Big data is all the buzz. Startups and seasoned companies alike are all exploring a new approach to problem-solving through the use of “big data.”

But what is big data? And how can you take advantage of the increasing demand for big data science and technology?

Data is information. Big data is more information. The key distinctions between data and big data are volume, velocity, and variety. Generally, big data is more extensive information with more individual components that are collected in a shorter period of time. Big data sources are often new but can encompass older data streams as well.

Nowadays, we create more data than ever before. Within this data are valuable insights that we can use to improve our various systems and processes. Data Scientists, Analysts, and Engineers collect and analyze data to find valid and useful conclusions.

Below, we’ll take a closer look at big data, the technologies behind it, the challenges of using it, and more.

Big data examples

As we said, big data holds valuable insights. Many of these insights help companies better serve their customers — and generate more money.

Because of this, big data is often used in marketing. Many of our online behaviors are tracked, from our activity on social media to our shopping habits. Marketers use this data for targeted advertising, promoting products and services that align with your interests.

Big data is also used in the healthcare industry. Think of all the wearable devices we have today, from Apple Watches to Fitbits. These devices can track your heart rate, breathing, sleep habits, and more — and they can even alert you to any concerning changes. Plus, physicians can use the data from these devices to create more comprehensive health profiles and better treat their patients.

We can also find examples of big data in the transportation and automotive industries. Self-driving cars and trucks use data on weather and road conditions, vehicle and pedestrian information, and more to increase safety and efficiency.

As you can see, big data holds great potential for improving our society. But, before we can make use of big data, it needs to be processed.

Big data processing

Because big data is so vast and comprehensive, it needs to be processed before being analyzed for insights. This involves collecting and comparing data from multiple sources, cleaning it to remove any errors or duplicates, and more.

After big data has been processed, data science professionals go through it to find any relevant patterns. This often involves the use of machine learning algorithms. Then, data visualization methods are used to make these insights easy to understand. Statistics also play a key role in data analysis as it helps us understand the relationships between data and probable outcomes.

Big data programming languages

There are several programming languages behind the tools Data Scientists use to collect, process, analyze, and visualize big data. Each language has its own advantages. Some of the most popular languages used for big data include:

Python

Python is easy to learn, and it’s one of the most popular languages used for data science. As a result, there are many Python libraries designed for data processing, analysis, and visualization. These libraries make it much easier to work with big data.

Python can also be used for statistical analysis, and it’s also commonly used in machine learning — two critical components of data science.

Java

Java also has utility for big data. Some of the most popular big data tools are written in Java. They’re open-source, flexible, and free to use — making Java a key language for anyone aspiring to work with big data.

JavaScript

JavaScript is one of the core programming languages for web development. It’s what makes websites interactive and dynamic rather than static.

The advantages of JavaScript make it useful for presenting and visualizing data online. JavaScript is often used for sharing big data and making it easier to understand.

C/C++

C and C++ are incredibly useful programming languages. While C was invented in the early 1970s and C++ in the mid-1980s, C and C++ programmers are still in strong demand today — and for a good reason.

When it comes to speed, C++ is often the best choice. Processing massive amounts of data quickly is one of the key advantages of C programming languages. When information is needed quickly in certain use cases for big data, C++ may be the best option.

R

With big data, statistical analysis is an integral part of drawing valid and useful conclusions. R excels at statistical analysis and visualization. When complicated statistics need to be applied, R is often the programming language of choice for data analysis.

SQL

SQL is used for accessing information stored in databases. The language was developed for handling large databases with relationships between different variables from different datasets. SQL is often the language utilized to easily access large amounts of stored data.

Challenges for big data

There are many challenges for big data. Incoming data that needs to be analyzed can be structured, unstructured, or somewhere in between.

Structured data is clearly defined, like a birthday or the number of widgets sold per day. As a result, it’s much easier to process and interpret.

Unstructured data is not well defined and needs further interpretation to be useful. The text of an email or a tweet is typically a good example of unstructured data.

Part of the challenge of big data is just making sense of the massive amount of information available. Algorithms for understanding the general meaning of text are often a key part of gleaning insights from big data.

The privacy and security of big data are also major challenges. It often seems like we hear about the theft of personal information from thousands of people every week. Big data demands new tools and techniques to keep information secure. Losing control of information can damage a company’s reputation and lead to various legal and financial consequences.

Data storage and processing are also huge challenges with big data. With large quantities of data that change rapidly, quick access and interpretation are required. Cloud storage is often utilized but can present challenges with speed, costs, and accessibility.

Learn more about big data

Opportunities in big data abound, and the demand for data science professionals is likely to grow as the online world continues to produce more information.

If you’re interested in working with big data, the first step is learning how to use some of the programming languages listed above. Use any of the courses below to get started:

Or, if you’d rather learn everything all at once, check out our Data Scientist or Data Analyst Career Paths. These courses will teach you how to use the programming languages above to collect, manipulate, and analyze data. You’ll also put your skills to use by creating projects that you can feature in a portfolio to impress future employers and clients.

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Pair programming is an agile software development technique in which two programmers work together. The goal of agile software development is to deliver software on an ongoing basis so the results can be evaluated more easily.

But how does having two programmers working together improve software development? Read on to learn more about pair programming, how it works, and its advantages and drawbacks.

Some of the very first programmers were the women who worked on the ENIAC (Electronic Numerical Integrator and Computer). These women worked in pairs, allowing them to discuss ideas and improve each other’s work.

Pair programming as a concept didn’t develop until the late 1990s, when agile software development practices were spread to a wider audience.

How does pair programming work?

In pair programming, one person is the “driver,” and the other is the “navigator.” The driver is the person at the keyboard who’s actively writing code. The navigator observes, checks code for accuracy, and keeps an eye on the bigger picture.

Pair programmers switch roles regularly, so both pairs stay engaged. They also work collaboratively, determining which tasks need to be done.

Why use pair programming?

Pair programming is a great educational tool for beginners. Observing other programmers and writing code in front of them are great ways to improve your problem-solving skills.

It’s also a good introduction for programmers who are onboarding with a new company. Learning about ongoing projects by working on them with an experienced and knowledgeable team member can help you stay up-to-date with programming practices. It also keeps individuals from becoming isolated from the rest of the team or project.

Christine Y., one of our Content Contributors, sheds more light on pair programming and how it’s beneficial:

“I personally love pair-programming because I prefer being on the exact same page as someone I’m working with. It creates less room for confusion/error, and I just think solutions are better as 2 minds > 1 mind.”

Pair programming requires communication to work. It improves the code that’s produced, and it’s less likely to contain errors since it’s reviewed by both pairs as it’s written.

Organizations that want to improve the quality of their code, integrate new members into their team more easily, and have development be more collaborative, benefit from using pair programming.

Advantages of pair programming

Here are some of the reasons why organizations use pair programming:

Knowledge sharing

While everyone on a project strives to be knowledgeable, there may be one or two programmers who hold the most knowledge of a project’s codebase. These people are sometimes called “towers of knowledge,” and teams may rely on them heavily. This becomes an issue if progress comes to a halt when those people are unable to work due to sickness or other causes.

Pair programming can ensure that one or two people aren’t holding all the knowledge critical to a project. Pairing them with a variety of programmers helps spread knowledge and keeps them from burning out.

Thinking

Writing code is only a small piece of what programmers do. Most of their time is spent solving problems, so having another programmer to bounce ideas off of and brainstorm with is invaluable.

You can come up with more creative solutions than you would on your own, and you might find solutions faster. Just having someone to talk through a problem with can be key to coming up with a solution.

Focus

It’s easy for your mind to wander when you’re working on your own. Having another person with you and changing roles often can help keep you fresh and focused.

Accuracy

Having someone quality checking the code as it’s being written will improve accuracy. Issues and bugs can be caught immediately rather than down the road when it’s harder to debug. This system makes it easier to improve code as well.

Challenges of pair programming

While there are many advantages to pair programming, it has its flaws, such as:

More complexity

Tasks often done by only one person tend to be simpler than those assigned to two people. For these complicated tasks assigned to a pair, an approach should be created and agreed upon.

Forming the best approach to a problem adds an extra layer of complexity and requires an extensive amount of time and thought. Ideally, programmers will have enough time to work on solo and pair-programming tasks.

Fatigue

When you’re working on your own, you can take breaks and work at your own pace. That’s more difficult when you’re working in a pair, so it’s important to discuss break schedules with your pair programming partner.

Managing skill levels

If there’s a significant gap in skill level between the two partners, one might end up relying too heavily on the other. This could lead to issues with workflow and between programmers.

It’s important for both partners to be open-minded and give the other person a chance to write code, make mistakes, and correct themselves. For example, if the driver makes an error, give them several seconds to correct it before pointing it out.

No alone time

Some tasks need to be done on your own, like email or returning phone calls. Expecting a pair to work together all the time is unrealistic and could lead to burnout. Instead, make it a consistent part of a schedule that includes time to work alone.

Getting started with pair programming

Many organizations use pair programming for improved efficiency. Even if you’re still learning to code, pair programming is a good way to improve your skills. For example, if you’re new to Python, you might want to practice with another student learning Python (or someone more experienced).

If you’re taking one of our classes, consider connecting with other students in our forums or on Discord. You can virtually pair program, and while it has a few more hurdles than in-person pair programming, it can still be beneficial.

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As a beginning Web Developer, you’ll hear many terms thrown around, and you might be unsure which are important in your study. One of those terms you may have heard is DOM, which is an important concept for Web Developers to learn. In fact, it may be the most important.

In this article and the video below, we’ll look at what the DOM is, what it looks like, how it’s used, and why you should care.

What is it?

DOM is an acronym that stands for Document Object Model. It’s how a web browser represents a web page internally.

The DOM determines what content should be on a page and how each element of the content relates to the other elements. Let’s look at each word of the acronym.

Document

We can think of a document as a way to structure information, including articles, books, and scientific papers. For Web Developers, a document is a name for a web page, and they consider the DOM as a model for all the stuff on the web page. The DOM calls this stuff objects.

Object

The “stuff” on web pages are objects and are sometimes called elements or nodes. Here are some objects you may run into on a web page:

• Content. The most obvious objects on a web page are the content. These can include words, videos, images, and more.
• Structural elements. These include divs, containers, and sections. You may not see these elements, but you see how they affect the visible elements because they organize those elements on the web page.
• Attributes. Every element on a web page has attributes. These include classes, styles, and sizes, for example. These are objects in the DOM, but they’re not elements like the content and structural elements.

Model

A model is a representation of something, and it helps us understand how something is put together. There are models for many things that need to be universally understood, analyzed, and used.

One example of a model being used is for instructions. Blueprints, floor plans, and IKEA directions are all examples of this kind of model. They show the object being modeled with enough detail that it can be recreated.

Another example of a model is a description. This type of model is used to simplify big ideas or complex systems so they can be understood more easily. These types of models help us to understand things like our galaxy.

The DOM is a model for web pages. It acts as a set of instructions for web browsers.

What does the DOM look like?

The DOM is represented as a type of data structure called a tree. Every object in the DOM is hierarchically under another object, and any object can have multiple children but only one parent.

Each DOM object “owns” its children. If you remove an object from the DOM, all of its children will also be removed with it.

The DOM itself is digital, so it actually doesn’t “look” like anything, but it can be represented in a few different ways. These representations can help us visualize what DOM is.

Tree graph

One way to represent the DOM is with a tree graph. A tree graph shows the relationship between parent and child objects, with lines between them representing their relationship.

Take a family tree as an example. At the top, you’d have your grandparents. Then, below, you’d have your parents and their siblings, followed by you, your siblings, and your cousins.

Similarly, the root node — the node at the top of the tree graph — of a web page would be the HTML element. Beneath, you’d have the head and body elements. Underneath the body element, you’ll possibly find header, main, and footer elements. Below the header element, you might find img, nav, and h1 elements.

HTML

The most common way to represent the DOM is with HTML. You can take a look at the HTML of a web page by opening the developer tools in your browser or by right-clicking on an element and choosing “Inspect element”. Here’s an example of HTML:

 ...  
  
Example Site
  
 
...
   

This list of elements may not look like a tree structure at first, but every indentation in the browser inspector or in the example above is like a vertical level in a tree graph.

HTML elements wrap other elements that are its children. The img element is a child of the header element, which is a child of the body element, which is a child of the HTML element.

Even though this is the most common way to represent the DOM, HTML isn’t the DOM itself — it just represents it.

Source code

There are more ways to create a document than using HTML. You can use a back-end programming language like PHP to generate HTML when a web page is loaded. You can use JavaScript to modify elements in the web page dynamically. Or, you can use a front-end framework like React to create complete web pages without writing any HTML.

Rendered web page

We can also view the DOM in the rendered web page when the browser converts it into something we can see in the browser.

In short, the DOM is a set of instructions on how to build a specific web page. The DOM tells a browser how to render the web page’s content, and we can edit the DOM via source code.

As a critical component of web development, you’ll need to understand the DOM if you’re looking to become a Front-End Engineer, Back-End Engineer, or Full-Stack Engineer. To learn more about building websites, check out our courses on web development.

A framework is a structure that you can build software on. It serves as a foundation, so you’re not starting entirely from scratch. Frameworks are typically associated with a specific programming language and are suited to different types of tasks.

Let’s say you’re building a house. You could pour the foundation and frame the house yourself. It would take a lot of time, but you could do it. If all of that were already done for you, though, it would save you quite a bit of effort — especially if it was done by expert home builders.

In software development, a framework serves a similar purpose. It’s designed and tested by other Software Developers and Engineers, so you know it’s a solid foundation.

A house isn’t complete with just the framework, though. Similarly, a framework in software development is a starting point, but you add higher-level functionality to it to make it work.

Below, we’ll take a closer look at frameworks, what they’re used for, the differences between libraries and frameworks, and more.

Why do we use frameworks?

Using frameworks saves time and reduces the risk of errors. You don’t need to write everything from the ground up, so there’s less chance of introducing errors. Plus, frameworks have already been tested, so there’s less to worry about. Other advantages include:

• More secure code
• Simpler testing and debugging
• Avoiding duplicate code
• Clean and easily adaptable code
• Able to focus on writing code specific to the project
• Can be extended

Types of frameworks

Frameworks can be used for developing websites, mobile applications, data science, and more. Here are some of the more popular frameworks:

Web application frameworks

AngularJS is a front-end JavaScript framework. It’s one of the most popular web frameworks and is backed by an enthusiastic community.

AngularJS was developed and is supported by Google. It includes features like two-way data binding, which reduces development time, and dependency injections, making it easier for different pieces of code to interact with each other.

Django is an open-source web development framework supported by the Django Software Foundation. It’s written in Python, a popular programming language, and is designed to encourage “rapid development and clean, pragmatic design.” It’s fast, secure, and scalable.

Rails is also an open-source framework. It’s written in the Ruby programming language, which was designed for ease of use. Rails is designed to involve less code and less repetition and is supported by a large community. It’s been used to develop sites like Airbnb, Twitch, and Hulu.

Express is a back-end framework for Node.js that allows you to start and configure a server with very little overhead. It’s free, open-source, and is written in JavaScript. Express has a reputation for being extremely flexible. It’s fairly minimalist, and developers have created middleware packages to address web development issues.

Mobile development frameworks

Flutter is Google’s open-source framework. It supports iOS and Android and has fully customizable widgets.

Flutter is designed to speed up app development and create attractive, user-friendly apps. It uses a thin layer of C/C++ code, but most of its system is in Dart.

Xamarin is a popular .NET-based framework by Microsoft. It also supports iOS and Android and has a community of 60,000 contributors.

React Native was developed by Facebook. It’s open-source, cross-platform, and written in JavaScript. It’s used in many popular apps, including Discord, Instagram, and Shopify.

NativeScript is an open-source, cross-platform framework. NativeScript apps are built using JavaScript, and it supports other JavaScript frameworks like Angular and Vue. It’s popular for its robust back-end support.

Ionic is another open-source, cross-platform framework. It uses JavaScript, HTML, and CSS, and it includes a library of mobile-optimized UI components, gestures, and tools. Ionic builds fast apps and integrates with front-end frameworks like Angular and Vue.

Libraries vs. frameworks

The terms “libraries” and “frameworks” are sometimes used interchangeably, but they’re very different.

In the video above, Carlos, a Web Developer, explains that a library is a set of previously-written code that you can use to build your own code. A framework is a supporting structure that requires specificity. You must follow the pattern of the framework.

A framework is essentially a skeleton, and you work with the skeleton. A framework calls on your code. Your code calls on a library. In other words, your code is in control when you use a library, but when you use a framework, it’s in control. This is referred to as “inversion of control.”

Frameworks can, and often do, include libraries. Libraries are used to fill out functions.

Getting started with frameworks

Frameworks are a valuable tool for developing applications. But, before you get started with frameworks, it’s best to have a solid understanding of a programming language.

For example, JavaScript is used in many frameworks. It’s a powerful, flexible programming language. We offer a beginner-friendly course to learn JavaScript. If you already understand the basics of JavaScript, our Learn Intermediate JavaScript course might be a better fit.

We also offer courses in popular frameworks. For example, if you understand JavaScript, you can use it to build a game in our Learn Game Development with Phaser.js course. Or, you can learn to create a website with the Handlebars.js framework in our Building Interactive JavaScript Websites course.

Ultimately, the better you understand a programming language, the easier it’ll be to understand frameworks. Programming languages are a solid foundation for many career paths in IT.

Which programming language you start with depends on your end goals and preferences. We offer programming courses in several languages, so you could even try a few to see which suits you best.

Once you understand frameworks, you can even give back by contributing to open source frameworks and participating in developer communities.Ready to get started? We’re here to help, whether you’re brushing up on skills, changing careers, or just starting out — so sign up today!

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