Learning SQL development can seem like an overwhelming task at first. However, mastering just a few key points will help ease your way through 80 percent of the day-to-day challenges when writing stored procedures and solving common problems. Here are three important SQL development factors to keep in mind:

Outer Joins
One of the most crucial things to understand in SQL server are joins. Joins are a way to retrieve data from two or more tables based on logical relationships between them. Joins dictate how Microsoft SQL Server ought to use data from one table to select the rows in another table.

In my experience inner joins are intuitive while outer joins can present additional hours of grief by overlooking associations in the other table(s). The outer join is the key to answering questions about what the database does not have. For example, if you need to make a query to display all the students who are without report-cards, you’ll need a left join to get all students coupled with a “where clause” to return the ones who have nulls for their report card table columns in the results.

Many talented Java script programmers have muddled through the SQL Server by deficient coding around the inner join. As a result, their queries can take five hours to run, whereas, properly written left joins, can take only two seconds to run.

Aggregation
Grouping results comes up in SQL a lot more than you might think. Knowing how to write a query when answering questions such as, “What’s the average grade for each teacher’s student list?” is invaluable. This kind of question cannot be answered with a single table or solely by joins.  You’ll often find you need to use joins in conjunction with group by statements. Always write the raw query first and then look at the results. Next, you have to figure out the best way to group them, rewrite your select clause and add a group by clause in the end.

Digging Through Data
I find this is the most lacking skill in many programmers. In fact, many otherwise-talented programmers holding Master’s Degrees fail to get jobs because they couldn’t analyze rows of data objectively during interviews. It’s just something that’s not taught but is crucial to get under you belt. Why? Eventually, some query is not going to perform as you may expect. And, the only way to find discrepancies is to look at rows of data, identify what join isn’t finding a match or where bad data is throwing things into chaos. Get familiar with how joins actually work, even if you have to manually walk through the logic of a large stored procedure’s tree of joins. It’s boring and time-consuming but absolutely necessary.

Take the time to master the core skills that will make you a successful SQL Programmer and avoid queries that run for five hours!

Controversy was recently courted as Southern California Edison (SCE) prepares to cut their own staff while looking to meet their staffing needs with offshore employees skilled in the field of “IT” or Informational Technology. This has been the second major utility company in the United States to take this path towards providing services to its consumers while holding current rates at consistent levels. SCE does not disclose the exact numbers of expected lay-offs, but the LA Times reports that it is in the hundreds.  Utility companies tell their consumers that these moves are necessary as a hedge against inflation and to keep their services at rates that their customers can easily afford. Critics claim that the use of foreign workers is the first step to using an entirely foreign workforce and promoting large scale unemployment amongst American citizens. Often this has been seen as a conflict between national and international workers for the same jobs, salaries and careers.

It has been noted that this State of California utility company, much like other corporations that hire foreign workers does so primarily when there is a shortage of national citizens that can perform these jobs well. IT workers that are brought in with H-1B Visa work permits usually are college educated and hold expertise in technical areas and studies that local employees may not be especially trained in. Once again, critics decry the fact that these employees are not hired directly. On shore contracting companies operating in the continental United States are directly hired by the utility companies. These contracted companies then serve as “middle-men” and hire a wide range of foreign workers with H-1B paperwork so that they can move to the United States. The workers then perform a variety of jobs instead of American workers who were either born in the country or have achieved American citizenship on their own.

Needless to say, the amount of visas issued in a given year is a concern for U.S workers in various fields but particularly in Information Technology. As large corporations stack the employment deck with foreign workers who put in the hours for a fraction of the pay-rate for local employees, local IT professionals are finding it more difficult to find work nationally.  They encounter rejections, endless interview processes or low –ball offers from companies and recruiting agencies looking to fill positions at a bare minimum cost for coveted skill-sets.  

Meanwhile, an H-1B worker is a worker brought in on a temporary basis with a visa allowing them to work freely in the United States. Much like a student or travel visa, it is issued for on a calendar oriented basis.  Applicants who successfully renew the visa for an extended period of time can expect to work in the United States for up to ten years.  Although U.S companies hiring these employees may pay them less than their local employees, the salaries earned by H-1B Visa workers are almost always higher than these workers would earn in their own country of origin.

Both sides can agree on several issues. When it comes to these H-1B Visa workers, their assignments are generally of a contractual nature and require them to reside in this country for a period of months to years. However it is also an accepted fact that while they are in this country, they are responsible for paying rent, utilities and all other living expenses. As residents of the United States on a permanent basis, they are also liable for taxes on any salary they have earned while living here.

Dr. Norman Matloff, a professor at the University of California, Davis and writer on political matters believes the shortage to be fiction. In his writing for the University of Michigan Journal of Law Reform, he claims that “there has been no shortage of qualified American citizens to fill American computer-related jobs, and that the data offered as evidence of American corporations needing H-1B visas to address labor shortages was erroneous. The American Immigration Lawyers Association (AILA) agrees with him and describes the situation as a crisis. Likewise, other studies from Duke, Alfred P. Sloan Foundation and Georgetown University have disputed that in some years, the number of foreign programmers and engineers imported outnumbered the number of jobs created by the industry

Machine learning systems are equipped with artificial intelligence engines that provide these systems with the capability of learning by themselves without having to write programs to do so. They adjust and change programs as a result of being exposed to big data sets. The process of doing so is similar to the data mining concept where the data set is searched for patterns. The difference is in how those patterns are used. Data mining's purpose is to enhance human comprehension and understanding. Machine learning's algorithms purpose is to adjust some program's action without human supervision, learning from past searches and also continuously forward as it's exposed to new data.

The News Feed service in Facebook is an example, automatically personalizing a user's feed from his interaction with his or her friend's posts. The "machine" uses statistical and predictive analysis that identify interaction patterns (skipped, like, read, comment) and uses the results to adjust the News Feed output continuously without human intervention. 

Impact on Existing and Emerging Markets

The NBA is using machine analytics created by a California-based startup to create predictive models that allow coaches to better discern a player's ability. Fed with many seasons of data, the machine can make predictions of a player's abilities. Players can have good days and bad days, get sick or lose motivation, but over time a good player will be good and a bad player can be spotted. By examining big data sets of individual performance over many seasons, the machine develops predictive models that feed into the coach’s decision-making process when faced with certain teams or particular situations. 

General Electric, who has been around for 119 years is spending millions of dollars in artificial intelligence learning systems. Its many years of data from oil exploration and jet engine research is being fed to an IBM-developed system to reduce maintenance costs, optimize performance and anticipate breakdowns.

Over a dozen banks in Europe replaced their human-based statistical modeling processes with machines. The new engines create recommendations for low-profit customers such as retail clients, small and medium-sized companies. The lower-cost, faster results approach allows the bank to create micro-target models for forecasting service cancellations and loan defaults and then how to act under those potential situations. As a result of these new models and inputs into decision making some banks have experienced new product sales increases of 10 percent, lower capital expenses and increased collections by 20 percent. 

Emerging markets and industries

By now we have seen how cell phones and emerging and developing economies go together. This relationship has generated big data sets that hold information about behaviors and mobility patterns. Machine learning examines and analyzes the data to extract information in usage patterns for these new and little understood emergent economies. Both private and public policymakers can use this information to assess technology-based programs proposed by public officials and technology companies can use it to focus on developing personalized services and investment decisions.

Machine learning service providers targeting emerging economies in this example focus on evaluating demographic and socio-economic indicators and its impact on the way people use mobile technologies. The socioeconomic status of an individual or a population can be used to understand its access and expectations on education, housing, health and vital utilities such as water and electricity. Predictive models can then be created around customer's purchasing power and marketing campaigns created to offer new products. Instead of relying exclusively on phone interviews, focus groups or other kinds of person-to-person interactions, auto-learning algorithms can also be applied to the huge amounts of data collected by other entities such as Google and Facebook.

A warning

Traditional industries trying to profit from emerging markets will see a slowdown unless they adapt to new competitive forces unleashed in part by new technologies such as artificial intelligence that offer unprecedented capabilities at a lower entry and support cost than before. But small high-tech based companies are introducing new flexible, adaptable business models more suitable to new high-risk markets. Digital platforms rely on algorithms to host at a low cost and with quality services thousands of small and mid-size enterprises in countries such as China, India, Central America and Asia. These collaborations based on new technologies and tools gives the emerging market enterprises the reach and resources needed to challenge traditional business model companies.

One of the most anticipated features that came on the iPhone 4S was a new thing called: Siri. Zooming out before concentrating on Siri, mobile assistants were the new rage. Beforehand, people were fascinated by the cloud, and how you could store your files in the Internet and retrieve it from anywhere. You could store your file at home, and get it at your workplace to make a presentation. However, next came virtual assistants. When you’re in the car, it’s hard to send text messages. It’s hard to call people. It’s hard to set reminders that just popped into your head onto your phone. Thus, came the virtual assistant: a new way to be able to talk to your phone to be able to do what you want it to do, and in this case, text message, or call people, and many other features. Apple jumped onto the bandwagon with the iPhone 4S and came out with the new feature: Siri, a virtual assistant that is tailored to assist you in your endeavours by your diction.

Getting started with Siri

To get Siri in the first place, you need an iPhone 4S; although you may have the latest updates on your iPhone 4 or earlier, having an iPhone 4S means you have the hardware that is required to run Siri on your phone. Therefore, if you are interested in using Siri, check into getting an iPhone 4S, as they are getting cheaper every single day.