Python programming language is general purpose open source programming language. One of its main features is flexibility and ease of use. Python has a variety of useful set of utilities and libraries for data processing and analytical tasks. Currently due to the rise in demand of big data processing python has grown in popularity because its features are easy to use which are core to the processing of huge chunks of information.

Guido Van Rossum, the pioneer of python, introduced python in the year 1980 and then implemented it in 1989. The intention behind the development of python was to make it open source language that can also be used for commercial projects. The fundamental principle of python is to write the code that is easy to use, highly readable and embrace writing fewer lines of code for achieving a particular task. One of the most popular standard libraries which have ready to use tools for performing a various work is Python Package Index. It was introduced in January 2016 and contains more than 72,000 packages for third-party software usage.

Python plays a critical role in linking data to customers. Recently python has found few entry barriers and many people have had access to have experienced the power of python in the past. So, what makes python the best language for big data analytics?

One of the reasons to choose python is that python ecosystem is very vibrant, the ratings at Redmonk are a proof of the strength python community. The Redmonk ranking is based on StackOverflow discussions and contribution made in Github to determine the popularity of programming language on the method used by users to ask questions about Python and the number of the open source projects contributions.

As developers we are overwhelmed with the number of language choices made available to us. It wasn't so long ago that C and it's object oriented sibling C++ where the mainstay of any programmer. Now though we have languages which make certain tasks so easy and simple that we simply cannot afford to ignore them.

In this article we are going to look at the overall differences between Python, Perl and TCL. All formidable and worthy in their own right, but each one has been designed to suit a specific programming need.

1)– Perl is the most mature out of the three languages we are looking at in this article. It was originally designed for processing textual data, and it does so extremely well. Of course Perl has grown over time and can be used for a multitude of different programming scenarios.

A business rule is the basic unit of rule processing in a Business Rule Management System (BRMS) and, as such, requires a fundamental understanding. Rules consist of a set of actions and a set of conditions whereby actions are the consequences of each condition statement being satisfied or true. With rare exception, conditions test the property values of objects taken from an object model which itself is gleaned from a Data Dictionary and UML diagrams. See my article on Data Dictionaries for a better understanding on this subject matter.

A simple rule takes the form:

if condition(s)

then actions.

An alternative form includes an else statement where alternate actions are executed in the event that the conditions in the if statement are not satisfied:

if condition(s)

then actions

else alternate_actions

It is not considered a best prectice to write rules via nested if-then-else statements as they tend to be difficult to understand, hard to maintain and even harder to extend as the depth of these statements increases; in other words, adding if statements within a then clause makes it especially hard to determine which if statement was executed when looking at a bucket of rules. Moreoever, how can we determine whether the if or the else statement was satisfied without having to read the rule itself. Rules such as these are often organized into simple rule statements and provided with a name so that when reviewing rule execution logs one can determine which rule fired and not worry about whether the if or else statement was satisfied. Another limitation of this type of rule processing is that it does not take full advantage of rule inferencing and may have a negative performance impact on the Rete engine execution. Take a class with HSG and find out why.

Rule Conditions

The world of technology moves faster than the speed of light it seems. Devices are updated and software upgraded annually and sometimes more frequent than that.  Society wants to be able to function and be as productive as they can be as well as be entertained “now”.

Software companies must be ready to meet the demands of their loyal customers while increasing their market share among new customers. These companies are always looking to the ingenuity and creativity of their colleagues to keep them in the consumer’s focus. But, who are these “colleagues”? Are they required to be young, twenty-somethings that are fresh out of college with a host of ideas and energy about software and hardware that the consumer may enjoy? Or can they be more mature with a little more experience in the working world and may know a bit more about the consumer’s needs and some knowledge of today’s devices?

Older candidates for IT positions face many challenges when competing with their younger counterparts. The primary challenge that most will face is the ability to prove their knowledge of current hardware and the development and application of software used by consumers. Candidates will have to prove that although they may be older, their knowledge and experience is very current. They will have to make more of an effort to show that they are on pace with the younger candidates.

Another challenge will be marketing what should be considered prized assets; maturity and work experience. More mature candidates bring along a history of work experience and a level of maturity that can be utilized as a resource for most companies. They are more experienced with time management, organization and communication skills as well as balancing home and work. They can quickly become role models for younger colleagues within the company.

Unfortunately, some mature candidates can be seen as a threat to existing leadership, especially if that leadership is younger. Younger members of a leadership team may be concerned that the older candidate may be able to move them out of their position. If the candidate has a considerably robust technological background this will be a special concern and could cause the candidate to lose the opportunity.

Demonstrating that their knowledge or training is current, marketing their experience and maturity, and not being seen as a threat to existing leadership make job hunting an even more daunting task for the mature candidate. There are often times that they are overlooked for positions for these very reasons. But, software companies who know what they need and how to utilize talent will not pass up the opportunity to hire these jewels.

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