that found that only 18% of COBOL to C# modernization projects complete within the scheduled period. Further research revealed that poor legacy application understanding was the primary reason projects could not end as expected. 

Furthermore, using the accuracy of the legacy system for planning and poor understanding of the breadth of the influence of the company rules and policies within the legacy system are some of the risks associated with migrating from COBOL to C# solutions. The way an organization understands the source environment could also impact the ability to plan and implement a modernization project successfully. However, accurate, in-depth knowledge about the source environment can help reduce the chances of cost overrun since workers understand the internal operations in the migration project. That way, companies can understand how time and scope impact the efforts required to implement a plan successfully. 

Use of Sequential Files 

Companies often use sequential files as an intermediary when migrating from COBOL to C# solution to save data. Alternatively, sequential files can be used for report generation or communication with other programs. However, software mining doesn’t migrate these files to SQL tables; instead, it maintains them on file systems. Companies can use data generated on the COBOL system to continue to communicate with the rest of the system at no risk. Sequential files also facilitate a secure migration path to advanced standards such as MS Excel. 

Modern systems offer companies a range of portfolio analysis that allows for narrowing down their scope of legacy application migration. Organizations may also capitalize on it to shed light on migration rules hidden in the ancient legacy environment. COBOL to C# modernization solution uses an extensible and fully maintainable code base to develop functional equivalent target application. Migration from COBOL solution to C# applications involves language translation, analysis of all artifacts required for modernization, system acceptance testing, and database and data transfer. While it’s optional, companies could need improvements such as coding improvements, SOA integration, clean up, screen redesign, and cloud deployment.

Big data is now in an incredibly important part of how many major businesses function. Data analysis, or the finding of facts from large volumes of data, helps businesses make many of their important decisions. Companies that conduct business on a national or international scale rely on big data in order to plot the general direction of their business. The concept of big data can be very confusing due to the sheer scale of information involved.  By following a few simple guidelines, even the layman can understand big data and its impacts on everyday life.

What Exactly is Big Data?

Just about everyone can understand the concept of data. Data is information, and information is everywhere in the modern world. Anytime you use any piece of technology you are making use of data. Anytime you read a book, skim the newspaper or listen to music you are also making use of data. Your brain interprets and organizes data constantly from your senses and your thoughts.

Big data, much like its name infers, simply describes this same data on a large sale. The internet allowed the streaming, sharing and collecting of data on a scale never before imaginable and storage technology has allowed ever increasing hoards of data to be accumulated. In order for something to be considered “big data” it must be at least 10 terabytes or more of information. To put that in perspective, consider that 10 terabytes represents the entire printed collection of material in the Library of Congress. What’s even more remarkable is that many businesses work with far more than the minimum 10 terabytes of data. UPS stores over 16 petabytes of data about its packages and customers. That’s 16,000 terabytes or the equivalent to 1,600 printed libraries of congress. The sheer amount of that data is nearly impossible for a human to comprehend, and analysis of this data is only possible with computers.

How do Big Data Companies Emerge?

All of this information comes from everywhere on the internet. The majority of the useful data includes customer information, search engine logs, and entries on social media networks to name a few. This data is constantly generated by the internet at insane rates. Specified computers and software programs are created and operated by big data companies that collect and sort this information. These programs and hardware are so sophisticated and so specialized that entire companies can be dedicated to analyzing this data and then selling it to other companies. The raw data is distilled down into manageable reports that company executives can make use of when handling business decisions.

The Top Five:

These are the five biggest companies, according to Forbes, in the business of selling either raw data reports or analytics programs that help companies to compile their own reports.

1. Splunk
Splunk is currently valued at $186 million.  It is essentially a program service that allows companies to turn their own raw data collections into usable information.

2. Opera Solutions
Opera Solutions is valued at $118 million. It serves as a data science service that helps other companies to manage the raw data that pertains to them. They can offer either direct consultation or cloud-based service.

3. Mu Sigma
Mu Sigma is valued at $114 million.  It is a slightly smaller version of Opera Solutions, offering essentially the same types of services.

4. Palantir
Palantir is valued at $78 million.  It offers data analysis software to companies so they can manage their own raw data analysis.

5. Cloudera
Cloudera is valued at $61 million.  It offers services, software and training specifically related to the Apahce Hadoop-based programs.

The software and services provided by these companies impact nearly all major businesses, industries and products. They impact what business offer, where they offer them and how they advertise them to consumers. Every advertisement, new store opening or creation of a new product is at least somewhat related to big data analysis. It is the directional force of modern business.

Sources:
http://www.sas.com/en_us/insights/big-data/what-is-big-data.html

http://www.forbes.com/sites/gilpress/2013/02/22/top-ten-big-data-pure-plays/

http://www.whatsabyte.com/

Related:

How does Google use Python?

Top Innovative Open Source Projects Making Waves in The Technology World

Is the U.S. the Leading Software Development Country?

How to Keep On Top Of the Latest Trends in Information Technology

As someone who works in many facets of the music industry, I used to seethe with a mixture of anger and jealousy when I would hear people in more “traditional” goods-based industries argue in favor of music content-based piracy. They made all the classic talking points, like “I wouldn’t spend money on this artist normally, and maybe if I like it I’ll spend money on them when they come to town” (which never happened), or “artists are rich and I’m poor, they don’t need my money” (rarely the case), or the worst, “if it were fairly priced and worth paying for, I’d buy it” (not true).  I always wondered if they’d have the same attitude if 63% of the things acquired by customers in their industries weren’t actually paid for, as was conservatively estimated as the case for the music industry in 2009 (other estimations put the figure of pirated music at 95%). Well, we may soon see the answer to curiosities like that. Though one can say with tentative confidence that music piracy is on the decline thanks to services like Spotify and Rdio, it could be looming on the horizon for the entire global, physical supply chain. Yes, I’m talking about 3d printers.

Before I get into the heart of this article, let me take a moment to make one thing clear: I think these machines are incredible. It’s damn near inspiring to think of even a few of their potentially world-changing applications: affordable, perfectly fit prosthetic limbs for wounded servicemen and women; the ability to create a piece of machinery on the spot instead of having to wait for a spare to arrive in the mail, or en route if your car or ship breaks down in a far away place; a company based out of Austin, TX even made a fully functioning firearm from a 3d printer a few months ago.

If these machines become as consumer-friendly and idiot-proof as possible (like computers), it’s possible that in a matter of decades (maybe less), a majority of U.S. households will have their own 3d printer. There’s also the possibility they could take the tech-hobbyist path, one that is much less appealing to the masses. Dale Dougherty of Makezine.com estimates there are currently around 100,000 “personal” 3d printers, or those not owned for business or educational purposes. I don’t think they’ll ever be as ubiquitous as computers, but there are plenty of mechanically inclined, crafty hobbyists out there who would love to play around with a 3d printer if it was affordable enough.

That being said, is there reason to worry about the economic implications of consumers making what they want, essentially for free, instead of paying someone else to produce it? Or will the printers instead be used for unique items more so than replicating and ripping off other companies’ merchandise in mass amounts? The number of people working in industries that would be affected by a development like this is far greater than the number of people who work in content-based industries, so any downturn would probably have a much larger economic implications. Certainly, those times are a ways off, but a little foresightedness never hurt anyone!