Structured Query Language (SQL) is a standardized programming language that is used to manage relational databases and perform various operations on the data in them. Initially created in the 1970s, SQL is regularly used not only by database administrators but also by developers writing data integration scripts and data analysts looking to set up and run analytical queries.
SQL queries and other operations take the form of commands written as statements and are aggregated into programs that enable users to add, modify or retrieve data from database tables.
A table is the most basic unit of a database and consists of rows and columns of data. A single table holds records, and each record is stored in a row on the table. Tables are the most used type of database objects or structures that hold or reference data in a relational database.
Relational databases are relational because they are composed of tables that relate to each other. For example, a SQL database used for customer service can have one table for customer names and addresses and other tables that hold information about specific purchases, product codes, and customer contacts. A table used to track customer contacts usually uses a unique customer identifier called a key or primary key to reference the customer’s record in a separate table used to store customer data, such as name and contact information.
SQL became the de facto standard programming language for relational databases after it emerged in the late 1970s and early 1980s.
SQL standard and proprietary extensions
An official SQL standard was adopted by the American National Standards Institute (ANSI) in 1986, with the International Organization for Standardization (ISO) adopting the standard in 1987. New versions of the SQL standard are published every few years, the most recent in 2016.
ISO/IEC 9075 is the ISO SQL standard developed jointly by ISO and the International Electrotechnical Commission. The standard way of referring to an ISO standard version is to use the standards organizations — ISO/IEC — followed by the ISO standard number, a colon, and the publication year. The current ISO standard for SQL is ISO/IEC 9075:2016.
Both proprietary and open-source RDBMSes built around SQL are available for use by organizations.
Some versions of SQL include proprietary extensions to the standard language for procedural programming and other functions. For example, Microsoft offers a set of extensions called Transact-SQL, while Oracle’s extended version of the standard is Procedural Language for SQL. Commercial vendors offer proprietary extensions to differentiate their product offerings by giving customers additional features and functions. As a result, the different variants of extended SQL offered by vendors are not fully compatible with one another.
SQL commands and syntax
SQL is, fundamentally, a programming language designed for accessing, modifying, and extracting information from relational databases. As a programming language, SQL has commands and syntax for issuing those commands. These are the following:
- Data Definition Language (DDL) commands are also called data definition commands because they are used to define data tables.
- Data Manipulation Language (DML) commands are used to manipulate data in existing tables by adding, changing, or removing data. Unlike DDL commands that define how data is stored, DML commands operate in the tables defined with DDL commands.
- Data Query Language consists of just one command, SELECT, used to get specific data from tables. This command is sometimes grouped with the DML commands.
- Data Control Language commands are used to grant or revoke user access privileges.
- Transaction Control Language commands are used to change the state of some data — for example, to COMMIT transaction changes or to ROLLBACK transaction changes.
What Is Cognitive Computing?
Cognitive computing is the use of computerized models to simulate the human thought process in complex situations where the answers may be ambiguous and uncertain. The phrase is closely associated with IBM’s cognitive computer system, Watson.
Computers are faster than humans at processing and calculating, but they have yet to master some tasks, such as understanding natural language and recognizing objects in an image. Cognitive computing is an attempt to have computers mimic the way a human brain works.
To accomplish this, cognitive computing makes use of artificial intelligence (AI) and other underlying technologies, including the following:
- Expert systems
- Neural networks
- Machine learning
- Deep learning
- Natural language processing (NLP)
- Speech recognition
- Object recognition
Cognitive computing uses these processes in conjunction with self-learning algorithms, data analysis, and pattern recognition to teach computing systems. The learning technology can be used for speech recognition, sentiment analysis, risk assessments, face detection, and more. In addition, it is particularly useful in fields such as healthcare, banking, finance, and retail.
How Does Cognitive Computing Work?
Systems used in the cognitive sciences combine data from various sources while weighing context and conflicting evidence to suggest the best possible answers. To achieve this, cognitive systems include self-learning technologies that use data mining, pattern recognition, and NLP to mimic human intelligence.
Using computer systems to solve the types of problems that humans are typically tasked with requires vast amounts of structured and unstructured data fed to machine learning algorithms. Over time, cognitive systems are able to refine the way they identify patterns and the way they process data. They become capable of anticipating new problems and modeling possible solutions.
For example, by storing thousands of pictures of dogs in a database, an AI system can be taught how to identify pictures of dogs. The more data a system is exposed to, the more it is able to learn and the more accurate it becomes over time.
To achieve those capabilities, cognitive computing systems must have the following attributes:
- Adaptive. These systems must be flexible enough to learn as information changes and as goals evolve. They must digest dynamic data in real time and adjust as the data and environment change.
- Interactive. Human-computer interaction is a critical component of cognitive systems. Users must be able to interact with cognitive machines and define their needs as those needs change. The technologies must also be able to interact with other processors, devices, and cloud platforms.
- Iterative and stateful. Cognitive computing technologies can ask questions and pull in additional data to identify or clarify a problem. They must be stateful in that they keep information about similar situations that have previously occurred.
- Contextual. Understanding context is critical in thought processes. Cognitive systems must understand, identify and mine contextual data, such as syntax, time, location, domain, requirements, and a user’s profile, tasks, and goals. The systems may draw on multiple sources of information, including structured and unstructured data and visual, auditory, and sensor data.
Examples and applications of cognitive computing
Cognitive computing systems are typically used to accomplish tasks that require the parsing of large amounts of data. For example, in computer science, cognitive computing aids in big data analytics, identifying trends and patterns, understanding human language, and interacting with customers.
It is also used alongside HTML and CSS to make websites and web applications. The market for application development in 2022 is huge. Freelancing as a developer or pursuing a full-time job are lucrative options for anyone dedicated and determined to learn programming skills.
- Comment a Lot
Using comments is important when learning any programming language. Comments help make your code more readable and understandable.
In the beginning, you will frequently forget what certain syntax means or what a particular line you wrote does in your code. To save yourself some headaches, write comments about any line that you feel you might forget for later reference. In fact, in the beginning, you should be commenting more than actually writing code.
With time, your grasp of the language will increase and your need to comment on your code will decrease. Eventually, your code will have very few comments or not at all.
- Do Programming Exercises
- Leverage Multiple Resources
- Always Make Documentation for Your Projects
Documentation can include a ‘How to’ that tells how to run the project. You can also include ‘Read Me’ files to tell you what the project does.
The point is to make documentation of all your projects. In the beginning, you will be making really simple documentation that only gives basic information. Later on, you will be adding more and more details.
Documentation will improve your understanding of what you have done. Beginners often follow tutorials and just code along with them. Unfortunately, such practices usually end up with beginners forgetting what they’ve done and not being able to understand their code.
Four Ways AI Can Improve Your Next Meeting
It may not be noticeable to most, but AI is now rooted in many aspects of our lives. From voice assistants to the cars we drive, to social media and shopping – AI is integrated into a multitude of everyday processes.
It should be of little surprise that AI is also becoming heavily embedded in our businesses. And while some people feel uncomfortable about this intersection of human and machine, it truly offers an abundance of transformative opportunities.
Here are four reasons why AI will continue to be important today and in the future:
- Automated note-taking allows brainstorms to go full speed
The days of being the meeting scribe and not absorbing what’s been said around you are over. Automated note-taking and accurate meeting transcripts are one of the simplest ways AI can help free up meeting attendees to focus on the discussion taking place.
Using this software means that transcripts can be searched for important keywords and ideas, allowing participants to fully absorb details after the meeting has concluded. Giving everyone at the meeting the ability to participate without the burden of constant note-taking fosters a lively and uninhibited discussion, encouraging a seamless flow of ideas.
- AI-powered action items, agenda updates, and deadline management
AI technology is founded on rules-based responses to decisions, meaning it can be taught to recognize keywords. Organizers can plug in important words such as “follow up” or “action item” and the AI can recognize them and react for easier sharing and review after a meeting.
In addition, AI can help to record deadlines and, if programmed to do so, could send out reminders as deadlines approach. With something like Natural Language Processing (NLP) embedded, AI can also know which parts of the meeting are most important, based on vocal tones, and can automatically record and share those parts with attendees, ensuring that none of the actions are forgotten.
- Automated capture of nonverbal cues
We all know those golden moments during a meeting where ideas are born and everyone reacts in a positive way – but they can be hard to identify, particularly if you’re engaging with remote workers on the phone or via video conference.
Wouldn’t it be great if AI was able to more easily recognize and record those moments, because they are generally identified by nonverbal cues such as facial expressions, nods, laughter, or peaks in the audio when everyone has that aha moment? A human note-taker may not be able to accurately capture this, but AI may be able to.
- Improved overall efficiency prevents meetings from dragging on
Everyone has experienced a meeting that seems to drag on endlessly, or watched co-workers talk in circles. This can happen when people are not paying attention because they’re scribbling on notepads and typing on laptops, bringing up topics that were already discussed. This is what turns meetings into chores instead of the energizing moments of team collaboration they are meant to be.
When AI removes the more mundane aspects of a meeting like scheduling or taking attendance, attendees can move through administrative tasks and housekeeping items rapidly, knowing the AI will have it all recorded for later reference, and move into free-flowing exchanges of ideas.
And for those routine meetings that occur frequently and don’t always entail a major brainstorming, AI also facilitates effective and concise meetings, so everyone can get into the meeting quickly, be productive with the time set out, and then get back into more inspiring work.
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