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Sunday, November 17, 2019

Which SQL Database Management System Should You Choose?

J-Wise Tech Insights Database , software

It’s easy to keep track of data using a basic spreadsheet, right? Up to a certain point, it is. However, the situations are very different when you’re working with information about ten items, and, say, a few thousand of them. That’s why no matter what kind of websites you create, sooner or later you will start looking into smarter data storage solutions. This is where database management systems come in.

It’s rather common to separate all database management systems into two groups, based on whether you can use SQL on them. In this tutorial, we will introduce you to the ones that support it. If by any case you need a reminder on SQL, be sure to take this interactive SQL course.

Getting the terminology down

Before we start exploring the world of database management systems, we should make sure we understand fully what is it we’re dealing with. First of all, we have data. Developers use this term to define all the information that is kept in the memory of the computer. For example, an e-commerce website would normally be storing data that relates to the user, such as their name, address, orders, and possibly banking information.

By entering their details on the webpage, the user sends their data to its server, which then stores it in a database. Without one, it would be hard to actually access and use the data: a database is what brings in the structure. It is a system in which you can store, update, manipulate, and access your data. To put it simply, each database consists of data and a method to access it conveniently.

Now, what is a database management system (DBMS for short)? It is a special kind of software that you use to create and manipulate your databases. You could say a database management system is an intermediary between the database and the user or the application that uses its data.

While there are various models, most agree relational database management systems are the most popular. The data they contain must be related in nature. The standard way of working with such databases is using SQL – the Structured Query Language. When asked to name a few relational database management system examples, most mention the most well-known ones: MySQL, Oracle, Microsoft SQL Server, and PostgreSQL. All of them were written in either C or C++.

MySQL: the industry titan

When thinking of SQL database management systems, most users instinctively think of MySQL. It’s only natural: not only the name itself contains ‘SQL’, but it’s also the most popular system worldwide. David Axmark and Michael “Monty” Widenius started working on it in 1994 and made the first version available to the public in 1995. Since then, MySQL has become an industry standard. The list of its current clients contains such famous names as NASA, Tesla, GitHub, Facebook, and a whole bunch of other giants. What is more, MySQL is simple and completely free to download and use. Developers praise its well-written and extensive documentation as well.

Since 2010, MySQL belongs to the Oracle Corporation. The acquisition caused a huge uproar, as the public feared Oracle will put MySQL to end. Michael “Monty” Widenius even started to work on a replacement database management system, creating a new branch of the MySQL of the time and calling it MariaDB. However, despite the worries, Oracle kept the free and open-source MySQL. It stayed on the top easily due to unique features that were never introduced in MariaDB. A large company owning and supporting the system made it even more reliable too.

The most current version is MySQL 8.0, published in 2018. The team releases small updates every two or three months. The newest version sports updated security, account, resource and table encryption management, as well as a transactional data dictionary. Most companies value MySQL for being very scalable and robust enough to handle huge amounts of data. Additionally, it works well across many different platforms, including but not limited to Microsoft Windows, macOS, and Linux.

Oracle: one step forward

Before Oracle Corporation acquired MySQL, it also had its own relational database management system, called Oracle Database, or simply Oracle. Initially released in 1979, it is now at version 19c, presented in 2019. The letter C in the name stands for the cloud.

When comparing MySQL vs. Oracle, you will notice the latter has a lot more to offer. It supports XML format, has more data types, more storage features, enhanced security and audit vault. And that’s not all! In 2018, Larry Ellison presented the version 18c as the first completely autonomous database management system. Armed with powerful machine learning capabilities, the new Oracle can perform a lot of tasks without human intervention, which lowers the risk of errors. The company presents the newest version as self-driving, self-securing, and self-repairing. It’s hard to disagree that it’s a huge step toward maximum security and ease of use.

However, the impressive features come at a price. The biggest difference between MySQL vs. Oracle is that while MySQL is free and open-source, Oracle is a commercial product. There are a few payment options available, depending on your exact needs. There is also a free version called Oracle Database Express Edition, but it has certain limitations: you cannot have more than twelve gigabytes of user data, two gigabytes of RAM, and 3 three pluggable databases.

Microsoft SQL Server: flexibility for a price

Another popular relational database management system is called Microsoft SQL Server. The team initially released it in 1989 and introduced some major updates with version 7.0 in 1998. By now, there are two latest versions: Azure SQL database 12.0 (2014) and SQL Server 2017. Confused? Let us explain.

To accommodate users with various needs and preferences, SQL Server offers a few different editions. Apart from the mainstream editions (Standard, Enterprise, Web, Express, and a few others), there are specialized ones. Azure might be one of the most popular. What separates it from simpler types is that it is fully cloud-based. The Developer edition is basically the same as Enterprise – however, it cannot be used as a production server. The list of editions goes on and on – what’s important to note is that Developer and Express are the only two versions that can be used free of charge. To take full advantage of Microsoft SQL Server, you will need to pay – and the costs aren’t low, either.

SQL Server is simple to use, plus, it detects and downloads any updates available automatically. After the initial installation, you can add extra components as well. You can use this database management system in one of the multiple languages, on multiple platforms. Additionally, SQL Server has comprehensive documentation and handy community features, such as forums and even a tech support blog.

PostgreSQL: the late bloomer

The PostgreSQL database management system might seem like the youngest of them all: the team at the University of California at Berkeley only formally published it in 1997. However, they did take the first steps in its development as early as 1982.

At first, there was the Ingres project. However, soon after it was done, the team noticed a few clear issues in using relational database management systems. Therefore, three years later, Michael Stonebraker, the leader of the team, started developing what was then known as Postgres (Post Ingres). His project underwent many updates and improvements: in 1994, it finally started to support SQL, and hence was renamed as PostgreSQL in 1996. In January next year, it was finally released to the public. As of now, the latest version is PostgreSQL 12.0, which went public in the fall of 2019.

Compared to other DBMSs, PostgreSQL has the most features, plus, it’s reliable and pretty quick. While it is the default choice for databases on the macOS Server, you can use it on all major operating systems. PostgreSQL is also pretty unique in terms of extensibility: you are free to create your own functions and data types. It supports a ton of programming languages, including C, C++, Java, JavaScript, Python, and others.

Choosing the right database management system for you

As you can see, each relational database management system has its own pros and cons. Choosing the right one depends on your priorities: do you need it to be free, or would you rather pay and get more features? Do you prefer cloud solutions, or do you want to try an autonomous system? The choice is yours. All you need to know is SQL – and this BitDegree’s online course is a great way to start!

What is Deep Learning: Neural Network Software Basics

J-Wise Tech Insights datascience , software

The evolution of technology has taken humanity to heights like never before. Work areas of medicine, safety, learning, and providing other kinds of help has reached a peak. But it does not stop there. Artificial intelligence is the next big thing in the world of technology and computer science but to understand it, it’s important to know what it consists of. It is essential to know what is deep learning and what artificial neural network means.

The AI technology field is extremely advanced and interesting. These two tools that are being used in artificial intelligence are very powerful in terms of solving complex problems and to develop even higher standards in science.

It is safe to say that this kind of mechanism is a transition to the next level of technology. The companies of today have already recognized its importance and started using it in most of their cases. Let’s take Google for example. Google uses search engine AI to learn from its’ users. If you are looking for something in its search bar, for example, a “laptop computer”, and after getting the results you press on it, you just taught Googles’ AI that a “laptop computer” is what you pressed on. Wonder how does it work? Let’s dive deeper and find out.

standing Deep Learning AI

What is Deep Learning technology so special about, that it is a technique for computers (AI) to learn just like humans do – by trial and error. If you are wondering if you have ever seen it before, you probably have. It is the technology behind such applications as voice control over devices like phones, tablets or television. Not so long ago we have been introduced to the driverless cars, which is also a product of deep learning. With the help of DL, artificial intelligence recognizes stop signs, pedestrians, and other obstacles in the road that might cause a disaster.

To perform such actions, a computer that is using deep learning techniques requests a large amount of training data (this is the work of neural networks, we will get to that a bit later). Such technological achievements like driverless cars need thousands of video footage and images to recognize every single situation for it to be safe. The recent improvements in Deep Learning have been taken to the level where it outperforms humans in a certain amount of tasks.

How Does It Work?

As already mentioned slightly above, what is deep learning using to perform such tasks are neural networks. Most of the times deep learning AI is referred to as a deep neural network. The word deep in this term stands for the layers that are hidden in the neural network.

Deep learning models are trained by getting a sufficient amount of data and neural network data architectures that learn features directly from the data without manual labour. Neural networks are systems that are connected just like our biological neural networks. These kinds of systems are created in a way to adapt to situational needs. Once the neural nets identify the results for a certain object, the next time the NN systems can identify whether it is the same object or not. The neural networks do not recognize objects the same way we do, it recognizes objects through their own unique set of features.

Artificial Neural Networks

One of the most common and popular types of what is deep learning using is known as conventional neural networks or CNN for short. It combines the learned features with input data, and uses 2D convolutional layers, making this architecture well suited to process 2D data. For example, it can be images or coordinate plane sheets.

Conventional neural networks work in a way that there is no longer a need for manual feature extraction. It extracts features directly from images. Artificial neural networks have an automated feature extraction that makes deep learning models picture-perfect accurate for computer vision tasks such as object classification.

CNN’s learn to detect different features using numbers of hidden layers. Every number of the hidden layer increases the complexity of the learned image features. CNN’s learn different features from every layer.

The Common Examples

According to sources, there are three most used ways to use deep learning to perform object classification:

Transfer learning. The learning approach is mostly used in deep learning applications. It is done by having an existing network and adding new data to previously unknown classes. This way it is a lot better to save some time because instead of you reduce the amount of image processing. It allows categorizing only certain objects rather than going through all different objects until it finds the correct one.
Training from nothing. This is mostly used for new applications that are going to have a large count of output categories. It begins by gathering a large number of labelled data sets and designing a network architecture that will learn the features. While transfer learning can take up to hours or minutes, this method takes a bit longer – from days to weeks to train.
Feature extraction. Not as popular as the mentioned methods before, but still used commonly. This is a method that is used for a more specialized approach to deep learning. It uses the network as a feature extractor. Since the layers in conventional neural networks are tasked with learning certain features from images, it is also possible to withdraw these features and make it as an input to a machine learning model.

What Are Other Types of Neural Networks?

While the conventional neural network could be considered as the standard neural net that has been expanded across space using shared weights, there are also some different types.

A recurrent neural network, rather than the conventional one, is extended across time by having edges that feed into the next time step instead of the next layer in the same time step. This artificial neural network is used to recognize sequences, for example, a speech signal or a text.

Also, there is a recursive neural network. This NN system has no time aspect to the input sequence, but the input has to be processed hierarchically.

Neural Networks in Action

It might get tricky when trying to understand what are the real benefits of the neural networks in real-life situations. Artificial neural networks are very popular among stock market experts. With the help of NN systems, it is possible to apply “algorithmic trading”, that can be applied to the likes of financial markets, stocks, interest rates, and various currencies. Neural network algorithms can find undervalued stocks, improve existing stock models, and use deep learning to find ways how to optimize the algorithm as the market changes.

Since neural networks are very flexible, they can be applied in various complex pattern recognitions and predict problems. As an alternative to the example above, the NN system can be used to forecast business, detect cancer from images, and recognize faces on social media images.

Deep Learning in Action

Not only neural networks have real-life examples. Deep Learning can also be described as some of the following creations:

Virtual assistants.
Chatbots or service bots.
Personalized shopping and entertainment.
Imagine colourization (uses algorithms to recreate true colours on images that are black-and-white)

What Are The Key Differences Between DL and NN?

With all this information it is clear that Deep Learning and Neural Networks are strongly connected and probably wouldn’t work well when separated. To be able to understand what is deep learning and what is neural networks it is essential to know the main takeaway.

Neural networks transmit data in the form of input values and output values. It is used to transfer data by using connections. Whereas Deep Learning is related to the transformation and extraction of feature which attempts to establish a relationship between stimulus and associated neural responses present in the brain. In other words, Neural Networks are used for natural resource management, process control, vehicle control, decision making, while Deep Learning is used for automatic speech recognition, image recognition, etc.

Overview

To sum up, Deep Learning and Neural Network complete each other and will develop into even bigger technological wonder than it is today. Head over to our courses page and take a course on Machine Learning applications. Artificial intelligence is the next step in our age, and the more experience it gets, the more benefits it will provide to society.

What is IOT: Understanding What is the Internet of Things

J-Wise Tech Insights datascience , IoT

Ever since the beginning of the internet visionary scientists imagined adding some sort of intelligence to basic objects to make every person’s life a little bit easier. The idea began in the early 1980s but it was slow to realize due to lack of technological advances.

To gain an understanding of what is IoT we must go back to the past century. In 1999 at MIT, the internet of things term was first mentioned as an idea to bring attention to Procter & Gamble’s (P&G) senior management. In addition to that, a book called “When Things Start to Think” appeared in the same years and drawn the way there the process of IoT should be heading.

IoT applications have evolved from things like wireless technologies, microservices, microelectromechanical systems and, of course, the internet itself. The convergence of these technologies has helped to break down the operational and information technologies, that enabled devices to receive data and make insights accordingly.

What is IoT?

From aeroplanes to self-driving cars and many other smaller devices, the internet of things is a reference to numbers of devices around the world that are connected to the internet. Collecting and sharing data, it enables the devices to work on its’ own and adds a “digital intelligence” to them. The electronic devices can communicate in real-time without any person involved and send generated messages.

To paint a better view of what is IoT, it helps to imagine that it is an ecosystem of devices, machines, appliances or any other things that can be connected to the internet through the wired or wireless network. The established network between devices allows data integration and exchange between the computer system and physical devices. This new chapter of technological advances is meant to be the step towards even better efficiency and productivity of daily people’s life.

Using the latest cutting-edge technologies like machine learning, artificial intelligence, and machine-to-machine communication, the internet of things extends the capabilities of typical physical devices like smartphones, tablets, desktop computers, and laptops. By expanding the connectivity of the devices that mostly are non-connected to the network, the IoT system bridges everyday devices like washing machines, heating systems, door or garage lock so you can monitor, control and provide other actions on a mobile phone or a tablet.

How Does IoT Work?

Just like any other system or mechanism works, the internet of things also has the main steps that are required for it to work. Trying to understand what is IoT and how it works may get confusing, so it is important to take it piece by piece. An IoT system is made of four main components that work together and create the desired output of the scheme. The components are:

Sensors

One of the main parts of the IoT engine, since it collects and specifies the data from the surroundings. The data that it collects can be considered as a very simple one – it may be a particular timing, geographical location, supply stock, or even such complex thing as the health state of the patient in a hospital. To notice even the smallest changes in surrounding data, the device can have a bundle of sensors that can be capable of more than data collection. The best example of that could be a mobile phone, that provides a lot of other functions while perfectly managing data.

Connectivity

After collecting the surrounding data IoT devices need to process it somewhere and this is when connectivity plays the main role. Collected data is sent to the IoT platform with the help of a medium. Wi-Fi, Ethernet, Bluetooth, Cellular Network and other network connections are crucial in transferring data to the cloud. Connectivity is what mostly defines what is IoT. Choosing the best connectivity method will decide the tradeoff of medium and power consumption, range of connectivity and bandwidth.

Data Processing

As mentioned slightly above, connectivity enables the transfer of data into a cloud where it is stored, analyzed, and processed. The data is processed using a “Big Data Analytics Engine” that helps the system to make better decisions according to the data. Data processing based decisions allow IoT applications to make a wide range of actions. For starters, it can be as simple as turning on the lights when the homeowner comes back at particular hours, or, with the help of surveillance systems, identifies danger or intruders.

User Interface (UI)

The final step of the internet of things process is to notify the main user. It can be done through numerous actions, as an alert, reminder, text message, notification or email. These actions can depend on the functionality of the system itself. An advanced IoT system can arguably control the whole home environment and even more. The user interface allows the user to perform a wide variety of actions, for example, adjusting lighting, temperature, air conditioning in the environment, etc.

One of the best ways to describe what is IoT or how it works is through daily tasks that will be changed after implementing the system.

The refrigerator will notify the user when it is out-of-stock of particular groceries.
Kitchen appliances will start working in hours when you wake up or get ready for making dinner.
Lights turn off or on when it’s time to sleep or wake up.
The vehicle sends a notification on gas shortage or delays in the road.

As said before, the goal is to make the day easier, more efficient, productive, and overall better for the consumer. The devices will overtake most of the chores without human intervention.

IoT in Labor Market

It is safe to say that the internet of things can bring many benefits not only to the regular life of casual citizens but also for many business organizations and authority institutions. Enterprises have a lot more access, devices, and appliances, therefore it needs even better and accurate overwatch of devices.

Manufacturers realized what is IoT and what are the benefits of its capabilities so they started adding sensors to collect data on their devices. By adding sensors, companies can transmit data back and forth, and see how their products are performing. IoT applications and devices can be implemented in various industries, can offer industry-specific sensors or real-time location devices. By the year 2020, it is predicted that across the industries there will be the amount of 4.4 billion IoT units and it will hit $3 trillion revenue. Let’s take a look at the industries that already started applying cutting-edge technology.

Medicine and healthcare. The newest sensors enabled doctors to get real-time access to the patient’s health status, collect and store data about it in the cloud. The IoT technology in medicine has significantly decreased the time of typical procedures, simplified the processes while reducing and mitigating disease risks, and improved the use and availability of hardware.
Sharing Economy. With the combination of the internet of things and blockchain, the sharing economy has been reshaped into a “Blockchain of Things”. The combo of both technologies has created possibilities for many marketplaces to collect and share data due to the concept of Smart Contracts.
Education. What is IoT in education doing, is that with the share of access to data, many people across the world can approach information and formal or informal education.
Retail. Automating as much as possible processes of delivery systems is the key to quick and safe transmissions. Automation makes the process effortless.
Travel. The migration of the people through the world is one of the most frustrating procedures that require a lot of security attention and information processing. With the implementation of IoT in travelling, agencies are now able to deliver real-time information and automate most of the processes that make travelling a smooth and pleasing experience.

Career Possibilities in IoT Field

There is no question that the growing field of the internet of things will need experts that are willing to improve it. Named as the next “Industrial Revolution”, the IoT field can be a great place to start a new career or shift towards it. What are the demanded skills in such an industry?

Information Technology Infrastructure Library (ITIL). Considered to be one of the vital elements in what is IoT, ITIL is a great addition of stability, networking, and security in the technology.
The Open Group Architecture Framework (TOGAF). Compared to what HTML did to HTTP for web development, TOGAF is the element that will enable connecting everything instantly. Having a TOGAF certification can be one of the best ways to get into the internet of things field.
Big Data. As mostly everything on the digital space revolves around data, the IoT will generate even more data to store and process, therefore there will be, or is right now, a huge need of Big Data specialists to handle the amounts of data created by IoT.
Blockchain. Although the blockchain is not considered as a part of IoT, the secure nature of it can become crucial in the upcoming years.

Amazon Go example of what is IoT — Source: Amazon.com

An Example of Amazon Go

If you are still not sure what is IoT or whether it is a relevant thing in your surroundings, there are many daily examples of it where it is used. We can begin by one of the most known examples that people use in their households – Amazon Go. Being one of its kind, Amazon Go is an app that allows users to shop with no checkout required. By having a certain application on your phone, you walk into the store, collect the groceries you need and leave. No lines, no checkouts. The IoT system behind it collects data using deep learning algorithms, computer vision, and sensor fusion. Just like self-driving cars, Amazon Go is using the same technology.

Overview – What are the Predictions?

The internet of things technology did not come out of anywhere, it is the evolution of the internet throughout the years that lead to it. Otherwise known as WEB 3.0, the process has still a long way to go. It is predicted that in the upcoming years the definition of what is IoT will become even smarter. Many companies and authority institutions will presumably start using this technology to save money and time, therefore increasing the efficiency of people’s work.

Of course, with the great benefits that the internet of things will bring to our society, the growing cybersecurity danger will also increase. While managing information remotely and automatically, hackers will look for ways to get into the system and disrupt the security and privacy of users with cyber attacks. Many regulations are still yet to come and there is still a lot of work to improve the current technology, but their future of it looks brighter every day. If you want to learn the main skills that are needed in this field, head to BitDegree courses and become an expert in the IoT field.

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